# Socially Aware V2X Localized QoS

Rafael Kaliski Yue-Hua Han This work’s funding support is from Taiwan’s
National Science and Technology Council (NSTC), grants: NSTC
108-2218-E-011-036-MY3 an NSTC 112-2221-E-110-023-Rafael Kaliski
(corresponding author) is with the Department of Computer Science and
Engineering, National Sun Yat-sen University, Republic of China, Email:
<EMAIL_ADDRESS>Yue-hua is with the Research Center for Information
Technology Innovation, Academia Sinica and also with the Department of
Computer Science and Information Engineering, National Taiwan University of
Science and Technology, Republic of China, Email<EMAIL_ADDRESS>work has been submitted to IEEE for possible publication. Copyright may be
transferred without notice, after which this version may no longer be
accessible.

###### Abstract

Vehicle-to-everything (V2X) is a core 5G technology. V2X and its enabler,
Device-to-Device (D2D), are essential for the Internet of Things (IoT) and the
Internet of Vehicles (IoV). V2X enables vehicles to communicate with other
vehicles (V2V), networks (V2N), and infrastructure (V2I). While V2X enables
ubiquitous vehicular connectivity, the impact of bursty data on the network’s
overall Quality of Service (QoS), such as when a vehicle accident occurs, is
often ignored. In this work, we study both 4G and 5G V2X utilizing Evolved
Universal Terrestrial Radio Access New Radio (E-UTRA-NR) and propose the use
of socially aware 5G NR Dual Connectivity (en-DC111Per Non-Standalone 5G[1],
in E-UTRA-NR Dual Connectivity (en-DC) 5G New Radio (NR) and 4G radios coexist
and are connected to a 4G core. UEs may connect to 4G and 5G Base Stations
(BSs) simultaneously or choose to only connect to 4G BS. En-DC enables legacy
technology to utilize 5G-enabled NR deployments.) for traffic differentiation.
We also propose localized QoS, wherein high-priority QoS flows traverse 5G
road side units (RSUs) and normal-priority QoS flows traverse 4G Base Station
(BS).

We formulate a max-min fair QoS-aware Non-Orthogonal Multiple Access (NOMA)
resource allocation scheme, QoS reclassify. QoS reclassify enables localized
QoS and traffic steering to mitigate bursty network traffic’s impact on the
network’s overall QoS. We then solve QoS reclassify via Integer Linear
Programming (ILP) and derive its approximation. We demonstrate that both
optimal and approximation QoS reclassify resource allocation schemes in our
socially aware QoS management methodology outperform socially unaware legacy
4G V2X algorithms (no localized QoS support, no traffic steering) and socially
aware 5G V2X (no localized QoS support, yet utilizes traffic steering). Our
proposed QoS reclassify scheme’s QoS flow end-to-end latency requires only
$\approx 15\%$ of the time legacy 4G V2X requires.

###### Index Terms:

QoS, V2X, Dual Connectivity, Social Networks, Non-Orthogonal Multiple Access
(NOMA), Road Condition Warning System (RCWS)

##  I Introduction

Vehicle-to-everything (V2X) is a core 5G technology. V2X and its enabler,
Device-to-Device (D2D), are essential for the Internet of Things (IoT) and the
Internet of Vehicles (IoV). V2X enables vehicles to communicate with other
vehicles (V2V), infrastructure (V2I), and networks (V2N). Vehicular network
traffic consists of low-latency, high-priority, typically safety-related, and
normal-latency normal-priority Quality of Service (QoS) flows[2, 3]. Due to
the heterogeneous networks and the multitude of services demanded by V2X (such
as live video, road conditions warning systems, internet, and traffic
information), coupled with the massive number of connected vehicles, meeting
system-wide QoS requirements remains a challenge [4]. V2X services often have
strict latency and reliability requirements. Due to the high number of
vehicles on the road and critical safety-related network traffic, V2X must
meet massive Machine Type Communication (mMTC) and Ultra-reliable low-latency
communication (URLLC) requirements.

Combining Road Side Units (RSUs) and Non-Orthogonal Multiple Access (NOMA)
enables higher vehicle density via spectrum reuse. RSUs provide localized
information, while NOMA enables more efficient use of the available spectrum
by exploiting spatial diversity[5] of the allocated vehicles. Ensuring the QoS
for vehicular and non-vehicular traffic remains challenging due to localized
hotspots of vehicular data exchange, such as at busy intersections, rush hour
on a highway, and areas prone to traffic accidents[2]. High-priority safety-
related V2X network traffic is inherently bursty and can have a payload
ranging from 190 to 1100 bytes per the 5G Automotive Association [2] (5GAA)
and $3^{rd}$ Generation Partnership Project[6] (3GPP). When many vehicles
transmit network traffic close in time and to the same macrocell Base Station
(BS), the overall system-wide QoS of non-safety V2X traffic can inadvertently
and unnecessarily be impacted.

Figure 1: Network traffic end-to-end QoS flow latency example with 4G LTE
macrocell BS and 5G NR RSU Dual Connectivity (en-DC). RSUs provide localized
resources, while macrocell BSs provide regional resources. Macrocell only does
not use traffic steering nor QoS reclassification. Socially aware uses traffic
steering only. QoS reclassify uses traffic steering and QoS reclassification,
where vehicular originating high-priority traffic is transmitted to the RSU
and then forwarded to the macrocell BS for global dissemination. QoS
reclassify reclassifies high-priority traffic as a normal-priority before
forwarding a QoS flow from an RSU to a macrocell BS. In contrast, normal-
priority traffic is directed only to the macrocell BS.

When a vehicle generates high-priority / critical traffic, such as when a
traffic accident occurs or a request for help is necessary, the network
traffic is considered exigent to the vehicles in the vicinity. As the vehicle
may not know the target audience or network destination, the exigent traffic
is necessarily broadcast over the network using the Road Condition Warning
System (RCWS) traffic type as high-priority network traffic. If we only
consider network conditions, addressing end-to-end network flow latency in 5G
V2X networks can prove difficult due to the data’s need to traverse the
network core and meet both 5G New Radio (NR) and Long Term Evolution (LTE) QoS
latency requirements over the heterogeneous network. To alleviate a network
flow’s impact on QoS in 5G V2X, we propose adding social awareness and QoS-
Reclassification to 5G V2X networks/endpoints. Socially aware networks
consider the origin and destination of the network traffic. In socially aware
networks, the network flows are steered toward different macrocell BSs per
network flow requirements. QoS-Reclassification consider locality and
priority, high-priority QoS flows only remain within the locality of their
origin before being forwarded and reclassified as normal-priority.

For example, compare the three V2X deployments in Fig. 1. Our example focuses
on 4G LTE macrocell and 5G NR RSU Dual connectivity (en-DC) to enable more
efficient downlink resource usage. Each vehicle connects to the closest in-
range RSU and the closest BS. The RSUs provide localized resources, while
macrocell BSs provide regional resources. We consider QoS Flow A a normal-
priority QoS flow, three timeslots in duration, that always uses a BS. Next,
we consider Flow B a high-priority, single-time slot in duration QoS flow,
which may use either an RSU or a BS. The primary difference between the three
deployments for Flow B is traffic steering and QoS reclassification. In
macrocell-only deployments, QoS flow B uses the macrocell BS. In socially
aware deployments, QoS Flow B is directed to an RSU and forwarded to a
macrocell BS. In contrast, in the QoS reclassify deployment, QoS Flow B is not
only directed to an RSU but is also reclassified from high-priority to normal-
priority prior to being forwarded to the macrocell BS.

Based on the example above, QoS reclassification and network traffic
differentiation can help mitigate the impact on QoS due to heterogeneous
traffic types and congestion. Since Flow B is not exigent to the macrocell BS,
it no longer needs to preempt Flow A on the macrocell BS after the BS
reclassifies it as a normal-priority. This paper proposes socially aware
traffic steering and QoS reclassification of V2X resource allocation
mechanisms. We compare these mechanisms against a macrocell-only V2X resource
allocation mechanism used in standard 4G V2X.

Socially aware V2X implies that the network considers the originating,
intermediate, and destination nodes when determining if a QoS flow’s priority
is necessary to achieve the QoS flow’s localized requirements for each network
segment. One method of extracting localized network segment-specific QoS flow
requirements is to consider the traffic type and overlay the social network
(path from the transmitter to destination group/users) on top of the network
topology to create a socially aware network [7] topology. Socially aware
networks can account for network traffic relevance for end-users and adjust
QoS accordingly for each network segment. This methodology enables us to
dynamically adjust a QoS flow’s settings to minimize a QoS flow’s adverse
impact on other QoS flows while still meeting the original QoS flow’s
requirements. As a transmitter may reside on one network, yet the receiver(s)
on another, this problem may be formulated as a heterogeneous network QoS flow
end-to-end timing closure problem. QoS Flow end-end-end time is calculated
from the first packet generation to the last packet received. This research
considers multiple non-overlapping RSUs and overlapping macrocell BSs
utilizing QoS-aware traffic steering in the context of 5G non-standalone dual-
connectivity networks.

###  I-A Contributions

Prior research did not consider how social awareness and the network traffic
type are interrelated and play an integral role in traffic-differentiated
BS/RSU selection. Neither did previous works consider the impact of high-
priority localized QoS flows on normal-priority regional QoS flows, i.e., QoS
priority was not considered a localizable property. In this paper, we expand
upon our previous work[8], provide insight into, and analyze socially aware
V2X QoS for NOMA-enabled222Our proposed scheme can work with Orthogonal
Frequency Division Multiple-Access (OFDMA), yet without NOMA only a lower
vehicular density is possible. V2X with Dual-Connectivity (DC). In addition,
we provide a more detailed network architecture and a more representative of
cellular networks’ max-min fair QoS-aware water filling NOMA-enabled resource
allocation algorithm and its approximation algorithm.

The main contributions of this work are:

  * •

We expand on our previous work[8] and discuss socially aware dynamic QoS
determination and its impact on end-to-end333In this paper, we consider QoS
Flow end-to-end latency as it better reflects the impact due to QoS flow
preemption. We only consider radio transmission and queuing time from the
transmission of the first packet of a QoS flow until the reception of the last
packet. In our analysis, we consider the back-end transmission time
negligible. latency in heterogeneous networks. We also discuss the network
architecture required to support the proposed QoS reclassification NOMA scheme
via 4G macrocell BSs, 5G RSUs, and co-located Multi-access Edge Computing
(MEC) servers.

  * •

We consider network broadcast of QoS flows (safety-related) and the more
complex scenario of groupcast communications (non-safety related) between
different BSs. In addition, we extend traffic analysis to include multiple
traffic distributions. Then, we investigate the time-out ratio and queuing
time from both a broadcast and multicast perspective.

  * •

Based on the queuing analysis, we propose a max-min fair QoS-aware water-
filling mechanism as necessitated by safety/critical network flows. Our prior
work only used a max-min QoS-aware water-filling mechanism. As such, edge
devices were more prone to starvation.

  * •

We derive a tractable approximation algorithm utilizing a maximum benefit and
minimum harm heuristic based on the proposed water-filling mechanism. Our
mechanism selects candidate Resource Block (RB) locations in a manner that
provides the most benefit to the current group while minimizing the
interference to other groups / future RB allocations. Using Simulation of
Urban Mobility (SUMO), we compared the proposed approximation algorithm to the
optimal algorithm in a real-world scenario.

In section II, we discuss related works. Then, in section III, we present the
system model and the problem formulation. After which, in section IV, we
analyze the network queues, NOMA, and their relationship to 5G QoS Flows.
Afterward, in section V, we present the max-min fair QoS-aware water filling
mechanism and its approximation in section VI. Finally, in section VII, we
present our simulations and discuss the results. Then we conclude in section
VIII and discuss future work in section IX.

##  II Related Works

Common LTE V2X resource allocation methods [9] attempt to address resource
allocation by finding a balance between V2V, V2I, and non-vehicular traffic.
Prior research utilized hypergraph matching [10] to determine how to share
resources among vehicles and cellular users. Accounting for resource
utilization and interference alone limits the achievable solutions. Thus, in
recent years, resource allocation has considered social awareness. As D2D is
an essential foundation of socially aware communication for next-generation
networks, the authors in [11] investigated the system-level performance of D2D
proximity services (ProSe) for two-hop User Equipment (UE) relays with
priority control proposed in 3GPP Release 13.

Then, in [12], the authors focus on V2I/V2V resource allocation with social
awareness. The goal was to maximize the sum capacity of V2I links while also
providing reliable V2V links via the proposed socially aware clustering
resource allocation (SACRA) algorithm. SACRA consisted of a socially aware V2V
link clustering algorithm that minimizes the intra-cluster interference of
each social community. A matching theory resource allocation algorithm was
derived. The works above do not consider dynamic QoS, where the QoS flow’s
settings are local to a subset of the network flow’s path.

One of the main pillars for V2X communication and 5G is URLLC. Unfortunately,
an aspect of URLLC is that it is particularly susceptible to interference.
[13] formulated the inter-cell Interference-Aware Radio Resource (IARR)
allocation problem to address interference, and derived a progressive
heuristic solution to improve link reliability in multi-cell cellular
networks. Another approach, proactive cell association [14], uses a virtual
cell associated with multiple access points simultaneously. Using open-loop
communication increased the reliability of the device’s connection while
decreasing latency. In [15], the authors improve the system sum capacity of
the NOMA-integrated NR V2X system via a two-phase approach. The first phase
provides a centralized graph-based sub-channel allocation for each vehicle
group. Then, in the second phase, a distributed cooperative game approach
controls NOMA-based vehicle group communication power. Finally, to address the
needs of URLLC in V2X, [16] proposed using relay-assisted NOMA to mitigate
traffic congestion and reduce latency via power control. These URLLC works
address interference and congestion yet do not consider the network side of
QoS, i.e., how the network allocates resources among QoS flows or QoS flow
prioritization.

Modern V2X systems use RSUs, enabling low-latency communications / localized
information for vehicles. However, LTE-based V2X is unlikely to address the
high number of high-priority connections necessitated by V2X and its
associated bursty URLLC-related traffic safety requirements in a QoS impact-
minimizing manner. The network must meet the diverse QoS requirements of
multiple vehicular users[2, 4]. Considering the high volume of bursty network
traffic typical to scenes of accidents, the unintentional adverse impact on
normal-priority QoS flows becomes evident. The high aggregate volume of high-
priority messages causes the end-to-end time of normal-priority traffic to
increase. In recent years, attempts to address the high volume of connections
include using NOMA[17] in V2X network designs. NOMA can increase radio
spectral efficiency by exploiting spatial diversity, i.e., the distance
between each vehicle and the BS. NOMA also enables Multi-User Diversity (MUD)
by permitting the same radio RB to be utilized by multiple users who exhibit
sufficiently different radio conditions/distances relative to the BS. In [18],
vehicles can only communicate indirectly with the macrocell BS via RSUs, i.e.,
each RSU acts as a layer-3 relay. The benefit, as determined by this research
work, was that using full-duplex NOMA-enabled relays enabled better QoS and
higher user fairness than only half-duplex relays. NOMA resource allocation
efficiency increases when the BS’s resource allocation is locally adjustable.
For more efficient V2X grouping and higher spectral efficiency [19] uses D2D
and NOMA. D2D enables a group to reuse the same resources as other groups in a
non-orthogonal localized manner, while NOMA enables more efficient intra-group
resource allocation. These prior works increased the number of simultaneously
supported users yet did not consider how localized bursty traffic impacts QoS
flow latency.

Regarding traffic differentiated routing, [20] analyzed delay-sensitive
services and their QoS requirements in a V2X multi-hop network. Based on the
tradeoff between directing traffic via Dedicated Short-Range Communications
(DSRC) to either RSUs (low-bandwidth, long end-to-end delay) vs. using
cellular connections, cellular networks incur bandwidth costs yet have a
higher bandwidth and a lower end-to-end delay. Per this insight, the authors
proposed a traffic-differentiated clustering routing mechanism in a Software
Defined Network (SDN)-enabled hybrid vehicular network. The results showed
that for a set bandwidth cost, when the cellular latency is higher than the
multi-hop DSRC latency, vehicles near RSUs will choose DSRC. When considering
localized routing, [21] analyzed and proposed a Wireless Sensor Network (WSN)
localized QoS routing protocol. In the protocol, each packet attempts to meet
the required data-related QoS metrics while also considering power efficiency.
The proposed protocol ensures a tradeoff between energy consumed and the
achieved QoS metrics. In other words, the protocol achieves low battery
consumption for low to average critical packet rates. At the same time, at
higher loads, high reliability is still maintained. When considering suitable
interfaces for communication, [22] used fuzzy logic to determine the
suitability of a link’s side link resources. The resources were either
reserved or managed, as determined by the BS, to maximize resource reuse and
improve network performance. Nevertheless, these works do not consider dynamic
QoS. Nor did they consider QoS-differentiated BS selection.

When considering QoS-aware protocols, we consider systems designed to optimize
the overall QoS. For example, [23] uses topological and link-quality aware
geography to derive an opportunistic routing protocol to determine the
selection of the next forwarding node to improve QoS. When considering IoT
applications, [24] proposed a generic IoT framework for deploying QoS-aware
multi-component IoT applications over fog infrastructures. In terms of vehicle
networks, [25] considers the social relationship between V2V users, link
reliability, and QoS of V2N and V2V users to maximize the weighted sum rate of
V2V users. In Vehicular Ad-hoc NETworks (VANETs) [26] used ant colony
optimization to compute feasible routes subject to multiple QoS constraints
dependent on the network traffic type. The works mentioned above do not
account for QoS localization wherein the network flow’s exigency depends on
the recipient and their relationship to the event’s originator. These methods
do not consider the impact of unnecessarily non-exigent high-priority events
on normal-priority QoS flows.

In recent years, Intelligent Reflective Surfaces (IRSs) and beamforming have
become attractive means of improving signal quality in VANETs. IRSs enables
the reflective surface to play a role in the communication system, enabling
optimization for different QoS goals [27] such as higher rates, enlarged
coverage area, and uninterrupted connectivity. For example, in [28], IRSs
using deep learning and beamforming was proposed to improve signal quality
while considering energy efficiency. While this method improves the radio
performance of V2N communication, this method requires additional
infrastructure to aid in signal boosting. [29] optimized user grouping and BS
blocklength allocation while performing IRS beamforming to reduce
communication latency in a URLLC downlink system. None of these works address
the QoS impact that high-priority QoS flows have on normal-priority QoS flows.

Our work bridges the gap between socially aware networks and traffic-
differentiated routing over regional and QoS-aware resources with localized
QoS flows in 5G non-stand-alone V2X networks. Our QoS-aware methodology
enables network flows to fairly compete for resources at a locality-level
granularity per each QoS flow’s exigency relative to each locality. Unlike the
prior works, we consider how a network can intelligently, dynamically, and
transparently adjust QoS settings of network traffic in a non-impacting manner
while improving the latency experienced by lower-priority network flows.

##  III System Model and Problem Statement

QoS flow management is problematic for V2X communications[4] due to the
differing technologies, architectures, network topologies, QoS definitions,
network connectivity, channel quality, target group identification
(determining the network flow’s destination), and stringent end-to-end QoS
guarantees. QoS requirements can be even more challenging in a high volume of
vehicular-originating network flows. In V2X networks, high network flow volume
commonly occurs at bottlenecks/accident points in the road network. Efficient
QoS-aware resource management requires real-time group membership and location
information to reduce QoS flow end-to-end latency and derive more effective
V2X socially aware QoS-based resource allocation methodologies via socially
aware networks.

Figure 2: V2X en-DC System Model: Every vehicle has a primary association with
a BS and potentially a secondary association with an RSU. The BS provides
regional resources, while the RSU provides local resources.

Our V2X system model is shown in Fig. 2. There are $|\textbf{R}|$ RSUs, each
assigned $B_{R}$ Mhz bandwidth symmetrically divided between $B_{R}/2$ Mhz
uplink and $B_{R}/2$ Mhz downlink, and with an effective range of $D_{R}$
(meters). Also, there are $|\textbf{B}|$ BSs, each assigned $B_{M}$ MHz
bandwidth symmetrically divided between $B_{M}/2$ Mhz uplink and $B_{M}/2$ Mhz
downlink, and with an effective range $D_{M}$ (meters). In addition, there are
$|\textbf{U}_{i}|$ vehicles / UEs per the $i^{th}$ BS. Each RSU is located at
a road intersection and is within the coverage of a single BS (RSUs do not
overlap.) The RSUs and BSs operate on orthogonal spectra, yet RSUs and BSs
share the spectra with other RSUs and BSs. RSU and BS utilize NOMA due to high
vehicular traffic and limited bandwidth. Each vehicle maintains a link to a BS
at all times. When a vehicle is within range of an RSU, it also automatically
associates with the RSU with the strongest Signal-to-Interference-plus-Noise
Ratio (SINR). Note: In this research, sets are denoted in bold. 4G macrocell
BSs are referred to as BSs. 5G RSUs are referred to as RSUs.

For our system, per Fig. 3, there are three potential segments for a QoS flow
between the originating transmitter and destination receiver. The first
segment $s_{i}=\text{\char 172}$ is between a transmitting vehicle and its
associated RSU/BS. The second segment $s_{i}=\text{\char 173}$ is from an RSU
to a BS. The third segment $s_{i}=\text{\char 174}$ is between the
transmitting RSU/BS and the target vehicle. Note: the second segment only
exists in a network flow that traverses an RSU to BS link to reach its
destination. As a result, for multi-destination network flows, each QoS flow
comprises two or more network segments.

Figure 3: V2X en-DC Network Segments: Network segment ① provides uplink
resources, ② provides inter-AP connectivity for QoS flow forwarding, and ③
provides downlink resources.

The goal of our V2X system is to minimize the overall end-to-end QoS flow
latency over the aggregate set of QoS flows per the QoS group
$\mathbf{G}_{p}$, the set of all QoS Priorities $\mathbf{P}_{g}$, subject to
the assigned QoS priorities $p_{g,i}$. Our V2X system considers all
intermediate network segments in $\mathbf{S}_{g}$ traversed by the QoS flow
$g$ between the origin and set of terminal recipients, as shown in (1). In
other words, in our proposed system, the QoS priority is a network segment
variable we optimize over to minimize unnecessary preemption and localize each
QoS flow’s priority.

$\displaystyle\operatornamewithlimits{arg\ min}_{\mathbf{x}_{g}}$
$\displaystyle\sum_{g\in\mathbf{G}_{p}}\sum_{i\in\mathbf{S}_{g}}\Delta({s_{i},p_{g,i}})+\rho_{i}\text{
}\forall p_{g,i}\in\mathbf{P}_{g}$ (1)
$\displaystyle\mathbf{P}_{g}=\begin{cases}p_{h}\text{ }(x_{g,i}=1)\\\
p_{n}\text{ }(x_{g,i}=0)\end{cases}$
$\displaystyle\mathbf{S}_{g}=\\{s_{1},s_{2},...s_{N}\\},\mathbf{x}_{g}=\\{x_{0},x_{1},...x_{N}\\}$

where $\Delta({s_{i}},p_{g,i})$ represents the delay incurred by a QoS Flow
$g$ per network segment $s_{i}$ per local queuing delay, given priority
$p_{g,i}$, and $\rho_{i}$ is the processing delay incurred by the transmitter.
$p_{g,i}$ takes the value of $p_{h}$ if the transmitter-receiver grouping
indicates that the traffic is exigent ($x_{g,i}=1$), as high-priority QoS flow
preempts normal-priority QoS flows. If the network traffic is not exigent
($x_{g,i}=0$), the priority of the QoS flow is assigned $p_{n}$, i.e., normal-
priority. Normal-priority QoS flows do not preempt other QoS flows.

###  III-A Social Awareness in Networks

In an end-to-end QoS flow management design, we consider each QoS flow’s
priority and latency dependent on its associated social group (a social group
refers to the QoS flow sender and its target recipients), its destination(s),
and its QoS priority. In other words, a socially aware network considers QoS
settings per network flow per network segment and each QoS flow’s exigency to
each recipient. Thus, each vehicle implicitly requests a social group by
identifying its QoS flow’s target recipients. The target recipients are,
therefore, considered a user-controlled yet network-managed list of members.
We note that the list of members may be implied (broadcast) or explicit per a
user-defined group of recipients (unicast/groupcast). The network manages this
list by tracking the target’s and recipient’s network locations. As such, the
network is better equipped to address each QoS flow’s requirements based on
the exigency of the QoS flow to the network segment’s locality. Note: we
define a social group’s QoS as being determined by the traffic’s type, origin,
destination, and initial QoS settings. Thus, a socially aware network tracks
each QoS flow’s destination and priority.

###  III-B System Architecture

When considering social awareness and cellular traffic’s QoS management, the
relevance and exigency of the data effectively determine the QoS utilized by
each BS/RSU. For example, safety-related messages are more relevant to the
vehicles near the incident. As such, an event-originating vehicle would
preferentially select an RSU over a BS as RSU deployments are local, i.e.,
less competition for wireless network resources, while BS deployments are
regional. To achieve dual connectivity and to ensure the system remains
compatible with 4G V2X, we utilize an en-DC[1] system topology, as shown in
Fig. 4. In an en-DC system, the LTE BS (eNodeB/eNB) is the Master Node (MN),
while a 5G NR (enhanced gNodeB/en-gNB) RSU is considered the Secondary Node
(SN). In en-DC, the BS and the RSU connect to the LTE Evolved Packet Core
(EPC). For our topology, we associate each RSU with the closest BS via an X2
interface. At the same time, each regional BS (local RSU) connects to the EPC
core via the S1 (S1-U) interface.

Figure 4: V2X en-DC System Architecture: V2X en-DC uses an LTE BS, which acts
as the master node, and a 5G NR RSU, which acts as a secondary node. Both
nodes terminate on the EPC core. The MEC QoS server establishes QoS classes
for the LTE BS, 5G QoS mapping rules for the 5G NR RSU, and performs group
membership list management via the EPC core connectivity.

The Uu air interface connects each vehicle and its associated BS and RSU. For
this DC setup, the upper layer’s Service Data Adaptation Protocol (SDAP)
function[30] utilizes EPC-connected $3^{rd}$ party MEC servers which collect
and maintain social group membership lists. The MEC servers are co-located
with the EPC[31] and modify the QoS priority and mapping rules. Vehicular
originating uplink high-priority traffic is routed from the BS/RSU to the MEC.
The MEC then broadcasts the high-priority traffic to the local BS/RSU, which
then conditionally forwards the traffic. The RSU reclassifies the QoS flow as
a normal-priority to the regional BS. For the 5G RAN (en-gNB), QoS mapping
rules[32] determine how network traffic, termed QoS flows in 5G, is mapped to
data radio bearers. Via a QoS mapping rule, the RCWS traffic type is
considered a high-priority QoS flow in both the uplink and downlink
directions. RCWS traffic is labeled high-priority via a QoS Class Identifier
for LTE. The network traffic flow is set to normal-priority when forwarded
between an RSU (5G) and the macrocell (4G). The lower protocol layers, Packet
Data Convergence Protocol (PDCP), Radio Link Control (RLC), and Medium Access
Control (MAC), handle packet transmission and reception and are unaffected by
the proposed localized QoS modification. This system design enables a vehicle
to roam between different service providers as long as the service provider’s
network connects to the same or synchronized $3^{rd}$ party QoS server. The
vehicle need not be concerned with how the QoS flow configuration changes as
the SDAP can dynamically set the QoS properties for each 5G QoS flow per the
network and the target social group. In other words, the user-specified QoS
settings implicitly become a network-assisted setting.

##  IV V2X Queuing Times and Social Networks

We now consider the impact of network traffic on QoS flow latency. Our
proposed system comprises an $M/G/k$ queue (Markov arrivals driven by Poisson
process, general distribution, k servers) associated with each radio
interface. As the backend, inter-AP, does not impose a limitation; the backend
can be modeled as an $M/G/\infty$ queue, as there is close to zero delay [33].

Regarding the radio interface, the number of concurrent QoS flows (each
serviced QoS flow corresponds to a server) depends on the geometries
associated with the NOMA RBs. For 5G, high-priority QoS flows have a RB
geometry of $360kHz\times 0.5ms$, while 5G normal-priority, or any 4G QoS
flow, has a $180kHz\times 1.0ms$ RB geometry. For a bandwidth of $20Mhz$ (4G
BS), at $10\%$ overhead, there is $20Mhz\times 0.9/180kHz=100\times 2$ (bi-
layer NOMA) $=200RBs$. If each QoS flow only requires one RB, then the maximum
$k=200$ QoS flows for the BS. For a bandwidth of $10Mhz$ (5G RSU), with only
high-priority RBs, at $10\%$ overhead, there is $10Mhz\times 0.9/360kHz\times
2$ (bi-layer NOMA) $=50RBs$. If each QoS flow only requires $1RB$, the maximum
$k=50$ QoS flows for the RSU.

For any BS/RSU $j$, when the network traffic transmission rate $\mu_{j}$ is
greater than the reception rate $\lambda_{j}$, BS/RSU congestion does not
occur as the network queue is considered stable. However, when the incoming
network traffic exceeds the transmission rate, i.e., $\lambda_{j}>\mu_{j}$,
the queue length and QoS flow latency increase due to BS/RSU congestion. In
our research, the network queue length can increase due to too many UEs
attempting to access the network simultaneously, a common occurrence in places
with high vehicular traffic. It can also occur due to inter-BS network traffic
data aggregation, such as when regional awareness of an event-generating RCWS
traffic is required. Vehicles transmit high-priority events to the nearest
RSU. We illustrate this scenario in Fig. 5. The regional BS is at level $k$,
aggregating RSU-originating traffic at level $j$. Each aggregating vehicular
traffic originates from level $i$. $\mu_{i,t}$ represents a vehicle’s traffic
transmission rate and $\mu_{j,s}$ represents the outgoing traffic from the
$s^{th}$ RSU. $\lambda_{k}$ represents the incoming traffic rate from the set
of RSUs $j,s$ to BS $k$ and $\mu_{k}$ represents BS $k$’s outgoing traffic
rate.

$\displaystyle
Q_{j,s}=\frac{\lambda_{j,s}=\sum\limits_{t=1}^{m}\mu_{i,t}}{\mu_{j,s}}$
$\displaystyle
Q_{k}=\frac{\lambda_{k}=\sum\limits_{s=1}^{n}\mu_{j,s}}{\mu_{k}=\sum\limits^{p}_{r=1}\mu_{k,r}}$

Figure 5: UE-BS Queue model: There are $m$ vehicles transmitting to RSU $j$,
and $n$ RSUs transmitting to the BS $k$ which serves $p$ vehicles. The
individual data reception and transmission rates combine and split per a
Poisson process.

When $Q_{j,s}>1$, the vehicle-originating QoS flows incur additional latency
via increased RSU transmit queuing duration. When $Q_{k}>1$, the inter-BS
traffic incurs additional latency via increased BS transmit queuing duration.
In both cases, an increased QoS flow reception delay and higher QoS flow end-
to-end latency occur. If the reception delay is too high, the network traffic
is timed out and dropped.

In non-socially aware V2X networks, $Q_{k}=Q_{j}$ due to all data utilizing
the macrocell BS. As such, $Q_{j}>1$ can occur when high or normal-priority
traffic exceeds the service rate, i.e., the BS transmission rate. In a
socially aware dual connectivity network architecture, on the other hand, RSUs
offer localized QoS, and BSs offer regional QoS. In other words, $Q_{j}$ is
less likely to be greater than one as the uplink radio resources are only
shared by the high-priority QoS flows. While socially aware networks can help
ensure high-priority network traffic’s QoS latency for local recipients, they
inherently increase the network traffic bound for the nearest regional BS.
Instead of queue congestion due to too many UEs attempting to access the
network, the increased traffic can cause queue congestion due to excessively
high inter-BS network traffic, i.e., $Q_{j}<1$, yet $Q_{k}>1$. Consequently,
the state of each queue impacts all BS traffic. Thus, the need for dynamic QoS
reclassification becomes evident.

When high-priority inter-BS traffic arrives at a destination BS, preempting
normal-priority traffic, normal-priority traffic incurs a higher queuing time.
By reclassifying the RSU originating high-priority traffic as normal-priority,
the inter-BS traffic queuing time incurred and the corresponding increase in
QoS flow end-to-end time latency can be mitigated.

###  IV-A NOMA and 5G QoS Flows

For downlink power-domain NOMA deployments, the decoding order and
interference experienced depend on the vehicle locations and allocation order
of the same RB resources. Without loss of generality, for $N$ QoS flows, we
assume a linear ordering of transmit powers $P_{i+1}\geq P_{i}$ (vehicle $i$
is the inner vehicle, vehicle $i+1$ is the outer vehicle.) For a NOMA system
where the outer vehicle’s QoS flow is decoded prior to the inner vehicle’s,
Successive Interference Cancellation (SIC) can remove all higher-powered
interfering signals. A vehicle allocated a QoS Flow with transmit power
$P_{i}$ can be decoded by successively decoding and subtracting all signals
with a transmit power higher than $P_{i}$. All signals with a transmit power
lower than $P_{i}$ are deemed as interference and are not decodable.
Therefore, NOMA-enabled vehicles can receive the composite downlink signal and
the set of the corresponding transmit powers P to determine which sets of
overlapping transmissions should be decoded and discarded prior to obtaining
the intended signal $S_{i}$, where $S_{i}=P_{i}|h_{ik}|^{2}$ ($h_{ik}$
corresponds to the transmission matrix for user $i$ in group $k$). We note
that power-domain NOMA’s improvement in spectral efficiency becomes more
evident when the spatial diversity of the vehicles sharing common RB resources
increases.

In QoS-based NOMA[34], the decoding order ensures that the highest priority
social group requiring a set of resources acquires the required bitrate over
the assigned RBs. The BS allocates unused capacity to the next highest
priority social group. In other words, for the highest priority social group
with a set demand, the allocated QoS Flow bitrate $R_{i}$ must meet the below
SINR requirement (2).

$\displaystyle
SINR_{i}(P_{i})=\frac{P_{i}|h_{ii}|^{2}}{\sum_{k=i+1}^{N}P_{k}|h_{ik}|^{2}+\sigma_{n}^{2}}$
(2) $\displaystyle B\cdot log_{2}\left(1+SINR_{i}\right)\geq
R_{i},c_{i}=f(SINR_{i})$ $\displaystyle\sum_{k=1}^{N}P_{k}\leq 1,\text{ and
}P_{j}\geq P_{k},\forall j<k$ (3)

where $N$ corresponds to the number of allocated QoS flows utilizing the same
wireless channel / RBs. $B$ corresponds to the bandwidth allocated to the BS /
RSU. $P_{i}$ corresponds to the transmit signal strength, and $h_{ik}$
corresponds to the weakest channel gain of all members in the social group $i$
who subscribe to the QoS flow $k$. The corresponding interference is
determined by $P_{k}$ and $\sigma_{n}$. $P_{k}$ is the normalized power
allocated to other QoS flows, and $\sigma_{n}$ is the Additive White Gaussian
Noise (AWGN). The variable $c_{i}$ reflects the RB capacity as determined per
the SINR $SINR_{i}$ to RB capacity mapping function $f(\cdot)$. The NOMA power
coefficient constraints for the set of $N$ QoS flows using the same RB are per
(3). For this research, we limit the number of power-domain multiplexed data
streams per RB to 2, i.e., $|\textbf{P}|\leq 2$.

###  IV-B QoS flow routing

In our proposed system, the social group of each vehicle’s QoS flow depends on
its network traffic type, origin, and destination(s). Assuming a vehicle is
associated with a BS and an RSU, it directs its QoS flow toward either an RSU
or a BS per the traffic type. In other words, the proposed system supports QoS
flow differentiated services. Each social group effectively indicates its
priority for associated QoS flows and each QoS flow’s relevance and exigency.

Figure 6: QoS reclassification and traffic steering example: (a) RSU (en-gNB)
originating high-priority QoS network flows are conditionally broadcast to the
vehicles around the RSU. The network traffic is also QoS reclassified to
normal-priority and then forwarded to the macrocell 4G BS eNB. (b, c) high-
priority and normal-priority eNB-originating QoS flows are not QoS
reclassified before being conditionally broadcast. Note: High-priority QoS
flows out of range of any RSU are transmitted to the BS (b).

As shown in Fig. 6, vehicles with critical / high-priority QoS flows transmit
their data to the nearest RSU $(a)$. If no RSUs are within range, the vehicle
transmits the QoS flow to the closest BS $(b)$. If the QoS flow is deemed
relevant to the region, then it is forwarded by the RSU to the closest
associated BS $(a)$. When the QoS flow is forwarded, the BS dynamically
changes the QoS flow’s social group to a normal-priority social group. If the
type of a QoS flow is normal-priority, it is transmitted directly to the BS
$(c)$.

This research investigates the broadcast RCWS and multiple groupcast
scenarios, such as caravans/vehicle platoons with group video chat or other
normal-priority shared data.

###  IV-C Dynamic QoS

Dynamic QoS aims to minimize the unnecessary delay incurred due to preemption
by all QoS flows, i.e., minimizing $\Delta(s_{i},p_{g,i})$ per (1). Consider a
normal-priority QoS flow A of length 2, starting at time 0, and a high-
priority QoS flow B of length 1, starting at time 1. Let the network segment’s
delay based on the QoS flow A be $\Delta_{A}$, while the optimal delay
achieved with QoS reclassification is $\Delta_{A}^{*}$. Analyzing the three
different scenarios:

  * •

Receiver considers data exigent:  The QoS flow B remains high-priority when
transmitting to the receiver. Thus, QoS flow B preempts QoS flow A, causing
QoS flow A to incur delay ($\Delta_{A}=1$). However, this delay is necessary
($\Delta_{A}^{*}=1$), i.e., $\Delta_{A}=\Delta_{A}^{*}$.

  * •

Receiver does not consider data exigent, no QoS reclassification:  The QoS
flow B preempts QoS flow A, causing QoS flow A to incur delay
($\Delta_{A}=1$). The preemption was unnecessary as QoS flow B is not
considered exigent by the receiver ($\Delta_{A}^{*}=0$), i.e.
$\Delta_{A}>\Delta_{A}^{*}$.

  * •

Receiver does not consider data exigent, uses QoS reclassification:  Since the
receiver does not consider QoS flow B exigent, QoS flow B was reclassified as
a normal-priority QoS flow, lowering QoS flow B’s impact to QoS flow A
($\Delta_{A}=0$). QoS flow A did not need to be preempted by QoS flow B
($\Delta_{A}^{*}=0$), i.e., $\Delta_{A}=\Delta_{A}^{*}$.

In terms of dynamically adapting a QoS flow’s priority, localizing QoS, we
consider the impact of the number of groups and each group’s size. A dilemma
with constantly changing group size is the computational complexity of the
resource allocation problem and how the network flows’ QoS priority between
the RSUs and BSs is adjusted to address the target group’s QoS goal. The
maximum number of groups, the number of QoS Flows, and the group size can vary
vastly from one radio frame to another. Thus a network aware dynamic QoS flow
adaption methodology is necessary.

To determine the forwarding behavior and impact of a transmission queue due to
a specific QoS flow, we need to know the population of the target group in a
network segment. If the target group is empty due to no member associating
with the BS, then the RSU-originating flow is dropped by the BS, i.e., the
BS’s transmission queue does not grow. A prerequisite for target group size
determination is that each BS must know each vehicle’s location. Collecting
location information is already performed as 4G and 5G support periodic and
event-driven tracking area updates [35] for paging purposes. A caveat with our
QoS Flow forwarding system is that the backhaul’s rate must exceed that of the
radio transmission speed. Otherwise, the radio resources may become
underutilized. For this work, we assume the backhaul’s capacity and speed
exceed those required by each BS and RSU.

##  V Max-Min Fair QoS-aware Water Filling

Each BS’s / RSU’s RB geometry444For this research, as only high-priority /
low-latency QoS flows use the RSUs, 5G NR RB geometry is limited to
$360kHz\cdot 0.5ms$. LTE RB geometry is $180kHz\cdot 1ms$ per [36]. selection,
resource assignment, and power coefficient allocation depend on each vehicle’s
Channel Quality Indicator (CQI555Detailed CQI reports[37, 38] include per RB /
sub-channel SINR.) report and the observed interference over every 180 kHz-
wide segment of radio spectrum666For NR, the maximum capacity of each RB
geometry is the same across all numerologies. Thus, selecting which geometry
to use depends on the available resources and the latency requirements[36]..
Based on the CQI report and reported interference, the BS / RSU generates an
SINR matrix to determine where each RB should be assigned. RB assignment
determines RB geometry, frequency, and temporal location.

For our NOMA deployment, we consider bi-level NOMA. Regarding resource
allocation and QoS, high-priority vehicles are first allocated QoS resources
[5]. After that, the resource allocation mechanism allocates unused resources
to normal-priority vehicles. The resource allocation problem considers
multiple factors, including the group payload demand $g_{\text{bits}}$, the
group size $g_{\text{size}}$, and the fairness $g_{\text{fair}}$ of a given
group $g$. We formulate the max-min fair resource allocation problem, which
maximizes the number of serviced users in (4).

$\displaystyle\textbf{P}^{*}:\operatornamewithlimits{arg\
max}\min_{g}\left\lceil\frac{g_{\text{bits}}}{g_{\text{size}}g_{\text{fair}}}\right\rceil,\forall
g\in\textbf{G}_{p},\forall p\in\bm{\mathcal{P}}$ (4) $\displaystyle
g^{t}_{\text{fair}}=(1-\frac{1}{T_{c}})g^{t-1}_{\text{fair}}+\frac{g^{t}_{\text{bits}}}{T_{c}}$

We define $\textbf{G}_{p}$ as the set of groups of priority $p$,
$\bm{\mathcal{P}}$ as the set of priorities $\\{p_{h},p_{n}\\}$, and the
resource assignments as $\textbf{P}^{*}$. Per max-min, we consider social
groups with large group sizes or small demands as higher priority groups for a
specific QoS priority. However, we also note that a pure max-min solution will
result in demand accumulation, i.e., many high-demand groups will aggregate
and eventually cause lower-demand groups to suffer from starvation. Therefore,
we use max-min fair to scale the group size according to a control variable
$g_{\text{fair}}$ that refers to the historical demands and changes at a
convergence rate, $T_{c}$, as time elapses.

Unfortunately, this combinatorial optimization problem in (4) is non-convex.
Thus, we derive a two-part optimal solution using an Integer Linear
Programming (ILP) Block Coordinate Descent[39] (BCD) approach that accounts
for the impact of each NOMA RB allocation on future NOMA RB allocations. The
original optimization problem can be decomposed into two sub-problems: (1.)
the NOMA inner layer RB allocation sub-problem ($I_{k}$) and (2.) the NOMA
outer layer RB allocation sub-problem ($O_{k}$). The resource allocation
process first allocates resources to high-priority QoS flows, then normal-
priority QoS flows. As the NOMA RBs are bi-layer, the 1st layer (inner) is
allocated prior to the 2nd layer (outer). The 1st layer may use up to the
entire power budget, while the outer layer may only use the unused power
budget. We optimize the resource allocation utilizing ILP for each allocation
sub-problem, per (7).

$\displaystyle\begin{gathered}\mathbf{x}=\sum_{c}^{C_{l,g}}{\sum_{i}^{L_{g}}{x_{g,l,c,i}}}\\\
x_{g,l,c,i}\in[0,1],l\in[0,1],\forall
g\in\textbf{G}_{p},p\in\bm{\mathcal{P}},\end{gathered}$ (7)

where $x_{g,l,c,i}$ indicates the assignment of a physical frequency-time RB
$i$ to group $g$ in layer $l$ with Modulation and Coding Scheme (MCS777The
corresponding MCS used by the BS is determined per each RB’s CQI, as reported
by each vehicle.) $c$. $C_{l,g}$ contains all possible MCS settings of the RB
fraction. $L_{g}$ represents the number of different frequency-time RB
locations within the radio resource allocation frame, e.g., for a RB of 180
kHz$\cdot$1 ms, there exist five different locations within a sub-frame of 900
kHz$\cdot$1 ms. The corresponding constraints are presented in (10) to (16).

Each 180 kHz$\cdot$0.5 ms RB fraction is constrained to only belong to a
single RB in the same layer to prevent overlapping allocations888An RB’s
latency is determined by its geometry-specific numerology. Each numerology
corresponds to a specific RB duration, RB bandwidth, and Sub-Carrier Spacing
(SCS)[36]. For this paper, normal-latency RBs use $15kHz$ SCS, while low-
latency RBs use $30kHz$. 5G is capable of both low-latency (360 kHz$\cdot$0.5
ms) and normal-latency (180 kHz$\cdot$1 ms) RB geometries, while LTE is only
capable of a normal-latency RB geometry.. In addition, due to the nature of
NOMA SIC, a single NOMA layer of an RB, may only be used by a single group at
a time. Each RB must be assigned to different groups in each NOMA layer per
(10).

$\displaystyle\begin{gathered}\sum_{c}^{C_{l,g}}{\sum_{i}^{L_{g}}{D(g,i,n)x_{g,l,c,i}}}\leq
1,\\\ l\in[0,1],n\in[1,N_{fr}],\forall
g\in\textbf{G}_{p},p\in\bm{\mathcal{P}},\end{gathered}$ (10)

where $D(g,i,n)$ determines if the RB $i$ of group $g$ contains RB fraction
$n$ of the $N_{rf}$ total RB fractions.

The total transmission power of RB fractions in both layers is limited to
$P_{M}$, the maximum transmit power. The corresponding power allocation
constraint is per (11).

$\displaystyle\begin{aligned}
\sum_{c}^{C_{l,g}}{\sum_{i=0}^{L_{g}}{P_{min}(c,g)D(g,i,n)x_{g,l,c,i}}}\leq
P_{M}\\\ l\in[0,1],n\in[1,N_{rf}],\forall
g\in\textbf{G}_{p},p\in\bm{\mathcal{P}}\end{aligned}$ (11)

where $P_{min}(c,g)$ gives the minimum transmission power required for group
$g$ to achieve the lower bound SINR required for MCS $c$ per the SINR of the
BS’s pilot signal.

###  V-A NOMA Inner Layer Optimization

NOMA inner layer optimization $I_{p}$ allocates resources specifically on the
first layer of NOMA, i.e., when $l=0$, per (12).

$\displaystyle x_{g,l,c,i}=\begin{cases}1,&\exists g\in\textbf{G}_{p}\text{
and }l=0\\\ 0,&\text{otherwise}\end{cases}$ (12)

By allocating the inner layer of NOMA, RBs can utilize the maximum transmit
power from the BS. In other words, the BS transmits the QoS flow data with the
best possible channel gain and MCS.

An allocated resource representation is shown in (15).

$\displaystyle\begin{gathered}R_{g,l,c}=\sum_{i=0}^{L_{g}}{x_{g,l,c,i}},\\\
l\in[0,1],\forall c\in
C_{l,g},g\in\textbf{G}_{p},p\in\bm{\mathcal{P}}\end{gathered}$ (15)

where $R_{g,l,c}$ are the time and frequency locations of the allocated RB of
MCS $c$ of group $g$ in layer $l$.

RBs allocated to higher QoS priority groups $g\in\textbf{G}_{p^{\prime}}$
where $p^{\prime}<p$ (a lower $p$ has a higher priority) must be preserved
while allocating resources to the lower QoS priority groups. However, the
assigned RBs’ location may be re-assigned to different valid locations to
utilize the radio resources fully.

The ILP determines the inner layer’s RB allocation. The inner layer’s RB
allocation forms the constraints for the following NOMA outer layer RB
allocation $O_{p}$ solution space.

###  V-B NOMA Outer Layer Optimization

The NOMA outer layer optimization $O_{p}$ builds upon the inner layer’s RB
allocations. As such, it is similar to NOMA’s inner layer allocation, yet its
allocated RBs are specifically on NOMA’s second layer. The assignment variable
is defined per (16).

$\displaystyle x_{g,l,c,i}=\begin{cases}1,&\exists g\in\textbf{G}_{p}\text{
and }l=1\\\ 0,&\text{otherwise}\end{cases}$ (16)

The NOMA outer layer allocation extends the allocated resources for high-
priority groups (per (15)) and the NOMA inner layer assigned RBs (per (15)
where $\forall g\in\textbf{G}_{p}$ and $l=0$).

Similar to inner NOMA resource allocation, location reassignment of RBs is
allowed and enables combining similar geometries and spare power RBs to
increase radio resource utilization.

For the outer layer of NOMA RB allocation, multiple MCSs are considered due to
the stochastic spare power coefficients resulting from the inner layer RB
allocations. The optimal solution derived through ILP is updated as additional
constraints for the next NOMA inner layer resource allocation of the next QoS
priority level $p+1$.

##  VI Approximation Algorithm

The optimal solution is NP-hard, as it is a combinatorial optimization
problem. We can reduce the complexity by deriving an approximation algorithm
that uses heuristics to reduce the search size of the solution space. For this
research, we examine two numerologies. They are $0.5ms\cdot 360kHz$ for high-
priority QoS flows (5G RSU only supports high-priority QoS flows) and
$0.5ms\cdot 180kHz$ for normal-priority QoS flows (4G BS supports high-
priority and normal-priority QoS flows). We observe that mixing numerologies
within an RB time-frequency location, which NOMA permits, should be prevented
due to uneven interference [40] generated by the low-latency resources onto
the normal-latency resources. Incompatible RB geometries cause inefficient
spectrum use. As such, we only permit similar RB configurations between
guaranteed and non-guaranteed resources. Therefore, we pack low-latency QoS
flows into as few RBs as possible to minimize the impact on normal-latency QoS
flows. For 5G and NOMA, resource utilization is limited by the interference
generated by non-similar RB geometries.

We propose using a maximum-benefit, minimum-harm heuristic to maximize RB
utility. With this heuristic, in order of priority, RBs are allocated to each
group based on which RB location provides the maximum gain for the group while
minimizing the impact (interference) caused to other groups. Each device
allocated RBs minimizes its transmit power while maintaining the same QoS
afforded by its maximal power budget by using power control. RB power
minimization minimizes the interference to other groups. Per this heuristic
and the above observations, the candidate RB locations are limited to the
unallocated RB locations. The number of candidate locations is upper-bounded
by $N_{fr}\cdot L_{g}$, i.e., the number of possible $180kHz$ aligned
locations in each NOMA layer.

We can allocate resources via the approximation algorithm shown in algorithm
1. The approximation algorithm is run once per radio sub-frame. It assigns
resources based on the best fit, per (17).

$\displaystyle x_{g,l,c,i}=\begin{cases}\operatornamewithlimits{arg\
min}\limits_{x}\left(c_{g,l,c,i}-D_{g}\right)\text{ if }\exists
c_{g,l,c,i}\geq D_{g}\\\ \operatornamewithlimits{arg\
min}\limits_{x}\left(D_{g}-c_{g,l,c,i}\right)\text{ otherwise}\end{cases}$
(17)

where $c_{g,l,c,i}$ represents the RB capacity for a specific RB location and
$D_{g}$ corresponds to group $g$’s demand (bits).

Algorithm 1 Hybrid 4G/5G V2X (H45V) resource allocation approximation
algorithm

1:P: list of QoS-prior social group flow sets. x : RB assignments for each
social group.

2:$U$ = set of dual layer allocable RBs.

3:$S$ = set of outer layer allocable general RBs.

4:$H$ = set of outer layer allocable combinatorial RBs.

5:$D$ = set of dual layer allocated RBs. $\textbf{G}\leftarrow\textbf{P}$

6:sort $G$ be ascending order of
$g_{\text{bits}}/(g_{\text{fair}}*g_{\text{size}})$, where $g$ represents a
social group setting. $g\leftarrow\textbf{G}$

7:$S_{\text{shape}}$ := RBs in $S$ with shape $g_{\text{shape}}$.

8:$\hat{S}_{\text{shape}}$ := RBs in $S$ except shape $g_{\text{shape}}$.

9:$H_{\text{shape}}$ := RBs in $H$ with outer layer shape $g_{\text{shape}}$.
$R\leftarrow[S_{\text{shape}},H_{\text{shape}},U]$

10:sort $R$ to ascend the remaining transmit power. $r\leftarrow R$
$g_{\text{bits}}>0$

11:remove $r$ from $R$.

12:decrease $g_{\text{bits}}$ with allocated bits.

13:add $r^{\prime}:=r+g$ into $D$. RB to satisfy
$g_{\text{shape}}\leq|\hat{S}_{\text{shape}}|$

14:sort $\hat{S}_{\text{shape}}$ to ascend the remaining transmit power.
$r\leftarrow S_{\text{shape}}^{\prime}$ $g_{\text{bits}}>0$

15:remove $r$ from S

16:decrease $g_{\text{bits}}$ with allocated bits.

17:add $r^{\prime}:=r+g$ into $H$.

For each set of prioritized QoS group flows $\mathbf{G}$, we sort the flows by
the group’s demand, fairness, and size on line 6. Then, on lines 6-17, QoS
flows are allocated resources. We first consider RB locations that have
allocated the inner layer resources to groups of identical RB shape
$g_{\text{shape}}$ (line 7). Assuming a suitable fit is possible, we assign
the QoS flow to the tightest fit possible, as the assignment minimizes the
impact on the remaining flows. After which, we consider spare outer layer
combinatorial RBs (line 8). These RBs have inner layer RBs with different
shapes than the outer layer RB. An example of spare combinatorial RBs with a
360 kHz$\cdot$0.5 ms RB allocable in the outer layer is one consisting of two
180 kHz$\cdot$1 ms RBs forming a 360 kHz$\cdot$1 ms RB in the inner layer and
a 360 kHz$\cdot$0.5 ms RB in the outer layer. Then, we select non-allocated
RBs (line 2) to allocate in an impact-minimizing manner. The non-allocated RBs
are selected at locations with neighboring RBs having identical RB shapes.
Finally, we select non-identical shape inner layer RBs containing spare outer
layer resources to form a combinatorial RB (line 9), then we allocate part of
the resources in the outer layer of the combined RB. The selection process is
performed in an impact-minimizing manner, i.e., we minimize the number of RBs
required to satisfy the outer layer’s resource demands.

###  VI-A Computational complexity

The optimal solution to the resource allocation problem is NP-hard, as all
feasible resource allocations and power settings must be investigated. Our
approximation algorithm, on the other hand, has a polynomial run time. We
define computational complexity in terms of the number of QoS priorities,
$\bf{\mathcal{P}}$, and the number of social groups $\mathbf{G}$ at a set QoS
priority $p$. First, each set of QoS flows is sorted by its rate gradient
$g_{bits}/(g_{fair}\cdot g_{size})$. Sorting requires at most $O(MlogM)$,
where $M=\max\limits_{p\in\mathbf{G}}|\mathbf{G}_{p}|$. Then the resources are
assigned in a max-min manner requiring $O(3\mathbf{G}Mlog(\mathbf{G}M)+1)$
time, as each flow must inspect three distinct sets of possible RB locations
for other QoS flows, given the RB configuration. Therefore, the time required
for our approximation algorithms is:
$O(\mathbf{G}(MlogM+M(3\mathbf{G}Mlog(\mathbf{G}M)+1)))\approx
O((\mathbf{G}M)^{2}log(\mathbf{G}M))$.

##  VII Simulation and Discussion

We simulated vehicular traffic in the area (street map) surrounding National
Taiwan University of Science and Technology (NTUST), Taiwan, by utilizing
SUMO[41].

(a) BS + RSU view

(b) RSU view

Figure 7: National Taiwan University Science and Technology SUMO Simulation:
macrocells are colored red, while RSUs are colored green.

In our simulation, we assumed a Poisson packet arrival as determined by the
network traffic type and the QoS flow/packet size. We utilized the 3GPP radio
models per TR 38.901[36]. RSUs appear as green cells, and BSs appear as red
cells with locations as per Fig. 7. The bandwidth allocated to both the BSs
and each RSU is split symmetrically per BS/RSU between uplink and downlink.
The simulation parameters we used are shown in table I. The CQI to SINR
mapping is per [37].

TABLE I: Simulation Parameters Parameter | Value
---|---
Number of Simulations | 20
Simulation Duration | 10 seconds
Average Number of Vehicles | $100\to 140$
CQI update rate | 100 ms
BS/RSU Bandwidth (symmetric) | 20 Mhz /10 Mhz
BS/RSU Tx power | 23 dBm / 10 dBm
BS/RSU Radio model | UMa / UMi
BS/RSU Frequency | 2.0 GHz / 3.5 GHz
BS/RSU Range: $D_{M}$ / $D_{R}$ | 500m / 50m
Number of BSs/RSUs: $|B|$ / $|R|$ | 2 / 5
Critical, High-priority, QoS flow size $(\mathcal{F})$ |  $\mathcal{U}$(300-1100) bytes
Critical, High-Priority, average flow size $(\overline{\mathcal{F}})$ |  $700$ bytes
Critical, High-Priority, average request rate $(\mathcal{R})$ |  $11$ pps999packets per second
Critical, High-Priority, average bit rate $(\mathcal{D})$ |  $64$ kbps
General, Normal-Priority, QoS flow size $(\mathcal{F})$ |  $\mathcal{U}$(64-2048) bytes
General, Normal-Priority, average flow size $(\overline{\mathcal{F}})$ |  $1056$ bytes
General, Normal-Priority, average request rate $(\mathcal{R})$ |  $60$ pps
General, Normal-Priority, average bit rate $(\mathcal{D})$ |  $500$ kbps
Fairness time constant $T_{c}$ | 300 ms

For our simulations, the relationship between the number of groups ($N_{G}$),
max group size ($S_{G,max}$), and number of vehicles ($N_{V}$) are per (18).

$N_{G}\geq\frac{N_{V}}{S_{G,max}}$ (18)

Unless otherwise stated, we fix the maximum group size $S_{G,max}$ at 20. The
size of each group dynamically changes throughout each simulation as vehicles
move in/out of the range of each BS and RSU. In all simulations, as only
critical high-priority RBs may use RSUs, each 4G macrocell BS only supports
1ms x 180 kHz RB geometry, while each 5G RSU only supports 0.5ms x 360 kHz RB
geometry.

For our simulations, we evaluate the following metrics:

  * •

Average Transfer Time: The duration of time a QoS flow occupies radio
resources.

  * •

Average End-to-End Time: The time from a QoS flow’s first packet generation
until its last packet reception, including preemption time.

  * •

Average Timeout Ratio: The ratio of QoS flows starved of resources per (20),
i.e., the receiver has not received the last packet within seven seconds of
the initial uplink queuing time of the QoS flow.

  * •

Average Queue Time: The time a QoS flow is en-queued in the system, i.e., the
transmission has started, yet it is waiting for resources to complete.

The timeout ratio $T_{q}$ is specified per (20):

$\displaystyle T_{q}=\frac{|F_{q}^{T}|}{|F_{Q}|},\ F_{q}^{T}=\left\\{d\in
F_{Q}\ \begin{tabular}[]{|l}$d_{t}>7s$\end{tabular}\right\\}$ (20)

where the QoS setting is $q$, $F_{q}^{T}$ represents the set of QoS flows
transmitted. $F_{Q}$ represents all QoS flows, and $d_{t}$ is the duration of
a network flow from the initial transmission time (first packet) to the end of
its reception (last packet).

As our resource allocation methodology is QoS-aware, high-priority flows are
allocated resources prior to normal-priority flows, i.e., throughput is not
our primary consideration among QoS classes as throughput is inherently unfair
to QoS flows with lower demand, potentially causing starvation. In this work,
we derive a fair resource allocation approach that ensures each QoS flow is
eventually allowed to transmit its data, thus reducing the likelihood of
starvation. As a result, our mechanism lowers the overall latency by allowing
each QoS flow to transmit.

In all simulations, we compare general (normal-priority QoS flows) and
critical (high-priority QoS flows) network traffic flows. The simulated radio
conditions demonstrate how channel gains, corresponding queuing duration, and
network traffic flows impact the system’s QoS when considering vehicle
location.

Compared to BS-only and socially aware deployments, the strength of QoS
reclassify V2X is that when critical/high-priority traffic is not urgent to
non-local BS users, as is the case where the traffic originates from an RSU
and forwarded to a different locality. When the traffic is forwarded, the BS
reclassifies the traffic as normal-priority traffic, i.e., the BS minimizes
the QoS impact of high-priority traffic on normal-priority traffic. However,
when RCWS traffic is considered urgent to other vehicles (vehicles within the
range of the RSU/BS receiving the high-priority V2X network flow), the QoS
flow’s priority remains unaltered within the originating locality. Using the
original QoS priority ensures timely delivery to the vehicles near the scene
of incidence. If members of the same target group associate with different
BSs, high-priority network traffic within a BS is forwarded to other
macrocells, without QoS reclassification, to support group member
communications.

In our bi-level NOMA system, high-priority connections only use the inner-
layer NOMA, while normal-priority connections use either layer of NOMA.
Network traffic with a target group associated with a BS / RSU is groupcast to
the group members within range of the BS / RSU. Safety-related RCWS social
groups include all vehicles as members, i.e., RCWS traffic broadcasts to all
vehicles. Non-safety groups consider group member conditions, i.e., network
traffic is groupcast. The following sections discuss and analyze broadcast and
groupcast traffic, the system’s queuing state, and the QoS Impact.

(a) Generic QoS Flows (b) Critical QoS Flows

Figure 8: Average Transfer Time

(a) Generic QoS Flows (b) Critical QoS Flows

Figure 9: Average End-to-End Time

(a) Generic QoS Flows (b) Critical QoS Flows

Figure 10: Average Queue Time

(a) Generic QoS Flows (b) Critical QoS Flows

Figure 11: Average Timeout Ratio

In figures 9 $\to$ 11, we show simulations for three different deployments:
NOMA-enabled 4G macrocell BS only V2X (”MC” series data), socially aware 5G
V2X (”SA” series data), and QoS reclassifying 5G V2X (”QR” series data). For
each deployment, we analyze the general and critical QoS flows under both the
optimal resource allocation algorithm (data series includes ”(O)”) and the
approximation resource allocation algorithm (data series includes ”(A)”).

In the QoS reclassify deployment, data that started and ended as critical
high-priority data impacts critical QoS flows. Data that started as critical
and the BS reclassified general impacts both critical QoS flows and general
QoS flows. Finally, data that started as normal QoS only impacts general QoS
flows.

###  VII-A Analysis of fixed group parameters

In this section, we consider a fixed maximum group size to understand better
how the number of vehicles impacts the system’s behavior. We first analyze the
average transfer time. Across all deployments in Fig. 9, as the volume of
high-priority (critical) traffic increases, the normal-priority (general)
traffic is preempted more often. When no RSU is present / in range, the high-
priority traffic preempts uplink and downlinks region-wide BS resources. In
all cases, the approximation algorithm requires at least as much time as the
optimal algorithm. The optimal algorithm simulation acts as a lower bound on
the transfer time. In the socially aware deployment, the benefit of socially
aware V2X is evident via the reduced transfer time due to RSU resource
utilization by critical QoS uplink/downlink. The RSUs provide additional
uplink radio resources explicitly provided for critical network flows. Note:
When an RSU forwards data to a BS, the critical QoS flow transfer times are
aggregated and averaged. The aggregate transfer time = RSU uplink + RSU
downlink (local transmission) + RSU uplink + BS downlink (regional
transmission). In the QoS reclassify deployment, we observe a decrease in
transfer time relative to socially aware as fewer critical flows compete for
resources. Thus, more resources are assignable to general network flows. For
general QoS flows, QoS reclassify V2X shows a lower average transfer time as
critical flows forwarded to the BS are reclassified as general flows, i.e., no
unnecessary preemption occurs. As critical flows remain critical within the
hierarchy they originate from, critical transfer time decreases due to said
flows being QoS reclassified when forwarded. As QoS flows share the spectrum,
fewer critical network flows compete for the same radio resources. In general,
when comparing the 4G BS (only) to the 4G BS + 5G RSU configurations, the
differences in transfer time are primarily due to the RB geometry and the
additional RSU resources. While the approximation algorithm does not achieve
as tight results as the optimal algorithm, the differences are negligible.
Comparing the generic QoS flow series across all three deployments, our
proposed QoS reclassify V2X scheme’s end-to-end time requires $\approx 15\%$
less time than 4G BS (only) and $\approx 10\%$ less time required by a
socially aware 5G V2X deployment.

The average QoS flow end-to-end time is displayed in Fig. 9. Per the 4G BS
(only) deployment, as only a BS is present, critical traffic is serviced
first, while general traffic incurs delay due to preemption. When we consider
QoS reclassifying V2X, we notice both socially aware and QoS reclassify 4G BS
+ 5G RSU schemes outperform 4G BS (only). However, we also notice that
socially aware V2X performs worse than QoS reclassifying V2X when traffic
congestion occurs, starting from 120 vehicles. In socially aware V2X, high-
priority traffic is forwarded from the RSU to the BS. The QoS is unchanged, as
data relevance is not a factor. Consequently, RSU originating high-priority
traffic preempts normal-priority traffic on the BS. Compared to QoS reclassify
V2X, there is a higher end-to-end time for the general QoS flows, as is
evident from 120 vehicles. In the QoS reclassify V2X, the normal-priority QoS
general traffic does not incur any unnecessary delay, i.e., the critical
traffic does not preempt the normal traffic as it is reclassified as normal-
priority QoS. We note that the approximation algorithm performs similarly to
the theoretically optimal results. Comparing the generic QoS flow series
across all three deployments, in terms of transmitting a QoS flow, our
proposed QoS reclassify V2X scheme’s end-to-end time requires only $\approx
15\%$ of the time required by a 4G BS (only) and $\approx 30\%$ of time
required of a socially aware 5G V2X deployment.

We now consider the queuing time graphs, as shown in Fig. 11. In the 4G BS
(only) deployment, we observe that the queuing time is nearly linear and
almost identical for critical and general traffic due to the preemption of
critical traffic. In the socially aware deployment, we observe a lower queuing
time than 4G macrocell BS only as local to the RSU flows do not need to
compete for regional resources. Finally, in our QoS reclassify deployment, we
observe that the queuing time slope is lower than the socially aware
deployments. The lower queueing time is due to general latency QoS flows not
being preempted by inbound inter-BS/RSU traffic.

We now look at the timeout ratio. Fig. 11 shows the QoS flow timeout ratio
graphs. Timeout can occur on a QoS flow if it generates network traffic, yet
the receiver only partially receives the QoS flow within seven seconds from
the QoS flow’s generation. In other words, a timeout occurs if the QoS flow
queuing time plus transmission time exceeds seven seconds. In our setup,
critical high-priority traffic is local to either the RSU or BS of origin. If
a critical QoS flow incurred a timeout, only the critical QoS flow would
reflect this. General traffic was either critical traffic that was duplicated
and reclassified or started as general traffic. If a general QoS flow incurred
a timeout, only the general QoS flow would reflect this.

First, looking into the BS-only deployment, we see that the timeout ratio is
relatively low, less than $\frac{1}{100}^{th}\%$. The implication is that the
bottleneck is in radio access, as the amount of resources allocated for uplink
is identical to that of the downlink. A BS-only deployment alleviates the
timeout ratio, as transmissions need to queue for resource allocation, but
sacrifices the gain obtained by priority groups during downlink resource
allocation as the BS uplink and broadcast receive all RCWS-generated traffic
to the cell. Looking into the socially aware V2X deployment, the BSs receive
QoS flows forwarded by the RSUs. As downlink allocation/demand is no longer
identical to uplink, the resource allocation algorithm allocates downlink
resources to higher-priority groups. As such, socially aware suffers from
higher demand, longer queuing times, and a noticeable timeout ratio. Finally,
we examine the QoS reclassified V2X deployment where high-priority flows are
reclassified as a normal-priority before being forwarded. We note that QoS
reclassification has a lower timeout ratio than socially aware. Also, we
observe that the approximation algorithm performs similarly to the
theoretically optimal algorithm until network congestion occurs around $120$
vehicles, after which the deviation between both methods becomes more
apparent. Relative to socially aware, the lower timeout ratio achieved by the
4G macrocell BS only deployment is due to an increased number of serviced
high-priority QoS flows. Also, we observe that the QoS reclassify timeout
ratio has increased relative to the socially aware due to high-priority flow
reclassification. The increase in normal-priority traffic generates a higher
timeout ratio. The optimal and approximation timeout ratios converge if
critical flows increase. Until network congestion occurs, our approximation
algorithm performs similarly to the optimal algorithm.

###  VII-B QoS Impact Analysis

Ultimately, the queuing state of the system determines the impact on each QoS
flow. When a vehicle or multiple BSs (inter-BS) are queuing for resources due
to preemption or the data arrival rate at the BS exceeding its service rate,
the queuing time increases. The cause ultimately determines the extent of the
queue increase. We note that the consequences of increased queuing time are
longer end-to-end times and a higher probability of a timeout event, i.e., a
higher timeout ratio.

When studying the impact on normal-priority network flows, the shift from
sufficient to insufficient wireless resources becomes evident. Per Fig. 11,
the 4G BS (only) and socially aware deployments are noticeably impacted. When
wireless resources are insufficient, the average queuing time is higher.
Higher queuing time is due to network traffic queuing for resources, i.e.,
higher competition.

When considering socially aware V2X networks and comparing them to 4G V2X, we
find that the addition of 5G RSUs results in a reduction in the average
queuing time. The network traffic maintains the same QoS priority when the
RSU/BS forwards data to other BSs yet has additional uplink resources to
exploit. In socially aware networks, wireless resource competition decreases
due to vehicles directing high-priority traffic to RSUs (when available), and
inter-BS traffic increases due to network flow duplication. Note: BS
originating and RSU-destined traffic maintains its original QoS. Thus, the
average queuing time becomes dominated by the inter-BS queuing delay.

Finally, when considering QoS reclassifying V2X compared to socially aware V2X
networks, we see a further reduction in queuing time as not only are high-
priority network flows being directed to RSUs, but they are also being
reclassified as normal ”non-preempting” priority QoS flows. Thus, lower
wireless resource competition and a lower inter-BS preemption rate result in
an even lower average queuing time.

##  VIII Conclusion

This research investigated how social networks can be combined with MEC
servers and DC to deploy localized QoS for socially aware V2X networks. In our
proposed system, the primary connection is a 4G BS, and the secondary
connection is a 5G RSU. Our system ensures compatibility with existing V2X
deployments. We found that each vehicle’s social network (origin and target
BS/RSU) and traffic type can define the QoS setting for each vehicle’s
associated QoS flows. The benefit of our socially aware deployment is two-
fold: 1) localized QoS is supported by leveraging the underlying social
network between vehicles and their QoS flows, and 2) legacy devices (4G) can
inherently use our system without being aware of its presence.

By reclassifying critical traffic as general traffic, our socially aware DC
system reduces our V2X system’s overall QoS flow end-to-end time, queuing
duration, and timeout ratio without adversely impacting critical traffic’s QoS
per target audience. This results from each QoS flow’s localized QoS priority
differing from its regional QoS priority, i.e., the QoS priority for critical
network traffic remains unaltered within the vicinity of the event yet is
modified when forwarded for regional distribution.

##  IX Future Work

In traditional cellular networks, a network traffic’s QoS priority selection
is configured when a data flow is created. However, static QoS definitions
ultimately lead to inefficient resource utilization. Nevertheless, we can
improve resource allocation and utilization in near real-time via Artificial
Intelligence (AI) on the network (6G/5G and AI). In our future work, we plan
to extend QoS reclassification to network-wide AI-managed QoS reclassification
based on social groups and each network interface’s traffic type, origin,
destination, and capacity.

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