3GPP

    The 3rd Generation Partnership Project
3D

    three dimensional
5G

    fifth generation
6G

    sixth generation
AI

    artificial intelligence
AR

    augmented reality
AoA

    angle-of-arrival
AoD

    angle-of-departure
BS

    base station
CF

    consumption factor
CSI

    channel state information
DL

    downlink
D-MIMO

    distributed multiple-input multiple-output
EER

    energy efficiency ratio
EM

    electromagnetic
GDOP

    geometric dilution of precision
GNSS

    global navigation satellite system
HMI

    human-machine interaction
IQ

    in-phase and quadrature
JCS

    Joint Communication and Sensing
ISAC

    integrated sensing and communication
JRC

    joint radar and communication
JRC2LS

    joint radar communication, computation, localization, and sensing
ICI

    inter-carrier interference
IOO

    indoor open office
IoT

    Internet of Things
IRN

    infrastructure reference node
ISG

    industry specification group
JCAS

    joint communications and sensing
KPI

    key performance indicator
KVI

    key value indicator
LCA

    life cycle analysis
LoS

    line-of-sight
MAC

    medium access control
MIMO

    multiple-input multiple-output
ML

    machine learning
mmWave

    millimeter-wave
NF

    network function
NLoS

    non-line-of-sight
NR

    new radio
NTN

    non-terrestrial network
OFDM

    orthogonal frequency-division multiplexing
OTFS

    orthogonal time-frequency-space
PRS

    positioning reference signal
PHY

    physical layer
QoS

    Quality of Service
RAN

    radio access network
RAT

    radio access technology
RedCap

    reduced capacity
RF

    radio frequency
RIS

    reconfigurable intelligent surface
RTK

    real-time kinematic
RTT

    round-trip-time
SDG

    sustainable development goal
SLAM

    simultaneous localization and mapping
SNR

    signal-to-noise ratio
SIT

    sustainability, inclusiveness, and trustworthiness
SOTA

    state of the art
SL

    sidelink
ToA

    time-of-arrival
TDoA

    time-difference-of-arrival
TR

    time-reversal
TRP

    transmission and reception point
TX/RX

    transmitter/receiver
TX

    transmitter
RX

    receiver
UE

    user equipment
UN

    United Nations
multi-RTT

    multi-cell round-trip-time
UL

    uplink
UL-TDOA

    uplink time-difference-of-arrival
DL-TDOA

    downlink time-difference-of-arrival
UMi

    3D-urban micro
UMa

    3D-urban macro
UWB

    ultra-wide band
FR1

    frequency range 1
FR2

    frequency range 2
WLAN

    wireless local-area network

# Joint Communication and Sensing for 6G – A Cross-Layer Perspective ††thanks:
This work is partially supported by the European Commission through the
Horizon Europe/JU SNS project Hexa-X-II (Grant Agreement no. 101095759).

Henk Wymeersch1, Sharief Saleh1, Ahmad Nimr2, Rreze Halili3, Rafael Berkvens3,
Mohammad H. Moghaddam4,
José Miguel Mateos-Ramos1, Athanasios Stavridis5, Stefan Wänstedt5, Sokratis
Barmpounakis7, Basuki Priyanto6,
Martin Beale6, Jaap van de Beek8, Zi Ye8, Marvin Manalastas9, Apostolos
Kousaridas10, Gerhard P. Fettweis2
1Chalmers University of Technology, Sweden, 2TU Dresden, Germany, 3IDLab,
imec-UAntwerp, Belgium,
4Qamcom Research and Technology, Sweden 5Ericsson Research, Sweden,
6Sony Europe, Sweden 7WINGS ICT Solutions, Athens, Greece 8Luleå University of
Technology, Sweden, 9Nokia, Finland 10Nokia, Germany

###### Abstract

As 6G emerges, cellular systems are envisioned to integrate sensing with
communication capabilities, leading to multi-faceted communication and sensing
(JCAS). This paper presents a comprehensive cross-layer overview of the Hexa-
X-II project’s endeavors in JCAS, aligning 6G use cases with service
requirements and pinpointing distinct scenarios that bridge communication and
sensing. This work relates to these scenarios through the lens of the cross-
layer physical and networking domains, covering models, deployments, resource
allocation, storage challenges, computational constraints, interfaces, and
innovative functions.

###### Index Terms:

6G, Joint Communication and Sensing, Cross-Layer Design.

††publicationid: pubid: 978-8-3503-8544-1/24/$31.00 ©2024 IEEE

##  I Introduction

6G is expected to be the first generation of cellular systems with built-in
sensing capabilities [1]. The inclusion of sensing capabilities within
cellular systems opens many opportunities, but also challenges. Challenges
include not only the needed technical solutions but also identifying the best
usage and providing privacy and security. Sensing is a broad concept in
communication systems [2], including radar-like sensing (locating objects
within the coverage of a radio network, that are not necessarily connected to
the network), imaging and spectroscopy, as well as general feature extraction
from received waveforms [3]. Conventional positioning (i.e., locating user
equipments) can thus be interpreted as a service relying on sensing.

The use of communication signals for sensing and the use of sensing
information for improving communication leads to the concepts of integrated
sensing and communication (ISAC) and joint communications and sensing (JCAS),
which we treat as equivalent,111Strictly speaking, _joint_ means ’shared,
held, or made by two or more parts together’, while _integrated_ means ’with
various parts or aspects linked or coordinated’. We will consider these
concepts interchangeable and therefore use only JCAS. and aim to endow the
communication system with sensing capabilities. JCAS can be broken down in
different ways, based on the level of integration, as shown in Fig. 1. On the
left side, very loose integration is shown, where sensing is only integrated
in terms of the communication sites, but is based on an external sensing
system (e.g., camera) [4] that provided information to improve communication,
while on the right side, very tight integration is shown, where sensing is
concurrent with data transmission, using the same radio resources [5]. All
levels of integration involve trade-offs and synergies between communication
and sensing [1].

There has been a large number of research papers on JCAS, ranging from broad
visions [6, 7, 8] to detailed technical contributions [9, 5] as well as
extensive survey and overview papers [1, 10, 11, 12]. In parallel,
demonstration of JCAS has been covered in, e.g., [13, 14, 15, 16].
Nevertheless, few works have addressed JCAS from a broader perspective in the
6G context, considering the use cases and applications, the relation between
the physical and networking layers, and the sustainability and trustworthiness
aspects. The EU Flagship Projects Hexa-X (https://hexa-x.eu) and Hexa-X-II
(https://hexa-x-ii.eu) aim to address this broader perspective, considering 6G
radio from its many facets and thereby supporting networks beyond
communication in a way that meets the values of our future society [17].

Figure 1: JCAS levels of integration: from very loose to very tight
integration, ranging from integration of sites, integration of spectrum,
integration of infrastructure, and integration of waveforms, to integration of
radio resources.

In parallel, work is ongoing in standardization, including in 3GPP, IEEE, and
ETSI. As a global cellular telecommunications standardization body, 3GPP
foresees multiple market segments and verticals where 5G-based sensing
services can be beneficial for intelligent transportation, aviation,
enterprise, smart city, smart home, factories, consumer applications,
telepresence, and the public sector [18]. 3GPP has investigated the potential
of integrated sensing and communication technology for enabling new services
and use cases for various applications [19]. The work on radio-related aspects
is likely to start by extending the existing channel models in [20] to include
the characterization of a sensing-related channel model, incorporating aspects
related to the reflection and scattering of signals. Subsequent work, in
future 3GPP releases, would then consider technical solutions to support JCAS
in terms of the system architecture and radio access network. In addition, in
IEEE 802.11, the task group BF (TGBF) is responsible for the introduction of
wireless local-area network (WLAN) sensing [21]. More specifically, TGBF aims
to define the use of the physical layer (PHY) and medium access control (MAC)
features of the IEEE 802.11 standard for extracting measurements that can
characterize features, such as the range and velocity, of objects in an area
of interest. The work in these standardization bodies focuses on the near
future, while Hexa-X and Hexa-X-II consider a longer time horizon.

In this paper, we aim to provide an overview of the work in Hexa-X-II as it
pertains to JCAS. Starting from 6G use cases, service requirements are
extracted, leading to a small set of more precise scenarios, for both
communication and JCAS. Relevant key performance indicators are defined and
the need for novel KPIs related to sustainability and trustworthiness is
emphasized. Next, the scenarios are considered from the perspective of the
physical and networking layers, considering models, deployments, resource
allocation, and signal processing methods, and then storage and computational
challenges, interfaces, exposure, and new functions.

##  II From JCAS Use Cases and KPIs to a Cross-Layer Perspective

In this section, the focus is on 6G use cases and their relation to JCAS.
These use cases are distilled into four scenarios, for which new KPIs are
introduced. Finally, the need for a cross-layer perspective is articulated.

###  II-A Exemplifying Use Case

In traffic safety, JCAS can enhance vehicle sensing range, especially in urban
settings. For instance, at a blind corner, a vehicle could request network
sensing for pedestrians or other vehicles nearby. Even when a base station
with line-of-sight (LoS) is absent, sensing-capable UEs would cooperatively
collect and send the necessary data to the vehicle, aiding the driver in
making safer turning decisions.

Figure 2: JCAS within the 6G use cases and radio scenarios, as well as KPIs.
Zoom in for details. The figure illustrates a framework for analyzing the
radio requirements of use cases. A use case is characterized by a set of
service requirement attributes, which are associated with a set of radio
requirement attributes. Based on the analysis of six representative use cases,
four radio scenarios emerged, each emphasizing extreme values in a particular
radio requirement attribute.

###  II-B From Use Cases to Radio Scenarios

Hexa-X has introduced a set of use case families (see Fig. 2) via the
combination of a top-down approach driven by vision, core values, and research
challenges, and a bottom-up approach to illustrate the use of new
technologies. It has been observed that the Hexa-X-II use cases as shown in
Fig. 2 have also been part of the study in 3GPP [18]: smart cities, e-health,
and mobile robots are among the use-cases.

Based on analyses and requirements of these use case families [22], Hexa-X-II
has defined a small set of radio scenarios, with well-defined KPI intervals.

  1. 1.

_Extreme data rate:_ with rates above 10 Gb/s, relying on wide bandwidths and
localized coverage. Despite large bandwidths and the potential for accurate
positioning and sensing, the requirements for positioning and sensing are
expected to be very loose.

  2. 2.

_Extreme low latency and high reliability:_ with lower rates, but stringent
requirements on positioning and sensing.

  3. 3.

_Extreme connection density:_ for medium data rates, and reduced requirements
on positioning and sensing.

  4. 4.

_Extreme coverage:_ providing communication, positioning, and sensing services
everywhere with variable requirements.

JCAS will play an important role in all the above scenarios, considering the
expected developments on enabling technologies on 6G radio, networking, and
intelligent computation and protocols. Further details are elaborated in
Sections III and IV.

###  II-C New KPIs in Support of the Radio Scenarios

The benefit of the four radio scenarios is that they allow the definition of
KPI ranges. This includes the conventional KPIs for positioning and sensing
(such as accuracy, resolution, latency, availability), which are elaborated,
e.g., in [8]. On the other hand, as one of the 6G cornerstones will be
improving sustainability and trustworthiness, new measurable KPIs must be
introduced [23]. It is noteworthy that trustworthiness and sustainability, as
one of the key aspects in Hexa-X-II, have also been considered in [18].

####  II-C1 Sustainable JCAS

As an example, power consumption holds significant importance for designing
and optimizing JCAS, particularly in achieving a balance between sensing and
communication performance due to shared resources [24]. Within Hexa-X-II, to
analyze the expected shifts in power consumption, two metrics are considered.
First, _the energy efficiency ratio (EER)_ is based on [25] and captures the
ratio between the output power (in Watts) and the total power consumed (also
in Watts) by the sensing or communication system (or a component thereof) to
achieve this power output. Second, _the consumption factor (CF)_ extends the
ideas from [25], where the CF captures the ratio between the maximum rate (in
bits/s) and the total power consumed (in Watts) for communication. In
positioning and sensing, in place of the maximum rate, we consider the
detection probability, ranging accuracy, angle estimation accuracy, correct
classification rate, or radar mutual information, to define the CF.

Figure 3: A cross-layer view of JCAS, relating the radio enablers (NTN, RIS,
D-MIMO) and resource allocation with compute and storage, new functions, and
AI, to provide external exposure in support of the 6G radio scenarios.

####  II-C2 Trustworthy JCAS

Due to its differentiation from the classical communication functionality and
the sensitive nature of the produced information, this awareness of the
physical environment introduced by JCAS leads to new challenges and
limitations. More specifically, the sensing functionality may produce
information that may belong to the private domain of an entity. Also, in many
use cases, it may trigger procedures that create changes in the actual
physical environment. Thus, the successful introduction of JCAS heavily relies
on the level of trustworthiness achieved by the new functionality. We propose
to measure the level of trustworthiness by evaluating the: i) reliability, ii)
security, and iii) ethical operation of the new functionality.

  * •

_Reliability:_ In a sensing procedure, this component of trustworthiness
relates to how reliable are the obtained results in terms of accuracy for a
given Quality of Service (QoS). In particular, it evaluates the proximity of a
sensing measurement to the actual ground truth. Reliability is enhanced with
the efficient allocation of the available time, frequency, and space resources
along with the use of sophisticated signal processing algorithms for the
suppression of degrading phenomena, such as clutter.

  * •

_Security:_ This component of trustworthiness is connected with the robustness
of a sensing procedure against malicious attacks that aim to degrade or
alternate a sensing observation. The security of a sensing procedure relies on
the successful identification and characterization of an ongoing attack
combined with the use of countermeasure algorithms which minimize the effect
of this ongoing attack. For example, the effective detection and localization
of jamming nodes [26] can enable the use of spatial filtering methods along
with cryptographic resource allocation protocols.

  * •

_Ethical operation:_ As a sensing procedure may have the capacity to create
information, that is connected with the private domain of an entity, it is
important to utilize procedures that ensure the privacy of sensed entities. In
addition, a producer of sensing data, such as the network, needs to ensure
that the collected data are handled with care and protected against
unauthorized use.

###  II-D The Need for a Cross-Layer Perspective

It becomes clear that JCAS will have to rely on enablers from the radio side
(to generate the necessary measurements) as well as from higher layers (to
provide the functions and computational/storage resources). Such a cross-layer
perspective is at the core of Hexa-X-II and is visualized in Fig. 3. The right
part of the figure shows transformative integration of artificial intelligence
(AI), wherein AI orchestrates network-wide resource optimization for specific
tasks rather than optimizing individual components. Thus, the integration of
AI into JCAS naturally emerges from the inherent contradiction in optimal
resource allocation between sensing and communications. AI-powered JCAS holds
the potential to obtain optimal task-specific trade-offs, and significantly
enhance network sustainability and performance. In 3GPP Release 18 [27], the
integration of machine learning (ML) and AI was explored, where AI replaced
traditional model-based components, aiming to enhance the 5G new radio (NR)
air interface. 6G proposes a holistic approach instead, enabling AI to further
design and manage physical and medium access control resources [28]. AI is
envisioned to seamlessly integrate physical and higher layer interactions,
dynamically adapting networking resources based on radio side measurements.

##  III 6G Radio for JCAS

In this section, expanding Fig. 3, we discuss JCAS from the radio perspective,
covering the physical deployments, resources, as well as enabling
technologies.

Figure 4: JCAS deployments considered in Hexa-X-II. Each of the deployments
can operate in uplink, downlink, and sidelink. Positioning can be seen as a
service using bistatic sensing.

###  III-A Deployment Alternatives and Radio Resource Allocation

Just as conventional positioning that can be formed in uplink (UL), downlink
(DL), or even sidelink (SL) transmission, the same is possible with sensing
(see Fig. 4). Sensing includes monostatic sensing, where transmitter and
receiver are co-located, sharing an oscillator, at the expense of requiring a
full-duplex transceiver or dedicated sensing signals (e.g., pulse signals).
Moreover, sensing between base stations is also foreseen in the form of
multistatic sensing, where one or more BSs receive and process the signals
from one or more transmitting BSs. Some of the sensing modes can even occur
concurrently, such as monostatic and bistatic sensing, which provides new
challenges for integration.

The concurrent operation of different sensing modes and communication over the
same spectrum requires careful radio resource allocation. Resource allocation
is performed to avoid interference and to ensure high-quality communication
and sensing functions, in support of the radio scenarios from Section II-B.
Resource allocation in the JCAS context provides immense degrees of freedom
since user data can be re-used for sensing at no cost in radio resources.
Alternatively, dedicated pilots can be used to ensure a guaranteed sensing
performance. Sensing can be performed sparsely in time, due to the slow
mobility of objects (within the channel coherence time), and may rely on
several BSs to provide a wide field of view.

###  III-B Enabling Technologies

Within the vision for 6G to improve sustainability and inclusiveness, new
enabling technologies will be employed to provide the necessary developments
for JCAS. Some of these are non-terrestrial networks, reconfigurable
intelligent surfaces, distributed multiple-input multiple-output (D-MIMO),
Adaptive Waveform Design, Intelligent Signal Processing, etc.

  * •

_NTNs_ integrate low Earth orbit (LEO) and geosynchronous Earth orbit (GEO)
satellite constellations as well as high altitude platform stations (HAPS) to
expand the reach of localization services to remote and rural areas [29].
Localization can be based on NTN alone, or integrated with terrestrial
networks (TNs), reducing the vertical dilution of precision (DoP). Although
the integration of NTNs with emerging technologies like RIS is currently
understudied, it is expected to enhance positioning accuracy considerably for
single LEO satellite positioning [30].

  * •

_RIS_ comprises passive meta-material elements that actively control the
propagation environment, extending the reach of 6G TNs and NTNs deployed in
dense/deep urban environments [31]. RIS enhances coverage and robustness by
serving as supplementary anchors, offering LoS measurements for positioning
[32] and sensing [33], with very high angular resolution.

  * •

_D-MIMO:_ In contrast to NTN and RIS, which are mainly geared towards
improving coverage with low cost, D-MIMO is suited for scenarios with extreme
sensing requirements. By extending the conventional resolution in range and
angle domain, with an extremely large aperture from phase-coherent access
points (APs), and high LoS probability to a small number of APs, highly
accurate localization and sensing becomes possible [34].

##  IV 6G compute and protocols for JCAS

The evolution of the network towards JCAS, and the corresponding radio
enablers, will also require changes at higher layers as well (see again Fig.
3). These changes relate to compute and storage, centralized and distributed
processing, data exposure and fusion, as well as new functions, and must be
designed to match the radio enablers.

###  IV-A Compute and Storage

Sensing and localization will likely generate large amounts of data with
characteristics distinctly different from the existing user and control plane
data. These expanded data volumes, or beyond-communication data, will have to
be managed by the network or fused at various network locations, like access
points, for efficient and coherent processing [35]. Hence, appropriate design
measures must be implemented to ensure realistic scaling that does not
compromise either the delivery or the integrity of standard control plane
data. These measures might encompass interfaces for transferring the sensing
data to a new data plane and to external entities, as required. The
examination of the trade-off between various storage options is crucial for
JCAS. [36] Data storage can be accomplished by various methods, including no-
storage, in-memory storage, and external database storage. Concerning
communication overhead, both no-storage and in-memory storage options are
well-suited for applications that require quick access and processing of data.
The utilization of external database storage can potentially result in
increased communication overhead as a result of the need to retrieve and
update stored data. Additionally, the selection of storage medium has a direct
influence on the accessibility of historical information. Both no-storage and
in-memory storage options are well-suited for real-time applications, as they
allow for quick access and processing of data. On the other hand, external
database storage provides the capability to analyze historical data, which is
crucial for identifying long-term trends and making informed decisions.

The ramifications of the data increase extend to the computational domain,
thereby necessitating a reevaluation of the system’s architecture [37]. Rather
than merely managing data, we must also efficiently allocate the computational
tasks related to the data [38, 35]. As such, when a device or network node
decides to offload a computation, it will have to discover and select the
candidate compute nodes, capable of performing the requested computation while
satisfying the associated KPIs. To efficiently perform the processing
(compute) node selection, it is required to precisely define the parameters
exchanged during the discovery and localization procedure, including
processing (computing) capabilities of network and/or device nodes and
requirements, such as latency and computational load.

###  IV-B Centralized and Distributed Processing

The architectural blueprint of beyond communication services, inclusive of
centralized and distributed sensing, significantly influences compute and
storage. Centralized configurations predominantly engage pre-configured nodes
and procedures, thereby ensuring a relatively static and controlled
environment. In contrast, distributed sensing introduces layers of complexity,
enabling a dynamic environment where sensing data is both generated and
utilized across diverse pre-processing stages, ranging from raw I/Q data to
semantically enriched information [39]. The convergence of communication,
computing, and sensing will bring stringent prerequisites on latency,
privacy/security, power consumption, and data accuracy [40]. Therefore, it is
necessary to introduce the corresponding novel architectural enablers,
including additions and/or modifications of network protocols and procedures.
This imposes several challenges in connection establishment procedures that
must be addressed, such as discovery, synchronization, and coordination of the
multiple sensing and computing nodes.

###  IV-C Exposure and Fusion

The cross-layer nature of sensing functionality is evident: high-layer
applications require geometrical contextual information derived from physical
layer radio measurements, and on-demand sensing necessitates provisioning
through specific high-layer network interfaces, adhering to defined
resolution, reliability, and performance metrics. The radio data at the
physical layer (represented as raw I/Q samples or as detections with
delay/angle/Doppler), undergoes refinement and compression before being
relayed to the application [40, 36]. Architectural considerations are key in
deciding the location and timing of fusion, processing, and inference within
the network architecture. To ensure proper scaling and manageable data
volumes, it is crucial that data compression is done near the measuring node,
especially if the node is a UE, to avoid excessive raw I/Q data transmission
[41, 39]. Conversely, preserving the usefulness of sensing information across
network layers is vital for optimal accuracy and resolution. Ideally, raw
radio measurements from different locations would converge at a central unit
for fusion and inference into geometric values. Nonetheless, practical
scenarios, especially with massive sensing functionality usage, pose
challenges in balancing sensing performance with network architectural
capacity.

The exposure of data is a critical component of the JCAS system, enabling
network entities to have access to crucial sources of data. The significance
of exposure grows as the volume of data generated and utilized for JCAS
increases. Firstly, trust distinction is crucial when sharing data outside of
the network domain (i.e., with third-party applications). The increasing
interplay between networks and applications, along with the utilization of
data from diverse sources, has the potential to impact the exposure framework.
The implementation of methods for exposure controls is of utmost importance,
as it aligns with the need to adhere to data requirements and accommodate
diverse trust relationships. It is imperative to monitor the degree to which
third-party applications can access these data. Secondly, the increase in data
exposure through APIs has the potential to cause significant spikes in
traffic, hence requiring the optimization of exposure performance. Meanwhile,
while the goal is to make these data accessible to nodes wanting to utilize
them, at the same time exposure of the collected and aggregated data needs a
strong authentication and authorization mechanism to ensure that security and
privacy will not be breached [40]. Finally, it is imperative to consider the
issue of latency in relation to data exposure [42]. The inclusion of data
gathering, aggregation, cleaning, labeling, and other preparatory procedures
may result in an extended duration between the initial data collection process
and the point at which the data becomes usable and exposed.

Figure 5: Logical nodes needed for sensing and how they connect to the
architecture. The SeMF initiates the configuration of RAN for measurements on
the control plane. Measurement data is forwarded from the RAN to the SPF over
the data plane. Modified from [43].

###  IV-D New Network Functions

The 6G inherent support for JCAS necessitates certain network functions and
architectural updates for the orchestration of the sensing procedures [36,
42]. Fig. 5 outlines fundamental functionalities and presents the
responsibilities of network functions, which are logical constructs that could
be integrated into existing NFs or could be part of a dedicated Sensing
Management Function (SeMF). The sensing process begins with an application
request (e.g., inquiring if an object exists within a specified area) received
by the SeMF. The request includes an area, what type of reply that the
application expects and, consequently, the complexity of the request can vary.
Optionally, an Authorization Function may vet the request before it reaches
SeMF. The SeMF then compares the requested area with the current cellular
deployment and a 3D map of the surroundings to identify suitable nodes, like
base stations and devices, needed to fulfill the request. Node selection
depends on deployment and request’s requirements; a simpler request may be
handled with a monostatic setup, while complex ones may require a bi- or
multi-static setup. Favorable scenarios exist if base stations have LoS to the
area; otherwise, UEs with LoS might be activated. The processed request, with
node configuration details, is used to initiate the configuration of the
identified nodes needed for the measurement. The request and resulting
configurations are forwarded over the control plane. Post-measurement outputs
are sent to the SPF where the collected measurements are processed so as to
derive the sensing result, according to the sensing request requirements. The
measurements have characteristics similar to user plane data, however, there
is no user to receive the data. Therefore, we propose a new _data plane_ for
transporting raw I/Q data. Finally, The sensing result is transmitted to the
requester over the user plane, releasing the sensing process. Additionally,
Fig. 5 introduces a _data plane_ for transporting raw measurements, separate
from the control and user planes, ensuring no user is connected to the raw
measurement data.

##  V Conclusions

The purpose of this paper was to emphasize the need for a cross-layer
perspective to JCAS, a topic addressed in the Hexa-X-II project. Starting from
the use cases and radio scenarios, we first argued that new KPIs must be
introduced to capture aspects related to sustainability and trustworthiness.
We also provided an overview of the main enablers across the different layers.
From this, it becomes clear that significant work remains to define
appropriate interfaces and to support integration among the different layers,
to realize JCAS for 6G.

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  *[ISAC]: integrated sensing and communication
  *[JCAS]: joint communications and sensing
  *[WLAN]: wireless local-area network
  *[PHY]: physical layer
  *[MAC]: medium access control
  *[KPIs]: key performance indicator
  *[LoS]: line-of-sight
  *[UEs]: user equipment
  *[KPI]: key performance indicator
  *[EER]: energy efficiency ratio
  *[CF]: consumption factor
  *[QoS]: Quality of Service
  *[AI]: artificial intelligence
  *[ML]: machine learning
  *[NR]: new radio
  *[UL]: uplink
  *[DL]: downlink
  *[SL]: sidelink
  *[BSs]: base station
  *[D-MIMO]: distributed multiple-input multiple-output
  *[NTN]: non-terrestrial network
  *[RIS]: reconfigurable intelligent surface
  *[UE]: user equipment
  *[NFs]: network function