# Beam Particle Identification and Tagging of Incompletely Stripped Heavy
Beams with HEIST

A.K. Anthony  C.Y. Niu  R.S. Wang  J. Wieske  K.W. Brown  Z. Chajecki  W.G.
Lynch  Y. Ayyad111IGFAE, Universidade de Santiago de Compostela, E-15782,
Santiago de Compostela, Spain. J. Barney  T. Baumann  D. Bazin  S. Beceiro-
Novo  J. Boza  J. Chen  K.J. Cook  M. Cortesi  T. Ginter  W. Mittig  A. Pype
M.K. Smith  C. Soto  C. Sumithrarachchi  J. Swaim  S. Sweany  F.C.E. Teh  M.B.
Tsang  C.Y. Tsang  N. Watwood  A.H. Wuosmaa  National Superconducting
Cyclotron Laboratory, Michigan State University. East Lansing, MI, 48824, USA
Department of Physics and Astronomy, Michigan State University. East Lansing,
MI, 48824, USA  Department of Chemistry, Michigan State University. East
Lansing, MI, 48824, USA  Department of Physics, Western Michigan University.
Kalamazoo, MI, 49008, USA  Department of Physics, University of Connecticut.
Storrs, CT, 06269, USA

###### Abstract

A challenge preventing successful inverse kinematics measurements with heavy
nuclei that are not fully stripped is identifying and tagging the beam
particles. For this purpose, the HEavy ISotope Tagger (HEIST) has been
developed. HEIST utilizes two micro-channel plate timing detectors to measure
time of flight, a multi-sampling ion chamber to measure energy loss, and a
high purity Ge detector to identify isomer decays and calibrate the system.
HEIST has successfully identified 198Pb and other nearby nuclei at energies
around 75 MeV/A. In the experiment discussed, a typical cut containing 89% of
all 198Pb+80 in the beam had a purity of 86%. The maximum purity of a cut is
limited by any charge states of the same element that overlap with the isotope
of interest. The observed charge state populations of these ions are presented
and are moderately well described by the charge state model GLOBAL.

###### keywords:

Rare Isotopic , Inverse-kinematics , Beam tagging , AT-TPC , MCP , MUSIC ,
Charge states

††journal: NIM A

##  1 Introduction

At accelerator facilities worldwide, projectile fragmentation is an important
mechanism for producing rare-isotope beams (RIBs) [1, 2, 3, 4]. Projectile
fragmentation produces a wide variety of isotopes, from stable to short-lived,
that can be subsequently filtered by fragment separators to select the desired
species. Unlike stable beam facilities, RIBs produced at fragmentation
facilities typically have lower intensities, larger emittances, and more than
one isotopic species, even after passing through a fragment separator.

For many experiments, it is necessary to identify the beam on an event-by-
event basis. Often, an appropriate choice of separator settings can remove
most unwanted ions in the beam. Subsequently measuring the time of flight
(ToF) from the fragment separator to the experimental setup allows the
remaining rare isotopes in the beam to be uniquely identified (i.e. tagged).
Such identification becomes more difficult when these secondary beams are
heavy and the beam particles have overlapping velocity distributions. In this
case, a ToF measurement is not sufficient to resolve the beam species. Beam
species can still be distinguished by an additional measurement of the energy
deposited in an ion chamber, $\Delta$E.

Heavy beams are routinely tagged by the $\Delta$E-ToF-B$\rho$ method at the
RIBF [4] and GSI [3] facilities. At those facilities, beams energies are
$E/A\geq 200$ MeV, and beam ions are often fully stripped. Slower beams, where
the ions are not fully stripped complicate the picture. At the energy regime
of interest, $E/A\approx$ 74 MeV, such heavy ions typically retain their 1s
electrons, making them “helium-like” nuclei with Q/e = Z-2, but with non-
negligible probabilities for them to be “lithium-like” ions with Q/e = Z-3 or
“hydrogen-like” with with Q/e = Z-1. The HEavy ISotope Tagger (HEIST) was
developed at the National Superconducting Cyclotron Laboratory (NSCL) to tag
and track secondary projectile fragments where ions are not fully stripped.

This leads to three questions: (1) how accurately can we tag these ions? (2)
how serious are the complications from multiple charge states in these ions?
and (3) how accurately do theoretical calculations predict the performance of
our beam identification setup and the production of different charge states?
Answers to these questions can facilitate future efforts to tag heavy nuclei.

##  2 HEIST Setup and Operation

HEIST was coupled to the A1900 fragment separator [5] at the NSCL and consists
of three distinct detector systems. The detector systems are: a time of flight
(ToF) system composed of two micro-channel plates (MCPs), a Multi-Sampling
Ionization Chamber (MUSIC) that provides beam tracking and energy loss
($\Delta$E) information, and a high-purity Ge (HPGe) crystal used to detect
isomer $\gamma$-rays. The physical locations of the detectors and the
experimental end station, here the Active-Target Time Projection Chamber (AT-
TPC) [6], are shown schematically in Fig. 1. The location of the first MCP
(MCP1) at the A1900 focal plane is shown in Fig. 2. Further details of each of
the detector systems can be found in Sec. 3.

Figure 1: Detector locations along the beamline. Not to scale.

###  2.1 A1900 Setup

At the Coupled Cyclotron Facility (CCF) of the NSCL [1, 5], a primary 208Pb
beam was accelerated first in the K500 Cyclotron, strip injected into the
K1200 Cyclotron where it was accelerated to its final energy of E/A = 85 MeV.
Rare isotopes were then produced by bombarding a 9Be production target, with
an areal density of 22.5 mg/cm2, located outside of the K1200 Cyclotron [5].
This produces a mixture of primary and secondary beams that enter the A1900
fragment separator [5], shown in Fig. 2.

Figure 2:  A1900 Fragment Separator: The locations of the production target,
Image 2 dispersive focus and the 1900 focal plane are shown.

Midway through the A1900 magnetic separator, there is a dispersive focus at
Image2 (I2) where the beam particles are focused to different points depending
on their rigidity. Using a set of thin narrow slits at I2 to define a 2 mm
aperture, we selected ions that have a magnetic rigidity of $B\rho$ = 3.271 Tm
with a precision of $\pm~{}0.035\%$. This rigidity transmits 192Hg ions with
Q/e = 77 at an energy of E/A = 79.7 MeV, as well as other ions with a similar
rigidity.

The isotopic selection was improved by passing the remaining ions through a
23.5 mg/cm2 Polyimide foil to degrade the energy. Ions with different atomic
numbers emerge from the foil with different rigidities [5]; slits at the
second dispersive focus at the A1900 focal plane removed unwanted ions with
magnetic rigidities that differ from the desired rare isotope [5]. This second
set of slits was set to select particles with rigidity of $B\rho$ = 3.117 Tm
$\pm~{}0.18\%$ corresponding to 192Hg ions with a charge of Q/e = 78 by
blocking undesired isotopes with larger or smaller magnetic rigidities.

The selected isotopes then pass through the first MCP (MCP1) that served as a
start detector for the ToF system and go on to the second MCP (MCP2), placed
32 m downstream of MCP1 that served as the stop detector, as shown in Fig. 1.
Using the two MCPs we measured the ToF and the velocity of the beam particles
before they passed into the MUSIC detector and the AT-TPC.

###  2.2 $\Delta$E-ToF-B$\rho$ Method

The momentum cut at the dispersive focus of the A1900 separator is related to
the A1900 rigidity setting, the beam momentum, beam charge and mass as
follows:

$B\rho=\frac{p}{q}\simeq\frac{mv}{q}$ (1)

Here $p$ is momentum, $q$ is charge, $m$ is mass, and $v$ is velocity. The
energy loss of a charged particle through a material, $\Delta$E, is related to
its velocity and atomic number by

$\Delta E\approx C\frac{Z^{2}}{v^{2}}f(v)~{},$ (2)

where $f(v)$ is a slowly varying function of the velocity $v$ [7].

Combining the energy loss in the ion chamber, $\Delta$E, with the velocity
determined by ToF provides uniquely the atomic number, Z, via Eq. 2. Combining
the velocity with the magnetic rigidity via Eq. 1 provides the ratio of mass
to charge. For reactions where only one charge state is present for each
isotope, this allows a unique determination of the mass and atomic numbers of
each particle. For slower heavy fragments where multiple charges are probable,
one can take guidance from calculations of the charge state distributions and
beam optics calculations that indicate where each charge state should appear
in the particle identification (PID) spectrum. In Sec. 5.3, we test these
charge state models via measurements of characteristic isomer $\gamma$-rays
that uniquely identify the beam fragment.

###  2.3 A1900 Simulation

To design HEIST, we modeled its performance with the LISE++ beam simulation
program [8]. The width of the I2 slit was optimized by considering the trade-
off between momentum resolution and beam rate. The rms time resolution $\delta
t$ of an MCP can be estimated by the relationship $\delta
t=\tau_{rise}V_{noise}/V_{signal}$ where $\tau_{rise}$ is the rise time of the
MCP anode signal, $V_{noise}$ is the rms noise and $V_{signal}$ is the
amplitude of the MCP anode signal. From this we estimated that we could
achieve a FWHM time of flight resolution of about 300 to 400 ps. Allowing for
different path lengths for different ions, we conservatively estimated a time
resolution of 500 ps could be achieved. LISE predicted the energy loss
straggling in the MUSIC with 300 torr CF4 to be somewhat less than 0.7%
(FMHW). Using the software package SRIM/TRIM [9], we calculated that an energy
loss resolution of 0.85 % (FWHM) could be achieved.

Figure 3: The LISE++ simulated PID with several isotope examples. The charge
states are helium-like.

The beam optics and detector response were simulated with LISE++ to determine
our predicted ability to successfully identify beam particles. Fig. 3 shows
the $\Delta$E vs ToF PID plot simulated with LISE++ assuming the A1900
configuration discussed in Sec. 2.1. For the simulation, we assumed a
conservative $\Delta$E resolutions of 2 % (FWHM), significantly larger than
the pure energy straggling calculations made with SRIM or LISE++, to include
fluctuations from secondary effects, such as high energy delta electrons that
escape the detector volume. With these assumptions, we predicted that the
various isotopes in the beam could be adequately resolved.

##  3 HEIST Construction and Operation

###  3.1 MCP

The timing detector was based on a thin emitting foil, 0.5 $\mu$m
polypropylene, placed along the path of the beam with an MCP readout. Removing
the MCPs themselves from the beam avoids unnecessary degradation of the beam
emmitence and they can operate at a rate exceeding 1 MHz without damage.

The design of both MCP timing detectors is similar to that described in Refs.
[10, 11]. Fig. 4 illustrates the design and operating principles of this MCP
timing detector. When a beam particle passes through the thin foil, electrons
are released by ionizing atoms which are accelerated away from the foil by a
static electric field. The trajectories of the electrons are bent by electric
and magnetic fields through an angle of $180^{\circ}$ so that they hit a stack
of 40 mm diameter MCP electron amplifiers produced by Photonis. The two MCPs
in the stack are arranged front to back in a chevron configuration to multiply
the primary ionization electrons by a gain factor of approximately $10^{6}$.
The amplified charges are collected onto an anode plate located behind the
MCP. The multiplication process in the MCP is fast, and the corresponding
signals have a rise time of about 3 ns.

Figure 4: (Color online) Schematic of the MCP detector, not to scale

A series of electrodes, shown by the dashed black lines in Fig. 4, are
connected with a resistor chain. When biased, they generate the electric field
that, in conjuncture with a permanent magnetic field, bends the electrons. The
highest potential electrode was biased to $V_{E-field}\approx 2$ kV. The
permanent magnetic field is generated by attaching high-field, permanent
neodymium magnets on two nickel-plated iron plates that sandwich the MCP
detectors. The locations of the permanent magnets were positioned so that the
magnetic field is approximately 100 G and uniform to within 2 % over the
electron trajectories. In addition, a -1 kV bias is applied to the foil and a
positive bias of $V_{mcp}\approx 2.5$ kV to applied the MCPs and anode.

Signals from the anode were amplified using a non-inverting 200 gain fast
amplifier (Ortec FTA820A). The signals were then discriminated using a
constant fraction discriminator (Canberra 454) and passed to a TDC (Caen
1190). The signals from MCP1 at the A1900 focal plane were discriminated near
the detector, and then sent to the S2 vault at the NSCL where MCP2, the MUSIC,
and the TDC to digitize the signals were located.

###  3.2 MUSIC

####  3.2.1 Mechanical Design

The MUSIC detector was constructed to measure the energy loss of the secondary
beam particles. Fig. 1 shows an overview indicating the horizontal dimensions
of the MUSIC chamber and the physical layout of the anode pads of the MUSIC
chamber. Fig. 5(a) shows a schematic side view of the MUSIC chamber indicating
the orientation of the anode electrodes, Frisch grid, and cathode. This MUSIC
detector is loosely based on the MUSIC80 design employed at GSI to measure the
energy loss of heavy beam particles [12].

(a) MUSIC schematic

(b) MUSIC picture

Figure 5: (Color online) The MUSIC used in this experiment. (5(a)) shows a
schematic of MUSIC detector, not to scale. (5(b)) is a photo of the MUSIC
during construction. The first two and last two segments are triangular pads
used for measuring beam position within the chamber via charge division.

Fig. 5(b) is a photograph of the inside of the MUSIC taken during construction
and the anode structure is visible. The MUSIC detector has an active area of
62 mm $\times$ 36 mm and active length of 333 mm. The anode and cathode are
both PCBs (333 mm $\times$ 128 mm), mounted onto aluminum plates that are held
parallel to each other and separated by 45 mm. The PCBs in this detector were
fabricated with a 1.6 mm thick halogen free substrate, IT-170RGA1 and their
1oz copper electrode surfaces were coated with Electroless Nickel Immersion
Gold (ENIG) plating. The cathode consists of a single conducting (332 mm
$\times$ 127 mm) pad.

The anode is split into 9 rectangular segments, with two additional anode
segments (the first and the last) that are subdivided into pairs of triangle
pads resulting in a total of 13 pads, as shown in Fig. 1 and 5(b). The
rectangular middle nine anode segments are 30 mm $\times$ 82 mm conducting
surfaces separated from each other by 1 mm insulating gaps. The triangular
pads (24 mm $\times$ 82 mm) provide information about the horizontal beam
positions near the entrance and exit of the chamber using the technique of
charge division. The beam tracking capabilities of the detector are discussed
in detail in Sec. 3.2.3.

Each of the thirteen anode pads were individually amplified via Zepto Systems
CSA-60-100 charge sensitive preamplifiers and a programmable spectroscopy
amplifier (CAEN n568). The slow output from the spectroscopy amplifier is
digitized by a peak sensing ADC (Mesytec MACD-32). The fast output from the
spectroscopy amplifier was discriminated using a leading edge discriminator
(Philips 7106) and digitized using a TDC (CAEN V1190). The typical signal to
noise ratio for the anode signal was 0.07 %.

The Frisch grid was fabricated using 76 $\mu$m diameter beryllium copper wires
and oriented parallel to, and 9 mm above, the anode. The pitch between
neighboring wires was chosen to be 1 mm and is discussed in detail in Sec.
3.2.2.

The electric field inside the drift region of the MUSIC detector is generated
by the electrode surfaces of the PCBs and of the exit and entrance windows.
The side wall PCBs have 0.2 mm wide conducting strips spaced 1mm apart and
connected through a resistor chain to the cathode and anode providing a
uniform electric field near the side walls that extends from the cathode to
the Frisch grid and between the Frisch grid and the anode. In addition, the
beam enters and exits the MUSIC field cage through 0.5 $\mu$m thick
polypropylene windows with an active areas of 30 mm $\times$ 30 mm. By
evaporating 1.6 mm wide Al strips, spaced 2.54 mm apart, and biasing them to
the same voltage as the side wall PCB we ensured a uniform electric field
throughout the MUSIC field cage. The locations of the first and last
rectangular anode sections were chosen so that the electrons that drift to
these pads only pass through a region where the drift field is uniform to
better than 1%.

The drift field is defined by the negative high voltage $V_{c}\geq-5~{}kV$ on
the cathode and the ground potential of the anode pads which are individually
grounded through 100 M$\Omega$ resistors. A simplified voltage divider circuit
and Frisch grid filter is shown in Fig. 5(a). An iseg VHQ 203M power supply
was used to bias the cathode. Table 1 contains the bias voltage, drift field,
and electron drift velocity for gas configurations used in this paper which
are discussed in detail in Sec. 3.2.4.

Table 1: Selected gas properties of CF4 at running conditions. | Cathode |  |  $e^{-}$ drift | Avg energy per
---|---|---|---|---
Gas (torr) | voltage (V) |  $E_{C}$ (V/cm) | velocity (cm/$\mu$s) | ion pair (eV)
CF4 150 | -1000 | 192 | 10.3 | 34.3
CF4 294.5 | -1000 | 192 | 8.3 | 34.3

This light-weight MUSIC assembly sits within a larger aluminum box that
provides additional shielding and serves as a pressure vessel. To minimize gas
contamination and recombination in the active region of the detector, gas
flows directly from the gas handling system into the active region through
numerous small holes in the anode PCB, seen in Fig. 5(b). This enforces a
laminar gas flow from the anode to the cathode and minimizes recombination.
Then the gas flows through small holes in the cathode to the outer pressure
holding vessel and finally back to the gas handling system.

####  3.2.2 Frisch Grid Considerations

Beam particles entering the gas volume of the MUSIC detector make a straight
line path ionizing the gas and creating a cylindrical distribution of
electrons and positive ions along the path of the beam particle. Since the
electron drift velocity greatly exceeds that of the ions, positive ions remain
in the gas long after the electrons are collected on the anode. Absent a
Frisch grid, this positive ion charge induces a negative image charge on the
anode that reduces the net anode signal by an amount that depends on the
distance of the track from the anode. With a Frisch grid in place, the image
charges appear on the Frisch grid instead, and the anode signal is independent
of the vertical position of the track within the chamber.

Following Bunemann [13], we define the grid “inefficiency”, $\gamma$, to be a
measure of how independent the field strength at the anode, $E_{A}$, is to the
field created by positive ions in the cathode region, $E_{Q}$:

$\gamma=\frac{dE_{A}}{dE_{Q}},$ (3)

where $\gamma$ is given by Bunemann’s expression:

$\gamma=\frac{d}{2\pi(x_{A}+r-\pi r^{2}/d)}\ln\frac{d}{2\pi r}.$ (4)

Here, $x_{A}=9$ mm is the distance between the Frisch grid and the anode PCB
boards, $d=1$ mm is the wire pitch, and $r=38~{}\mu$m is the radius of the
Frisch grid wires, shown in Fig 5(a). This provides a value of $\gamma$ =
0.026, effectively suppressing the dependence anode signals on the position of
the positive ion space charge.

If the anode were fully shielded, then no electrons would make it past the
grid to the anode. The shielding of the anode to the positive space charge
must be balanced against the need for most produced electrons to reach the
anode. The proportion of electrons collected on the anode is roughly equal to
the proportion of field lines that bypass the Fricsh grid, and terminate on
the anode. Bunemann calculated the condition for the relative field strengths
in the anode, $E_{A}$, and cathode, $E_{C}$, drift regions where all field
lines by-pass the Frish grid [13]:

$\frac{E_{A}}{E_{C}}\geq\frac{1+2\pi r/d}{1-2\pi r/d}.$ (5)

For our chosen values of $d$ and $r$, the field in the anode region must be
1.6 times larger than the field in the cathode region to satisfy this
condition. We constructed a voltage divider for the field cage accordingly to
achieve $E_{A}/E_{C}~{}=~{}1.8$, satisfying the condition given by Eq. 5, and
minimizing the loss of signal due to electrons terminating on the Frisch grid
instead of the anode.

####  3.2.3 Position sensitivity of the MUSIC

The four triangular anode pads allow the horizontal “x”-position of the track
to be determined at the entrance and exit of the MUSIC detector. Each track
deposits charge on the pad surfaces that are directly above it. This makes the
deposited charge in the triangular pad proportional to the width of the pad at
the x-position where the charge is deposited. Defining beam right to be
positive x direction and the left pad to be the one that is wider on the beam-
left side of the anode plane, one can divide the difference in the charges in
the right and left pads by the sum of both charges to get the x-position as
follows:

$x=x_{max}\frac{Q_{R}-Q_{L}}{Q_{R}+Q_{L}},$ (6)

where $Q_{L}$ and $Q_{R}$ is the charge deposited on the left and right
triangular pads, respectively, and $x_{max}$ = 41 mm is half the width of the
anode plane.

The y-position of the track can also be determined from the time difference
between the signal in MCP2 located 40 cm upstream, which records the arrival
time of the beam particle and the anode signal which starts when the ionized
electrons pass through Frisch grid. If the drift velocity of electrons in the
detection gas is known, then the delay time for the anode signal is a linear
function of the drift distance from the track to the anode plane. By combining
the x and y positions determined by these anode signals,the location and
direction of beam tracks can be reconstructed in 3D space and extrapolated to
different points along the beamline.

####  3.2.4 Gas selection and properties

CF4 was selected as the detection gas. It has the virtues that it is not
explosive, has a fast drift velocity, which minimizes pile-up effects, and a
large stopping power. Table 1 provides relevant counter gas properties for
CF4, including the electron drift field strengths, drift velocities calculated
using MagBoltz [14], and average energy required to produce an ion/electron
pair [15].

###  3.3 HPGe

For calibration and characterization of the HEIST system, individual isotopes
in the beam were uniquely identified by stopping them in an aluminum block
after they pass through the MUSIC detector and detecting $\gamma$-rays fed by
the decay of long-lived isomers in these isotopes. These $\gamma$-rays were
detected in an OR120 Ortec High Purity Germanium crystal (HPGe), which is a
liquid-nitrogen cooled GEM series HPGe coaxial detector system. The Ge crystal
in this model of detector has dimensions 80.8 mm $\times$ 105.8 mm, with a 4
mm gap between the end cap of the housing and the crystal. A 1 mm thick
Aluminum layer and 0.7 mm of inactive Germanium served as absorbing layers.
Despite the attenuation of some $\gamma$-rays in the absorbing layers, we were
able to match the observed coincident $\gamma$-rays to known isomers fed
$\gamma$-rays and thereby uniquely identify many of the isotopes in the beam,
as discussed in Sec. 5.1. At typical gamma-ray energies observed in this
experiment (300 - 1000 keV), a resolution of 1.1 keV (FWHM) was achieved.

##  4 Commissioning HEIST

A number of stable primary 208Pb beams were used to commission HEIST, ensuring
proper functionality and calibration of the various detector systems described
above. The properties of these beams are detailed in Table 2.

Table 2: Selected properties of stable 208Pb beams used to test detectors. Beam type |  $B\rho$ (Tm) | Charge states
---|---|---
Pilot beam | 4.4785 | 63+
Calibration beam | 2.5782 | 79+, 80+, 81+, 82+

###  4.1 ToF Calibration

The absolute time of flight between the two MCP detectors was determined using
a 208Pb+63 primary beam from the CCF. The resulting ToF spectrum for this beam
is shown in Fig. 6(a). The beam’s rigidity, and therefore velocity, is set by
the A1900. Using LISE++, the physical ToF for the primary beam can be
calculated for the flight path along the beamline. By fitting the ToF spectra,
we corrected for unknown cable delays so that the absolute flight times were
obtained for all beams.

(a) Undegraded 208Pb63+ beam.

(b) Multiple charge states of 208Pb.

Figure 6: ToF spectra for two primary beam settings. Shown in (6(a)) is the
ToF spectra for an undegraded 208Pb63+ beam with B$\rho=4.4785$ Tm. Shown in
(6(b)) is the ToF spectra for multiple charge states of the 208Pb beam with
B$\rho=2.5782$ Tm.

To provide a further check, the primary beam was degraded prior to the A1900
and transmitted with the magnetic rigidity set to 2.5782 Tm. As the primary
beam passes through the degrader, it will exchange electrons with the target
producing multiple charge states which after the B$\rho$ selection of the
A1900 will have different velocities. We observe the degraded primary beam in
four charge states: Q/e=Z, Z-1, Z-2 and Z-3, where Z=82. The ToF values shown
in Fig. 6(b) for these four charged states agree with the expected values to
within 0.2 ns.

##  5  Rare Isotope Beam Measurements

###  5.1 Beam Identification

Fig. 7 shows the correlation between the secondary beam ToF (x-axis) and the
sum of the energy losses measured by the nine rectangular anode electrodes in
the MUSIC for the secondary beam tuned to $B\rho$ = 3.1173 Tm. Multiple peaks
appear in this two dimensional spectrum. Each represents a different isotope,
which is marked on the figure. Since this PID plot is unchanged by stopping
the beam ions in an aluminum block approximately 30 cm downstream, we can
identify these ions by measuring the $\gamma$-rays emitted after an isomeric
state decay with the HPGe detector.

Figure 7: The PID plot with some labeled isotopes.

Fig. 8 illustrates the identification of secondary beam isotopes. Gates on
each of the PID peaks and coincident $\gamma$-ray spectra were obtained for
each peak. The black contour in Fig. 8(a) surrounds the 198Pb PID peak with
Q/e=80. The middle panel of Fig. 8(b) shows the 198Pb $\gamma$-ray peaks that
were observed for ions detected within the black contour. For isomer decays to
feed these $\gamma$-ray transitions, 198Pb isomer states must have long enough
lifetimes so that some isomers survive the 590 ns flight from the production
target.

(a) PID with gate on 198Pb80+.

(b) Gamma spectra for various nuclei.

Figure 8: Shown in (8(a)) is the secondary beam PID spectra illustrating a
gate on 198Pb. Shown in (6(b)) is the typical gamma spectra for some of the
more intense species in the RIB. Obtained by first placing cuts in $\Delta$E
and ToF on the PID. The labeled gamma rays correspond to isomer decay chains.

Fig. 8(b) also shows some other examples of gamma ray spectra that enabled us
to identify other isotopes. Table 3 lists all the nuclei that were identified
via isomer fed delayed $\gamma$-rays [16] and their associated energies. While
our single HPGe detector does not provide details of the feeding of isomer
decays into the delayed $\gamma$-ray we observe, there is enough consistent
information obtained from all of these decays that there can be no doubt about
isotope assignments shown in Fig. 7.

Table 3: A list of isotopes that have been confirmed by isomer tagging, their corresponding charge states measured in the PID, and some characteristic gamma rays seen in the HPGe.  Isomer-tagged Isotopes | Charge States | Typical Gammas (keV)
---|---|---
196Pb | 79+ , 80+ | 337.3 , 371.9 , 689.0
197Pb | 80+ | 387.0 , 531.6 , 589.0
198Pb | 80+ , 81+ | 197.6 , 317.9 , 450.9
192Hg | 78+ | 283.4 , 422.8 , 745.5
194Hg | 78+ | 280.2 , 427.9 , 636.3
190Au | 77+ | 232.3 , 854.9 , 427.8
191Au | 77+ , 78+ | 264.0 , 420.1 , 725.2
199Bi | 81+ | 145.7 , 499.6 , 1002.2
201Bi | 81+ | 185.8 , 411.7 , 967.4

###  5.2 Beam Resolution

Fig. 3 visually displays the amount of separation between isotopes achieved
with HEIST. To make more concrete comparisons to our estimated ToF
performance, we first put a gate on the PID spectrum shown by the red dashed
line in Fig. 9(a) and plot the enclosed data as a function of the ToF in Fig.
9(a).

(a) PID with gate for ToF.

(b) ToF spectrum.

Figure 9: Shown in (8(a)) is the PID spectrum with a gate (in red) that
provides information about the ToF resolution. Shown in (6(b)) is the ToF
spectrum for the gated data.

The time resolution for each peak is about 300 ps, which indicates that each
of these secondary beams follows its own tightly defined trajectory so that
the final time resolution is consistent with the simple estimate based on the
noise and the rise times for the MCP signals made in Sec. 2.3.

(a) PID with gate for ToF.

(b) ToF spectrum.

Figure 10: Shown in (8(a)) is the PID spectrum with a gate (in red) that
provides information about the ToF resolution. Shown in (6(b)) is the ToF
spectrum for the gated data.

To make similarly concrete comparisons to our estimated $\Delta E$ resolution,
we apply the nearly vertical gate on the PID spectrum shown in Fig. 10(a), and
plot the enclosed data as a function of the the energy loss $\Delta$E in Fig.
10(b). The observed $\Delta$E resolution is approximately 2.2 % (FWHM) of the
average $\Delta$E deposited in the MUSIC chamber for that isotope. This is
about 2.5 times larger than the estimated $0.85~{}\%$ resolution given by the
calculated straggling using SRIM. To explore this discrepancy, a GEANT4 [17,
18, 19] Monte-Carlo simulation was made using the physical detector geometry
to explore secondary effects, like delta electron production. The simulation
showed an energy resolution of 3 % (FWHM), consistent with, though somewhat
larger than, the experimentally determined resolution.

It is impossible to quantify the performance of HEIST in a single number.
Because the tails of the energy and time distributions of different species
overlap, the purity of an isotopic selection depends on the cut made. The
maximum purity of a cut is determined by the amount of charge state
contamination from same Z isotopes. If there is no directly overlapping charge
state in the acceptance of the beamline, a cut can be made that has an
arbitrarily high purity, at the cost of lost statistics. The typical 198Pb80+
cut show in Fig. 9(a) contains 89% of all 198Pb80+ in the beam, and has a
purity of 86%. A purity of $\geq 99\%$ 198Pb80+ was achieved using a cut
containing less than 20% of the all 198Pb80+ in the beam.

Table 4: Theoretical ToF calculated by LISE++ compared to the measured ToF after absolute time calibration of the MCPs. Isotope | LISE++ ToF ($ns$) | Data ToF (ns)
---|---|---
198Pb80+ | 292.14 | 292.06
197Pb80+ | 290.88 | 290.86
196Pb80+ | 289.61 | 289.68
196Pb79+ | 292.75 | 292.64
193Hg78+ | 292.07 | 291.98
192Hg78+ | 290.77 | 290.74

###  5.3 Charge State Contamination

One of the challenges of data from a RIB containing high-Z isotopes is the
issue of charge state contamination, or the presence of more than one charge
state configuration for a given isotope in the beam. The acceptance of our
beam line limits the number of charge states of a given isotope to one or two.
Most isotopes arrive at the MUSIC in a helium-like configuration with Q/e =
Z-2. However, that does not prevent contamination from another isotope with a
less probable charge state from contributing to the PID spectrum within the
same PID gate.

After identification of the isotopic species through isomer tagging in the
HPGe, LISE++ was used to calculate ToF for several different charge states of
each isotope. Table 4 shows a comparison of the calculated ToF from LISE++ and
the corrected experimental data. The measured and calculated ToF agree to
within 1 ns, whereas the ToF difference between adjacent charge states for a
given isotope is on the order of several ns, allowing for the unique
identification of the charge state.

(a) PID with gate on 196Pb $\gamma$-rays.

(b) Ungated PID.

Figure 11: An example of the presence of multiple charge states in the RIB for
196Pb. The main 80+ charge state is clearly visible in the ungated PID, while
the 79+ state is not obvious. (11(a)) shows the the measured PID for events
with a gamma ray fed by the decay of an isomer state in 196Pb. (11(b)) shows
the full PID with the location of 196Pb charge states circled in red.

Examples of both lithium-like and hydrogen-like secondary charge states have
been detected and identified using the HPGe. Separation of neighboring charge
states in ToF allows for the observed ratio of charge states to be measured
accurately. Fig. 11 illustrates this process when the HPGe selection is set to
select 196Pb. The two resulting peaks in Fig. 11(a) indicate the presence of
two separated charge state peaks, determined to be the helium-like (80+) and
lithium-like (79+) configurations by ToF. The peak from the lithium-like
charge state is not immediately obvious in the ungated PID, as seen in Fig.
11(b). The observed ratio between charge states of the same isotope depends on
the acceptance of the beamline and may differ from the actual production ratio
at the A1900.

Fig. 12 shows measured charge state distributions for the two most common
charge states of 196Pb and 191Au. The physical rigidity cut of the A1900 is
shown by a vertical bar. Using the GLOBAL [20] model implemented in LISE++,
the theoretical charge state distribution across a range of A1900 rigidity
cuts was calculated, as seen in Fig. 12.

Figure 12: The measured charge state distributions of (a) 196Pb, and (b) 191Au
are plotted as a function of rigidity in the A1900. The solid symbols indicate
the experimental values, while the open symbols shows the LISE++ simulation
results using the GLOBAL model.

There is a discrepancy between the calculated charge state distributions and
experimental measurement. The model reproduces the data better if the B$\rho$
value of the A1900 and beamline is adjusted upwards in the calculation by 0.3%
to 3.283 Tm from 3.271 Tm. This discrepancy is larger than the uncertainty in
the calibration of the A1900 rigidity which is believed to be good to $\pm
0.1\%$. This discrepancy can be explained by GLOBAL under-predicting the
electron capture cross-sections for these nuclei. Regardless of the reason for
the discrepancy, the predictions of GLOBAL are consistent with all
measurements made if the adjusted B$\rho$ value of 3.283 Tm is used, as seen
in Table 5.

Table 5: A comparison between the theoretical and measured charge state distributions of isotopes Pb, Hg, and Au with the charge state $Q=Z-3$, $Z-2$, and $Z-1$. The theoretical charge state distributions were calculated using the GLOBAL model with LISE++. The experimental charge state distributions were extracted by gating on the corresponding $\gamma$-rays from known isomers. Isotopes |  | Calculation |  |  | Experiment |
---|---|---|---|---|---|---
| $Z-3$ | $Z-2$ | $Z-1$ | $Z-3$ | $Z-2$ | $Z-1$
196Pb | 59% | 41% |  | 52% $\pm$ 3% | 48% $\pm$ 3% |
197Pb |  | 100% |  |  | 100% |
198Pb |  | 100% |  |  | 99% $\pm$ 2% | 1% $\pm$ 1%
191Hg | 27% | 73% |  | - | - | -
192Hg |  | 100% |  |  | 100% |
193Hg |  | 100% |  | - | - | -
189Au | 2% | 98% |  | - | - | -
190Au |  | 100% |  |  | 100% |
191Au |  | 95% | 5% |  | 95% $\pm$ 5% | 5% $\pm$ 1%

###  5.4 Beam Tracking

The MUSIC detector has the capability to measure the position and direction of
the beam as it passes through the detector. As discussed in Sec. 3.2.3, the
x-position is determined by charged division with the triangular pads at the
entrance and exit of the MUSIC detector and the y position is determined from
the drift time of the electrons to the Frisch grid.

Figure 13: (Color online) Experimental beam hit pattern on the entrance of the
MUSIC. The red square shows the edge of the MUSIC window.

To check the x- and y- position resolution, the last quadrupole magnet along
the beamline was turned off so the beam would clip the edges of the MUSIC
entrance window. Fig. 13 shows the measured beam profile. The MUSIC entrance
window’s right and upper edges are clearly shown. The corresponding x- and y-
position distribution is shown in the upper panels of Fig. 14. A numerical
differentiation of the spectrum was performed and fit with a Gaussian in order
to determine the position resolution, shown in the lower panels of Fig. 14.
The obtained x-position resolution was 1.45 mm, while the y-position
resolution was 0.27 mm.

Figure 14:  (Color online) (top panels) Position distribution in X and Y
direction. (bottom panels) The numerical differentiation of the position
spectrum are used to determine the position resolution. The edges are fitted
with a Gaussian function.

The estimation of the y position resolution can be improved. There are 9
independent measurements of the y position, one from each rectangular anode
pad. A more accurate y position resolution can be obtained by taking the
difference between the y positions of neighboring pads. Fig. 15 shows the y
position difference between the average of the $(n+1)^{th}$ $\&$ $(n-1)^{th}$
pads, and $n^{th}$ pad. The FWHM of this difference distribution is 0.19 mm,
so the y resolution for each pad is $0.19/\sqrt{3/2}=0.16$ mm.

Figure 15: The Y position difference between the average of $n+1$ $\&$ $n-1$
and $n$ rectangle pads.

As seen in Figs. 16 and 14, within the MUSIC detector there is a structure to
the x-position of the beam. Even though the momentum selection at I2 is very
precise, the wedge foil modifies the energy of the beam and therefore its
rigidity, and different beam particles ultimately enter the MUSIC at different
x positions leading to the structure observed.

Figure 16: (Color online) Beam tracking displayed with hit pattern at
different positions from upstream to downstream. The red lines show the edge
of detector windows.

With accurate measurements of the positions at the entrance and exit window of
the MUSIC, the beam particle trajectory can be extrapolated to other detectors
along the beamline. Fig. 16 shows the beam tracking results with hit patterns
at different positions. At MCP2, before the MUSIC, the beam was well focused.
In the MUSIC the beam was off-center, indicating a slight misalignment of the
apparatus. The beam nonetheless enters the acceptance of the ATTPC entrance
window 1.2 m downstream of the MUSIC detector.

##  6 Conclusion

The HEIST system was designed, built, and commissioned. It consists of two MCP
detectors, a MUSIC ion chamber, and a HPGe detector. HEIST was able to
successfully tag and track secondary isotopes produced from the fragmentation
of a 85 MeV/A 208Pb beam. The time of flight performance of this system
betters the design criterion, but the charge resolution, while acceptable, is
somewhat worse than the design value. GEANT4 calculations for the charge
resolution are consistent with the measured charge resolution, indicating that
the larger measured energy loss resolution results from secondary processes
such as delta electrons scattering out of the active region of the MUSIC
chamber. A typical cut containing 89% of all 198Pb+80 in the beam had a purity
of 86%. The maximum purity of a cut is limited by any charge states of the
same element that may overlap in velocity with the isotope of interest. If
there is no _directly_ overlapping charge state then a cut with an arbitrarily
high purity can be made at the cost of lost statistics. In the case of
198Pb+80, a cut containing less than 20% of all 198Pb+80 in the beam with
purity greater than 99% can be achieved.

Using isomer tagging, the charge state distributions of the isotopes in the 74
MeV/A radioactive ion beams were measured. These distributions were compared
to calculations done with the GLOBAL model using LISE++ to simulate the beam
optics. GLOBAL was found to under-predict the electron capture cross-sections;
nevertheless, good agreement between experiment and model was achieved by
adjusting the magnetic rigidity (and therefore velocity) of the beam fragments
in the simulation.

## Acknowledgements

We would like to acknowledge support from Michigan State University, from the
U.S Department of Energy under Grant Nos. DE-SC0020451, DE-SC0014552, and DE-
NA0003908, from the National Science Foundation under Grant Nos. PHY-1565546,
and PHY-1712832.

## References

  * [1] A. Gade and B. Sherrill, “NSCL and FRIB at Michigan State University: Nuclear science at the limits of stability,” Physica Scripta, vol. 91, no. 5, p. 053003, 2016.
  * [2] R. Anne, D. Bazin, A. Mueller, J. Jacmart, and M. Langevin, “The achromatic spectrometer LISE at GANIL,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 257, no. 2, pp. 215–232, 1987.
  * [3] H. Geissel, P. Armbruster, K. H. Behr, A. Brünle, K. Burkard, M. Chen, H. Folger, B. Franczak, H. Keller, O. Klepper, et al., “The GSI projectile fragment separator (FRS): a versatile magnetic system for relativistic heavy ions,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, vol. 70, no. 1-4, pp. 286–297, 1992.
  * [4] T. Kubo, M. Ishihara, N. Inabe, H. Kumagai, I. Tanihata, K. Yoshida, T. Nakamura, H. Okuno, S. Shimoura, and K. Asahi, “The RIKEN radioactive beam facility,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, vol. 70, no. 1-4, pp. 309–319, 1992.
  * [5] D. . J. Morrissey, B. Sherrill, M. Steiner, A. Stolz, and I. Wiedenhoever, “Commissioning the A1900 projectile fragment separator,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, vol. 204, pp. 90–96, 2003.
  * [6] J. Bradt, D. Bazin, F. Abu-Nimeh, T. Ahn, Y. Ayyad, S. B. Novo, L. Carpenter, M. Cortesi, M. Kuchera, W. Lynch, et al., “Commissioning of the active-target time projection chamber,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 875, pp. 65–79, 2017.
  * [7] E. Segre, H. Stau, H. B. Albrecht, J. Ashkin, et al., Experimental nuclear physics. volume I.  New York : John Wiley & sons ; London : Chapman & Hall, cop. 1953., 1953\.
  * [8] O. Tarasov and D. Bazin, “LISE++: Radioactive beam production with in-flight separators,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, vol. 266, no. 19-20, pp. 4657–4664, 2008.
  * [9] J. F. Ziegler, M. D. Ziegler, and J. P. Biersack, “SRIM–the stopping and range of ions in matter,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, vol. 268, no. 11-12, pp. 1818–1823, 2010.
  * [10] B. Wiggins, V. Singh, J. Vadas, J. Huston, T. Steinbach, S. Hudan, and R. T. deSouza, “Development of a compact E$\times$ B microchannel plate detector for beam imaging,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 866, pp. 202–206, 2017.
  * [11] J. L. Wiza et al., “Microchannel plate detectors,” Nucl. Instrum. Methods, vol. 162, no. 1-3, pp. 587–601, 1979.
  * [12] R. Schneider and A. Stolz, “Technical manual ionization chamber music80,” tech. rep., Tech. Rep.(Technische Universität München, 2000), 2000.
  * [13] O. Bunemann, T. Cranshaw, and J. Harvey, “Design of grid ionization chambers,” Canadian journal of research, vol. 27, no. 5, pp. 191–206, 1949\.
  * [14] S. Biagi, “Monte carlo simulation of electron drift and diffusion in counting gases under the influence of electric and magnetic fields,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 421, no. 1, pp. 234–240, 1999.
  * [15] G. F. Reinking, L. G. Christophorou, and S. R. Hunter, “Studies of total ionization in gases/mixtures of interest to pulsed power applications,” Journal of Applied Physics, vol. 60, no. 2, pp. 499–508, 1986.
  * [16] A. Sonzogni, “NuDat 2.0: Nuclear structure and decay data on the internet,” in AIP Conference Proceedings, vol. 769, pp. 574–577, American Institute of Physics, 2005.
  * [17] S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, G. Cooperman, G. Cosmo, P. Degtyarenko, A. Dell’Acqua, G. Depaola, D. Dietrich, R. Enami, A. Feliciello, C. Ferguson, H. Fesefeldt, G. Folger, F. Foppiano, A. Forti, S. Garelli, S. Giani, R. Giannitrapani, D. Gibin, J. Gómez Cadenas, I. González, G. Gracia Abril, G. Greeniaus, W. Greiner, V. Grichine, A. Grossheim, S. Guatelli, P. Gumplinger, R. Hamatsu, K. Hashimoto, H. Hasui, A. Heikkinen, A. Howard, V. Ivanchenko, A. Johnson, F. Jones, J. Kallenbach, N. Kanaya, M. Kawabata, Y. Kawabata, M. Kawaguti, S. Kelner, P. Kent, A. Kimura, T. Kodama, R. Kokoulin, M. Kossov, H. Kurashige, E. Lamanna, T. Lampén, V. Lara, V. Lefebure, F. Lei, M. Liendl, W. Lockman, F. Longo, S. Magni, M. Maire, E. Medernach, K. Minamimoto, P. Mora de Freitas, Y. Morita, K. Murakami, M. Nagamatu, R. Nartallo, P. Nieminen, T. Nishimura, K. Ohtsubo, M. Okamura, S. O’Neale, Y. Oohata, K. Paech, J. Perl, A. Pfeiffer, M. Pia, F. Ranjard, A. Rybin, S. Sadilov, E. Di Salvo, G. Santin, T. Sasaki, N. Savvas, Y. Sawada, S. Scherer, S. Sei, V. Sirotenko, D. Smith, N. Starkov, H. Stoecker, J. Sulkimo, M. Takahata, S. Tanaka, E. Tcherniaev, E. Safai Tehrani, M. Tropeano, P. Truscott, H. Uno, L. Urban, P. Urban, M. Verderi, A. Walkden, W. Wander, H. Weber, J. Wellisch, T. Wenaus, D. Williams, D. Wright, T. Yamada, H. Yoshida, and D. Zschiesche, “Geant4—a simulation toolkit,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 506, no. 3, pp. 250–303, 2003.
  * [18] J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce Dubois, M. Asai, G. Barrand, R. Capra, S. Chauvie, R. Chytracek, G. Cirrone, G. Cooperman, G. Cosmo, G. Cuttone, G. Daquino, M. Donszelmann, M. Dressel, G. Folger, F. Foppiano, J. Generowicz, V. Grichine, S. Guatelli, P. Gumplinger, A. Heikkinen, I. Hrivnacova, A. Howard, S. Incerti, V. Ivanchenko, T. Johnson, F. Jones, T. Koi, R. Kokoulin, M. Kossov, H. Kurashige, V. Lara, S. Larsson, F. Lei, O. Link, F. Longo, M. Maire, A. Mantero, B. Mascialino, I. McLaren, P. Mendez Lorenzo, K. Minamimoto, K. Murakami, P. Nieminen, L. Pandola, S. Parlati, L. Peralta, J. Perl, A. Pfeiffer, M. Pia, A. Ribon, P. Rodrigues, G. Russo, S. Sadilov, G. Santin, T. Sasaki, D. Smith, N. Starkov, S. Tanaka, E. Tcherniaev, B. Tome, A. Trindade, P. Truscott, L. Urban, M. Verderi, A. Walkden, J. Wellisch, D. Williams, D. Wright, and H. Yoshida, “Geant4 developments and applications,” IEEE Transactions on Nuclear Science, vol. 53, no. 1, pp. 270–278, 2006.
  * [19] J. Allison, K. Amako, J. Apostolakis, P. Arce, M. Asai, T. Aso, E. Bagli, A. Bagulya, S. Banerjee, G. Barrand, B. Beck, A. Bogdanov, D. Brandt, J. Brown, H. Burkhardt, P. Canal, D. Cano-Ott, S. Chauvie, K. Cho, G. Cirrone, G. Cooperman, M. Cortés-Giraldo, G. Cosmo, G. Cuttone, G. Depaola, L. Desorgher, X. Dong, A. Dotti, V. Elvira, G. Folger, Z. Francis, A. Galoyan, L. Garnier, M. Gayer, K. Genser, V. Grichine, S. Guatelli, P. Guèye, P. Gumplinger, A. Howard, I. Hřivnáčová, S. Hwang, S. Incerti, A. Ivanchenko, V. Ivanchenko, F. Jones, S. Jun, P. Kaitaniemi, N. Karakatsanis, M. Karamitros, M. Kelsey, A. Kimura, T. Koi, H. Kurashige, A. Lechner, S. Lee, F. Longo, M. Maire, D. Mancusi, A. Mantero, E. Mendoza, B. Morgan, K. Murakami, T. Nikitina, L. Pandola, P. Paprocki, J. Perl, I. Petrović, M. Pia, W. Pokorski, J. Quesada, M. Raine, M. Reis, A. Ribon, A. Ristić Fira, F. Romano, G. Russo, G. Santin, T. Sasaki, D. Sawkey, J. Shin, I. Strakovsky, A. Taborda, S. Tanaka, B. Tomé, T. Toshito, H. Tran, P. Truscott, L. Urban, V. Uzhinsky, J. Verbeke, M. Verderi, B. Wendt, H. Wenzel, D. Wright, D. Wright, T. Yamashita, J. Yarba, and H. Yoshida, “Recent developments in Geant4,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 835, pp. 186–225, 2016\.
  * [20] C. Scheidenberger, T. Stöhlker, W. Meyerhof, H. Geissel, P. Mokler, and B. Blank, “Charge states of relativistic heavy ions in matter,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, vol. 142, no. 4, pp. 441–462, 1998.


# Integrated Sensing and Communications for IoT: Synergies with Key 6G
Technology Enablers

Aryan Kaushik, Rohit Singh, Ming Li, Honghao Luo, Shalanika Dayarathna,
Rajitha Senanayake,
Xueli An, Richard A. Stirling-Gallacher, Wonjae Shin, and Marco Di Renzo A.
Kaushik is with the School of Engineering & Informatics, University of Sussex,
UK (e-mail: aryan.kaushik@sussex.ac.uk).
R. Singh is with the Department of Electronics & Communication Engineering,
Dr. B. R. Ambedkar National Institute of Technology, India (e-mail:
rohits@nitj.ac.in).
M. Li and H. Luo are with the School of Information and Communication
Engineering, Dalian University of Technology (e-mail: {mli,luohong-
hao}@dlut.edu.cn).
S. Dayarathna and R. Senanayake are with the Department of Electrical &
Electronic Engineering, University of Melbourne, Australia (e-mail:
{s.dayarathna,rajitha.senanayake}@unimelb.edu.au).
X. An and R. A. Stirling-Gallacher are with the Huawei Technologies, Germany
(e-mail: {xueli.an, richard.sg}@huawei.com).
W. Shin is with the School of Electrical Engineering, Korea University, South
Korea, and the Department of Electrical Engineering, Princeton University, USA
(e-mail: wjshin@korea.ac.kr).
M. Di Renzo is with Université Paris-Saclay, CNRS, CentraleSupélec,
Laboratoire des Signaux et Systèmes, 3 Rue Joliot-Curie, 91192 Gif-sur-Yvette,
France. (e-mail: marco.di-renzo@universite-paris-saclay.fr).

###### Abstract

The Internet of Things (IoT) and wireless generations have been evolving
simultaneously for the past few decades. Built upon wireless communication and
sensing technologies, IoT networks are usually evaluated based on metrics that
measure the device’s ability to sense information and effectively share it
with the network, which makes Integrated Sensing and Communication (ISAC) a
pivotal candidate for the sixth-generation (6G) IoT standards. This paper
reveals several innovative aspects of ISAC from an IoT perspective in 6G,
empowering various modern IoT use cases and key technology enablers. Moreover,
we address the challenges and future potential of ISAC-enabled IoT, including
synergies with Reconfigurable Intelligent Surfaces (RIS), Artificial
Intelligence (AI), and key updates of ISAC-IoT in 6G standardization.
Furthermore, several evolutionary concepts are introduced to open future
research in 6G ISAC-IoT, including the interplay with Non-Terrestrial Networks
(NTN) and Orthogonal Time-Frequency Space (OTFS) modulation.

###### Index Terms:

Integrated Sensing and Communication (ISAC), 6G Standardization, ISAC-IoT,
RIS, NTN, OTFS Modulation.

##  I Introduction

The Internet of Things (IoT) has revolutionized the way we interact with our
environment and technology. IoT devices rely on advanced communication
protocols and networks to share the acquired data in real time. Leveraging the
potential of advanced sensing and communication abilities, significant efforts
are being made to revolutionize the IoT experience further. For instance, the
radio communication division of the International Telecommunication Union
(ITU-R) already drafted new recommendations for International Mobile
Telecommunication 2030 IMT-2030 (6G) [1]. Integrated Sensing and Communication
(ISAC), one of the key 6G usage scenarios, is of particular interest since it
enables one to sense and better understand the physical world. ISAC embodies
the seamless fusion of two critical IoT components: sensing capabilities and
communication infrastructure over a single integrated platform or cooperating
as individual entities over a single network and hence complements various
modern 6G IoT use cases.

Figure 1: ISAC enabled 6G IoT framework.

An illustration of the components of ISAC-enabled IoT is given in Fig. 1,
which covers almost a complete 3600 IoT framework, including enhanced
positioning, seamless connectivity, smooth monitoring, and several other
possibilities. In particular, ISAC offers numerous technological advantages,
including [2]: a) Real-time Data Insights: By combining sensing and
communication operations, ISAC empowers IoT devices to instantaneously
transmit and receive data, making it more convenient for timely decision-
making, b) Predictive Analytics: Joint utilization of emerging processing
tools and real-time data analysis enables IoT applications to predict trends,
anomalies, and potential issues, c) Personalized Experiences: IoT devices with
ISAC can tailor experiences as per the individual user’s requirements and
location.

Figure 2: DFRC-based ISAC for predictive beamforming.

The coexistence of ISAC, whether on a single platform or as individual sensing
and communication units cooperating with each other, can provide most of the
required IoT features envisioned in the next generation of wireless services.
For instance, enabling real-time data insights makes it more convenient to
support critical IoT applications such as smart cities. All such features pave
the way for innovative applications across industries, transforming the way we
interact with technology. Given the emergence of 6G-IoT and the novel features
of ISAC, this work highlights the benefits of ISAC implementations in 6G IoT,
including its key enabling techniques, use cases, challenges, and future
prospects.

##  II ISAC-enabled IoT: Benefits and Integration

###  II-A Transformative Benefits of ISAC

Better Resource Management: ISAC enables communication and sensing
functionalities on a common platform, providing better spectrum utilization to
support ultra-low latency and high bandwidth connections. This is indeed
crucial for most of the recent IoT applications, i.e., industrial automation,
autonomous vehicles, and augmented reality, etc.

Massive Connectivity: 6G and Beyond (6GB) networks aim towards a massive
number of IoT devices where ISAC can play a key role through accommodating
diverse deployment needs, ranging from smart cities to healthcare,
agriculture, smart transport, environmental monitoring and public safety
applications.

Contextual Awareness and Cost Efficiency: ISAC allows IoT devices to gather
and process rich contextual information, especially gathered from their
surroundings. An optimal utilization of these functionalities provides several
benefits, including better decision-making processes, intelligent responses,
etc., such as, for example, a reduction in frequent high-power communication,
extending the battery life of devices.

Distributed Intelligence and Edge-Cloud Computing: The coexistence enabled by
ISAC allows IoT devices to collaborate and share device-to-device information
directly with each other. This enables the networks to make decentralized
decisions at the device end itself.

Security and Privacy: ISAC can also help in enhanced security through
localized threat detection and anomaly recognition. In addition, the
integration of communication and sensing can provide enhanced user privacy,
e.g., minimizing the transmission of sensitive information.

###  II-B ISAC Support for Critical IoT Applications

6G is intended to support critical IoT applications mainly characterized by
the degree to which they fulfill various metrics, including reliability,
resilience, sustainability, responsiveness, accuracy, etc. In the following,
we summarize how the joint utilization of communication and sensing fulfills
the key aspects of critical IoT applications.

ISAC As DFRC: Under the umbrella of ISAC, Dual Function Radar Communication
(DFRC) also proposes sensing and communication functionalities on a single
platform [3], where the sensing function involves radar probing and target
mapping. DFRC can achieve simultaneous data transmission and localization of a
target source in terms of its signal’s estimated Angle of Arrival (AoA). As
depicted in Fig. 2, DFRC is helpful in millimeter Wave (mmWave) systems,
especially in high mobility scenarios, where it can provide seamless beam
estimation and prediction to support critical applications such as those in
vehicular networks.

ISAC Meets Intelligent Metasurfaces: Another parallel research thrust focuses
on the fusion of Reconfigurable Intelligent Surfaces (RIS) with ISAC to
support a seamless integration [4]. RIS is known for its ability to control
the transmission environment via tuning multi-path phases, which makes it a
convenient tool in supporting ISAC applications. RIS-added benefits to ISAC
are discussed in more detail in subsequent sections.

Figure 3: RIS deployments in the ISAC-IoT network.

##  III Intelligent Metasurfaces and AI for ISAC-IoT

###  III-A RIS-aided ISAC-IoT

Owing to the superior ability to construct an intelligent and controllable
electromagnetic environment by manipulating the electromagnetic properties of
the metasurface, RIS has been envisioned as a promising technique to
revolutionize ISAC-IoT networks. As depicted in Fig. 3, RIS can be deployed in
ISAC-IoT networks to enhance the connectivity and coverage for various
practical applications, e.g., intelligent transportation, smart cities, and
beamforming [5]. The fusion of ISAC-IoT and RIS is more than the combination
of two functionalities. ISAC-IoT grants devices not only enhanced
communication performance but also active participation in the realm of
environmental sensing.

The deployment of RIS introduces a layer of intelligence to ISAC-IoT networks,
endowing them with the capacity for real-time signal enhancement, energy-
efficient and reliable transmission and sensing, connectivity and coverage
expansion, and effective interference management. In addition, with the
ability to manipulate electromagnetic waves, intelligent metasurfaces can also
act as wireless sensors, capturing environmental data such as temperature,
humidity, and even the presence of objects, providing IoT devices with a
comprehensive understanding of their surroundings. Therefore, the deployment
of RIS offers much more than incremental performance improvements; plus, it
plays a vital role as an active contributor to ISAC-IoT networks.

Figure 4: Beampattern of RIS-assisted ISAC-IoT system (BS: diamond, RIS:
square, target: star, clutter: triangle, user: circle).

###  III-B Artificial Intelligence for RIS-aided ISAC-IoT

As discussed above, the joint utilization of RIS with ISAC can further extend
various new capabilities within the realm of IoT. RISs are designed to go
beyond passive functionality by incorporating sensors, communication modules,
and Artificial Intelligence (AI)-driven processing to enable a wide range of
advanced IoT applications. Indeed, RIS can be further fused with AI-driven
algorithms, making it convenient to support modern IoT applications. In the
following, we cover some of the key aspects of AI-driven RIS-aided ISAC-IoT:

AI Assisted RIS Training for Massive IoT: RIS provides signal enhancement at
the receiver but can require high-complexity channel training. Effective
utilization of AI models, especially based on modern Machine Learning (ML)
algorithms like deep neural networks, are intended to provide smooth formation
of relationships between transmitted signals, reflections, and received
signals. The model can be trained using the collected data from various IoT
nodes to predict the channel characteristics, such as path loss, phase shifts,
and delays, for different scenarios.

ML Based Pre-coding and Optimization: Similar to channel training, RIS
transmission precoding and optimization involve extensive computational
complexity due to numerous multi-paths and channel coefficients, thereby
scaling drastically with the advent of massive IoT. Nevertheless, ML-based
solutions can also be applied to learn complex relationships and patterns
through various RIS elements, enabling more effective precoding strategies and
optimization.

AI Added Data Security: The evolution of massive IoT is accompanied by another
issue in the form of data security, especially in critical IoT use cases
requiring sensitive data sharing. Research efforts are being conducted to
combat such issues. For instance, AI-augmented data security pillared on RIS
involves utilizing innovative AI techniques for the manipulation of
electromagnetic signals for better security.

Figure 5: Radar SNR gain versus the number of RIS reflection elements.

###  III-C Demonstrating RIS-aided ISAC-IoT: Results and Future Prospects

In order to better reveal the potential and advantage of deploying RIS in
ISAC-IoT systems, a case study is provided in this subsection. We consider an
RIS-assisted ISAC-IoT system, where a colocated multi-antenna Base Station
(BS) or Access Point (AP) simultaneously performs multi-user communications
and target detection with the assistance of an RIS. In particular, the BS/AP
simultaneously transmits data to two single-antenna users and detects one
target in the presence of a clutter source. Utilizing the algorithm in [6], we
aim to maximize the radar Signal-to-Interference-plus-Noise Ratio (SINR) and
satisfy the communication Quality-of-Service (QoS) and constant-modulus
transmit power constraints.

We first present the resulting beampattern in Fig. 4. We display the signal
power at each location in different colors according to the colorbar shown on
the right-hand side in Fig. 4. In the considered system, the channel condition
of the direct links is worse than that of reflected links. Therefore, the
beams generated by the BS are mainly transmitted toward the RIS, whose passive
beams further assist the BS to serve users and detect the target.

We plot the radar signal-to-noise ratio (SNR) gain of different paths versus
the number of RIS elements $N$ in Fig. 5. The scheme without an RIS (“w/o
RIS”) is also included for comparison. It can be easily observed that an RIS
with more reflection elements offers higher radar SNR as it provides more
spatial Degrees-of-Freedom (DoF). Besides, as the number of RIS elements
increases, the BS will generate a stronger beam toward the RIS and a weaker
beam toward the target to achieve better sensing performance because the
virtual Line-of-Sight (LoS) link established by the RIS is enhanced. In
particular, the role of the LoS link between the BS and the target dominates
for relatively small $N$, while the virtual LoS link created by the RIS is
important for large $N$.

In order to stimulate the deployment of RIS in ISAC-IoT systems and capture
the opportunities they offer, we discuss several opportunities and shed light
on various future directions, such as:

Multiple RISs: Since multiple RISs can offer additional DoFs, their
geographical distribution presents opportunities for optimizing signal
transmission and mitigating interference. By strategically deploying multiple
RISs, we can achieve high-quality communication QoS and accurate sensing
performance in hotspots owing to the improved beamforming gains. Moreover,
multiple views of a target provided by multiple RISs increase the spatial
diversity of radar echo signals and thus improve the target detection and
parameter estimation performance.

Target/Device-Mounted RIS: Deploying RIS on the surface of the target or IoT
device can effectively assist the cooperative sensing and tracking. This
approach becomes particularly valuable when dealing with a target or IoT
device that needs to be known and tracked by the ISAC-IoT system. In order to
achieve better beam direction or target tracking performance, RIS can be
deployed on the surface of a friendly target to enhance the back-scattered
signal reflected to the radar receiver, which is especially useful when the
target area is small. To achieve this goal in a complicated and dynamic
electromagnetic environment, key challenges include mathematical modeling of
the system, optimization of RIS reflection coefficients and design of RIS
control signaling, etc.

###  III-D Holographic MIMO-Aided ISAC-IoT

Holographic MIMO (HMIMO) is another groundbreaking technology that represents
a software-defined transceiver capable of producing three-dimensional (3D)
beams using holographic principles and is being considered a promising
technique for the upcoming wireless generation. Specifically, HMIMO-aided ISAC
refers to a framework that makes a joint utilization of holography MIMO
systems towards enhanced sensing and communication capabilities [7]. Since the
IoT ecosystem pillars connectivity and data exchange, the incorporation of
HMIMO further adds a layer of dynamic adaptability. Devices equipped with
HMIMO can dynamically adjust their beams to ensure seamless communication even
in challenging scenarios, such as dense urban areas.

Bringing together holographic MIMO and ISAC empowers communication
capabilities to support various modern applications, including smart city,
virtual/augmented reality, industrial automation, etc. Besides, beyond
communication, HMIMO-assisted IoT is also intended to revolutionize sensing
abilities. Via this integration, the same beams employed for communication can
be repurposed for precise sensing, opening the doors for seamless IoT
deployment for various modern applications, including environment monitoring,
traffic flow patterns, etc.

##  IV ISAC-IoT Standardization and Use Cases

###  IV-A ISAC-IoT: 3GPP Standardization

In terms of the current standardization within the 3rd Generation Partnership
Project (3GPP), the Service and System Aspects working group 1 (SA1) for the
Fifth Generation (5G) New Radio (NR) has conducted a Study Item on use cases
and requirements for future enhancement of 5G systems [8] to support ISAC
sensing services for various applications. This 3GPP SA1 study has identified
use cases with requirements for Vehicular-to-X (V2X), Unmanned Aerial Vehicles
(UAVs), 3D map reconstruction, smart city, smart home, factories, healthcare,
and applications for the maritime sector [9]. Within the indoor factory
setting and of particular relevance to ISAC for Industrial IoT (IIoT), the
following use cases are considered: detection of Automatic Guided Vehicles
(AGV) and measurement of proximity to humans, collision avoidance for
Autonomous Mobile Robots (AMR) and AGVs, the combined use of sensing and
localization for accurate positioning of AGVs and AMRs, gesture detection and
recognition, etc.

This 3GPP Study Item paves the way for future specifications of the Service
and System Aspects (SA) and the Radio Access Network (RAN) required to support
sensing services in subsequent releases (e.g., Release 19 and beyond) within
the constraints of 5G systems. It is, however, expected that 6G will be
designed from the outset to support ISAC in a more optimized way natively.
Therefore, the expectation is that 6G will support ISAC-IoT in further use
cases with more demanding Key Performance Indicators (KPIs).

###  IV-B ISAC-IoT Use Cases: Potential for 6G and Beyond

High-Accuracy Localization and Tracking: In a 6G ISAC-IoT system, the large
bandwidth beyond mmWave bands and ultra-massive MIMO technologies can provide
superior resolution and excellent multi-path resolving capabilities for high-
accuracy localization applications for both device-based (e.g., UE connected
in a 6G network) and device-free (environmental objects) scenarios. In
addition, the dense deployment of massive antennas will also enable high-
precision direction estimation. With such sensing capabilities, collaborative
robots in future smart factories can work safely with humans and also with
each other. Some examples of collaboration between moving robots may include a
drone landing on a moving carrier vehicle to get charged; a delivery robot
filling a smart container (bin/tank) when detected as empty, etc. In such
proximity use cases, centimetre-level localization accuracy is required to
perform the tasks.

Simultaneous Imaging, Mapping, and Localization: 6G ISAC-IoT based sensing
capabilities in simultaneous imaging, mapping, and localization enable mutual
performance improvements for these functions, which opens up a realm of
possibilities in 3D indoor/outdoor non-line-of-sight imaging and mapping. For
instance, the sensors on a single mobile vehicle usually have a restricted
view and limited coverage due to the weather, obstacles, and the sensors’
power control. That said, nearby moving vehicles and stationary base stations
can jointly provide a greater ﬁeld of view, longer sensing distance, and
higher resolution with the help of an ISAC system.

Thus, the vehicles can use the reconstructed map processed by the base
stations to determine their next move to achieve higher levels of autonomy.
Furthermore, the sensing resolution and accuracy signiﬁcantly improve due to
the fusion of imaging results that are shared globally through the network
with cloud-based services. Densely distributed base stations in an urban area,
together with ISAC, make environmental reconstruction and 3D localization
possible, which in turn can be used to form a virtual urban city. Similar use
cases could be applied to indoor factories with many autonomously moving AGVs
or robots.

Augmented Human Sense: With the use of much higher frequency bands, an ISAC-
IoT system can be implemented in portable devices to augment human senses and
enable people to _see_ beyond the limits of human eyes. Such capabilities can
be incorporated into portable terminals, e.g., sensing devices such as
6G-enabled mobile phones, wearables, or medical equipment implanted beneath
the human skin. This will open the door for numerous applications such as
remote surgery, detection of product defects, sink leakage detection, etc.,
where very high range and cross-range resolutions are required.

Smart Healthcare: Device-free gesture and activity recognition based on the
joint capability of sensing and ML is another promising aspect of 6G ISAC-IoT
applications to promote contactless user interfaces and camera-free
supervision where privacy can be protected. With high classification accuracy,
many functionalities, such as gesture recognition, emotion recognition,
heartbeat detection, fall detection, respiration detection, sneeze sensing,
intrusion detection, etc., can be implemented in a smart hospital in the
foreseeable future. As a novel usage scenario, a medical rehabilitation system
in a smart hospital enables automatic supervision of patients during their
physiotherapy exercises. There will be automatic prompt alerts for incorrect
movements or gestures, thus signiﬁcantly improving the patients’
rehabilitation.

###  IV-C ISAC IoT Over Non-Terrestrial Networks

As the number of IoT sensors grows exponentially, achieving massive
connectivity based on traditional terrestrial infrastructure becomes
challenging. Moreover, IoT sensors in remote areas such as mountains, forests,
oceans, deserts, and rural regions are difficult to serve through existing
Terrestrial Networks (TN). To overcome these issues, low Earth orbit (LEO)
satellite mega constellation networks with low latency and high throughput can
be promising solutions. Moreover, the integration of UAVs and high altitude
platform stations (HAPS), which are closer to the ground-based IoT sensors,
enable the creation of a more flexible Non-Terrestrial Network (NTN) [10].

ISAC-IoT Meets NTN: The interplay of NTN with ISAC-IoT provides an opportunity
that enables NTN platforms to serve both communication and sensing functions.
As shown in Fig. 6, this integration enables NTN platforms not only to
seamlessly communicate with a large number of IoT sensors in remote areas but
also to simultaneously perform remote sensing functions such as synthetic
aperture radar (SAR) [11]. In order to achieve both massive connectivity and
satisfactory sensing performance by leveraging the wide and flexible coverage
of NTN, efficient resource allocation and multiple access techniques are
vital. As depicted in Fig. 6, satisfying these demands through orthogonal
resource allocation methods like time-division and frequency-division ISAC
could be challenging due to the inefficient use of the wireless resource. In
this regard, overlapped resource allocation methods based on non-orthogonal
multiple access (NOMA) and Rate-Splitting Multiple Access (RSMA) are promising
candidates. In this case, joint waveform design and interference control
techniques become inevitable to simultaneously meet both communication and
sensing performance requirements at a satisfactory level.

Figure 6: NTN and its interplay with ISAC-IoT.

UAV-Mounted RIS for ISAC-IoT: Combining the high mobility and flexibility of
UAVs with the low cost, light weight and low power consumption of RIS, creates
an excellent means for providing reliable coverage in ISAC-IoT systems.
Lightweight RIS can be mounted on the UAV to create virtual LoS links to cover
shadowed areas. Nevertheless, it is crucial to acknowledge that signals
reflected from the UAV may interfere with target detection, and Doppler
frequency components generated by the moving UAV may introduce ambiguities in
velocity measurements. Therefore, the joint design of RIS reflection
coefficients associated with transmit beamforming, received signal processing,
and the UAV trajectory becomes an important tool for achieving satisfactory
communication and sensing performance.

###  IV-D ISAC-IoT: Facilitating OTFS Modulation

Communication in high-frequency bands provides the most efficient method to
improve the capacity of IoT networks. As a result, mmWave and terahertz bands
are becoming more popular in future IoT devices. This makes future IoT devices
more susceptible to the impact of non-ideal power amplifiers and phase noise
in high-frequency bands. Similarly, high-mobility IoT applications suffer from
high Doppler due to complex time- and frequency-variant channels. Therefore,
existing solutions fail in the context of future IoT devices that utilize
high-frequency bands and operate in high-mobility networks.

Orthogonal Time Frequency Space (OTFS) modulation is a novel approach that
provides an attractive solution for this problem due to its resilience to time
and frequency-varying channels, low Peak-to-Average-Power Ratio (PAPR),
robustness to phase noise, and higher spectral efficiency [12]. OTFS
modulation-based solutions have been proposed for several IoT application
areas, such as future vehicular networks, underwater acoustic communication,
and NTN. For example, integrated grant-free non-orthogonal multiple access
with OTFS is proposed to mitigate the severe Doppler effect and round trip
delay experienced by LEO satellite-based non-terrestrial IoT networks.Given
the advantages of using ISAC in IoT networks, it is reasonable to consider
OTFS as a potential waveform for IoT-ISAC networks. Existing research on OTFS-
ISAC enabled IoT focuses on the following topics:

High-Mobility Vehicular IoT Networks: OTFS-ISAC-enabled vehicular networks
allow the Road Side Unit (RSU) to obtain the necessary information to generate
a dynamic vehicle network. Considering the precision of delay and Doppler
shift estimation in OTFS, OTFS-ISAC signals are utilized in [13] to predict
the motion state of moving vehicles combined with wide beams to estimate the
position. This approach addresses the issue of vehicles with high speed not
being covered by narrow beams in high-mobility vehicular IoT networks.

RIS-aided OTFS for IoT: RIS-aided OTFS is capable of facilitating future IoT
networks due to the combined benefits of flexible channel configurations and
robustness in high-mobility communication. Given the real-time communication
and sensing needed in future IoT networks, a new transmission scheme is
proposed in [14] that uses delay and Doppler shifts in cascaded channels for
sensing the user, followed by configuration of RIS passive beamforming based
on the sensed parameters.

Low-Complexity Sensing for IoT: Despite the popularity of OTFS-based ISAC
solutions, existing sensing solutions involve complex algorithms and high
computational complexity, which makes them infeasible for IoT devices.
Recently, an echo pre-processing approach [15] and new waveform designs that
couple OTFS with Frequency Modulated Continuous Waveforms (FMCW) have been
proposed for OTFS-ISAC-aided IoT. However, research areas, such as sensing in
the presence of timing and frequency offsets due to distributed, non-coherent,
and asynchronous transceivers, are yet to be explored.

##  V ISAC-IoT Challenges and Future Capabilities

ISAC in IoT systems provides numerous potential benefits, although innovative
approaches are required to enable advanced ISAC coexistence. Nevertheless,
ISAC opens up several doors for future applications, research, and
integration.

###  V-A Challenges and Solutions

Network Reliability and Security: Wireless transmissions generally encounter
impairments including attenuation, interference, noise, etc., and ISAC
integration necessitates a robust network implementation. Some useful tools
may include error correction codes or adaptive modulation techniques to
maintain reliable connections. Another integration issue arises in the form of
data security, e.g., ensuring data confidentiality, device authentication,
etc.

Compliance and Regulatory Considerations: Another challenge arises in the form
of non-uniform industrial regulations, e.g., data protection, electromagnetic
compatibility, safety standards, etc. For seamless integration, ensuring the
regulatory-approved behavior of IoT devices is necessary without compromising
functionality or usability.

Cost Effectiveness and Power Management: An exponential increase in IoT
devices will proportionally affect power consumption, especially for ISAC
functions. This requires efficient power management strategies, e.g., duty
cycling, low-power components, sleep modes, etc., as well as innovative
implementation and research efforts, optimizing hardware components and
tracing, minimizing power consumption, embedding suitable communication
modules, etc.

Data Volume Handling and Processing: ISAC integration and evolving IoT
techniques are expected to generate substantial amounts of data from a wide
variety of sensors. This, in turn, requires efficient data handling and
processing. Some helpful tools in this regard include data compression,
filtering, aggregation, etc. In addition, research efforts are currently
focused on advanced data processing techniques such as edge analytics, data
fusion, etc.

###  V-B Future Capabilities

Edge Computing Integration: Forthcoming IoT systems will leverage edge
computing, enabling data processing and analysis to occur closer to the data
source. This involves deploying edge servers or gateways that employ powerful
processors, GPUs, and FPGAs to execute complex algorithms, reducing latency
and conserving network bandwidth. This integration requires the development of
edge applications, containerization techniques, and orchestration frameworks
to manage resources effectively.

Synergy with AI: As discussed above in Section III.B, ISAC combined with the
potential of AI will lead to significant advancements, creating devices that
can make decisions in real-time based on the data they collect. From an
industrial perspective, AI algorithms can analyze sensor data to predict when
maintenance is required. Similarly, AI-powered analysis of data received from
various IoT sensors can enable doctors to detect early signs of diseases and
even predict health issues before they become critical. The utilization of AI
will help in numerous IoT applications including smart cities, supply chains,
agriculture, security, smart transport, augmented logistics, environmental
monitoring, public safety, etc.

Multi-Modal Sensing Fusion: Multi-modal sensor fusion aims to integrate data
from different types of sensors/sources to create a more comprehensive and
accurate understanding of the environment. Such integration will allow IoT
systems to leverage various sensors to make more accurate decisions. For
instance, in a smart city application, capturing and processing data from
different traffic cameras and sound sensors will provide a more accurate
understanding of traffic conditions.

Swarm Intelligence and Collaborative Utilization: Apart from the above
technologies, ISAC-assisted IoT can also benefit from swarm intelligence and
collective utilization of massive IoT units and sensor data. Inspired by
nature, their collective utilization can significantly benefit IoT networks,
e.g., achieving better scalability, adaptability, etc. For instance, IoT
devices can communicate and adapt themselves via collective information,
enabling them to make group decisions towards optimizing an overall objective.

##  VI Conclusion

IoT has evolved the way we interact with technology, and the coexistence
enabled by ISAC is intended to revolutionize the IoT experience in 6G. This
paper discusses several innovative aspects of ISAC-enabled IoT in 6G.
Specifically, this work has emphasized the following contributions: a)
comprehensively explored ISAC-enabled IoT, highlighting its benefits,
applications, challenges and future prospects in 6G-IoT, b) describing the
progress of ISAC-IoT in 3GPP standardization for 6G standards, and vital use
cases of ISAC-IoT in real world plus synergies with the key technology
enablers such as RIS, AI, NTN and OTFS modulation, c) highlighting the future
potentials such as edge computing integration and multi-modal sensing fusion
for ISAC-enabled 6G IoT.

## References

  * [1] R. Liu, R. Y.-N. Li, M. Di Renzo, and L. Hanzo, “A vision and an evolutionary framework for 6G: Scenarios, capabilities and enablers,” _arXiv preprint arXiv:2305.13887_ , 2023.
  * [2] A. Kaushik, R. Singh, S. Dayarathna, R. Senanayake, M. Di Renzo, M. Dajer, H. Ji, Y. Kim, V. Sciancalepore, A. Zappone _et al._ , “Towards integrated sensing and communications for 6G: A standardization perspective,” _arXiv preprint arXiv:2308.01227_ , 2023.
  * [3] F. Liu and C. Masouros, “A tutorial on joint radar and communication transmission for vehicular networks—Part III: Predictive beamforming without state models,” _IEEE Communications Letters_ , vol. 25, no. 2, pp. 332–336, 2020.
  * [4] K. Meng, Q. Wu, W. Chen, and D. Li, “Sensing-assisted communication in vehicular networks with intelligent surface,” _IEEE Transactions on Vehicular Technology_ , 2023.
  * [5] E. Vlachos and A. Kaushik, “Subset selection based ris-aided beamforming for joint radar-communications,” in _2023 IEEE Wireless Communications and Networking Conference (WCNC)_. IEEE, 2023, pp. 1–6.
  * [6] R. Liu, M. Li, Y. Liu, Q. Wu, and Q. Liu, “Joint transmit waveform and passive beamforming design for RIS-aided dfrc systems,” _IEEE Journal of Selected Topics in Signal Processing_ , vol. 16, no. 5, pp. 995–1010, August 2022\.
  * [7] H. Zhang, H. Zhang, B. Di, M. D. Renzo, Z. Han, H. V. Poor, and L. Song, “Holographic integrated sensing and communication,” _IEEE Journal on Selected Areas in Communications_ , vol. 40, no. 7, pp. 2114–2130, 2022.
  * [8] “Study on integrated sensing and communication,” _3GPP SA WG1 Meeting_ , 2022\.
  * [9] “Feasibility study on integrated sensing and communication (Release 19),” _3GPP TR 22.837 V19.0.0_ , June 2023.
  * [10] M. Vaezi, A. Azari, S. R. Khosravirad, M. Shirvanimoghaddam, M. M. Azari, D. Chasaki, and P. Popovski, “Cellular, wide-area, and non-terrestrial IoT: A survey on 5G advances and the road toward 6G,” _IEEE Communications Surveys & Tutorials_, vol. 24, no. 2, pp. 1117–1174, 2022.
  * [11] J. Wang, X.-D. Liang, L.-Y. Chen, L.-N. Wang, and K. Li, “First demonstration of joint wireless communication and high-resolution SAR imaging using airborne MIMO radar system,” _IEEE Transactions on Geoscience and Remote Sensing_ , vol. 57, no. 9, pp. 6619–6632, 2019.
  * [12] L. Xiao, S. Li, Y. Qian, D. Chen, and T. Jiang, “An overview of OTFS for internet of things: Concepts, benefits, and challenges,” _IEEE Internet of Things Journal_ , vol. 9, no. 10, pp. 7596 – 7618, 2022.
  * [13] Z. Ling, Y. Wu, G. Zhang, and S. Sun, “Integrated sensing and communications with OTFS: Application in V2I communications,” in _International Conference on Signal And Information Processing, Networking And Computers_ , 2022\.
  * [14] Z. Li, W. Yuan, B. Li, J. Wu, C. You, and F. Meng, “Reconfigurable intelligent surface aided OTFS: Transmission scheme and channel estimation,” _IEEE Internet of Things Journal (Early Access)_ , 2023.
  * [15] K. Wu, J. A. Zhang, X. Huang, and Y. J. Guo, “OTFS-based joint communication and sensing for future industrial IoT,” _IEEE Internet of Things Journal_ , vol. 10, no. 3, pp. 1973 – 1989, 2023.