# System Level Simulation of Scheduling Schemes for C-V2X Mode-3

Luis F. Abanto-Leon2, Arie Koppelaar3, Chetan B. Math4, Sonia Heemstra de
Groot8

<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>2 4 8 Eindhoven University of Technology  3NXP
Semiconductors

###### Abstract

The 3rd Generation Partnership Project (3GPP) introduced Cellular
Vehicle–to–Everything (C-V2X) as a novel technology to support sidelink
vehicular communications. While a distributed scheduling approach has been
proposed by 3GPP for the out–of–coverage scenario, i.e. C-V2X mode-4, there is
no standardized scheme for centralized systems in C-V2X mode-3. In this paper,
we propose two scheduling approaches for C-V2X mode-3. One of them is based on
the minimization of the overall power perceived by the vehicles. The second
approach is based on the maximization of the subchannels re-usage distance.
The two centralized schemes are compared against the distributed approach.
Through simulations we show that the proposed schemes outperform C-V2X mode-4
as the subchannels are assigned in a more efficient manner with mitigated
interference.

###### Index Terms:

subchannel scheduling, vehicular networks, mode-3, sidelink, C-V2X

##  I Introduction

Cellular Vehicle–to–Everything (C-V2X) communications is one of the novel
paradigms introduced by the 3rd Generation Partnership Project (3GPP) [1] in
Release 14. C-V2X communications is envisaged as a dependable technology with
the capability of satisfying stringent latency and reliability requirements in
dynamic scenarios. Two operation modalities have been described within C-V2X,
namely mode-3 and mode-4 [1]. The former one is aimed for centralized systems
and therefore relies on the availability of cellular infrastructure such as
eNodeBs. Thus, eNodeBs may pursue any criteria to allocate subchannels to
vehicles. For instance, [2] describes a setting where maximization of the
system sum-capacity is aspired based on the subchannels
signal–to–interference–plus–noise ratio (SINR) that vehicles report to
eNodeBs. Furthermore, [3] extends the previous work including additional
constraints where differentiated QoS requirements per vehicle are considered.
Once an eNodeB has computed a suitable distribution of subchannels, vehicles
will be notified of the allocation via downlink—upon which direct
vehicle–to–vehicle (V2V) communications will take place over the sidelink.
Contrastingly, mode-4 operates autonomously and distributedly without the
necessity of a central orchestrator. As a consequence, vehicles have to
monitor the power levels on each subchannel and select a suitable one for
their own utilization. A vehicle will self-reserve a subchannel which may be
unoccupied or experiences low interference in order to improve the likelihood
of its own transmitted messages being received reliably. In mode-3 the
sidelink subchannels can be more efficiently utilized due to the huge amount
of knowledge arriving at the eNodeBs from all the vehicles in coverage.
Conversely, a noticeable drawback of mode-4 is the restricted local knowledge
of each vehicle, which may cause the most appropriate subchannels not to be
always selected. Moreover—due to incoordination—several vehicles may compete
over the same subset of subchannels, and therefore leading to severe packet
reception ratio (PRR) degradation. In order to diminish the occurrences of
conflicts, 3GPP standardized a semi-persistent scheduling (SPS) scheme whereby
vehicles can reserve subchannels on a quasi-steady basis until re-scheduling
is required. Thus, under the presumption of short-term invariability, any
receiving vehicle is capable of acquiring a degree of understanding about the
subchannels utilization.

In the absence of centralized scheduling schemes for C-V2X broadcast
communications, we have devised two approaches and compare them against mode-4
proposed by 3GPP [1]. The paper is structured as follows. In Section II, the
two centralized schedulings approaches for mode-3 are described. In Section
III, the 3GPP SPS scheduling scheme for mode-4 communications is briefly
revisited. Section IV is devoted to simulation results. Finally, Section V
summarizes the conclusions of our work.

##  II Scheduling Approaches for Mode-3

In this section, we briefly describe two schedulings approaches that we
propose.

Preliminaries: A 10 MHz intelligent transportation systems (ITS) channel for
exclusive support of sidelink communications has been considered. Thus, the
whole channel is divided into several time-frequency resource
partitions—hereinafter called subchannels. Each has dimensions of one subframe
(1 ms) in time and a number of resource blocks (RBs) in frequency. A
subchannel is assumed to be capable of carrying a cooperative awareness
message (CAM). In this work we have assumed a nominal message rate of
$\Delta_{CAM}=10$ Hz; therefore the maximum amount of time divisions is 100.
In a similar manner as subchannels in mode-4 are reserved on a semi-persistent
basis—in the centralized schemes presented herein—the same assumption prevails
in order to make a fair comparison between the approaches. Thus, subchannels
are utilized by vehicles for $T_{SPS}$ seconds. During this period, a vehicle
will periodically broadcast on the assigned subchannel, and upon termination a
new reservation will be required. In addition, at any time instance there is a
number of vehicles in the system that require re-scheduling. The eNodeB will
be notified of such a requirement and based on either $(i)$ the received power
sensed by the vehicles at every subchannel or $(ii)$ the position and usage of
subchannels, a new allocation of resources will be attempted. Both approaches
presented herein are based on maximum weighted bipartite graph matching.

###  II-A Minimization of Overall Received Power

In this approach the objective is to minimize the overall received power of
the assigned subchannels. Thus, vehicles requiring re-scheduling transmit via
uplink the conditions they experience on the monitored subchannels. Based on
this information, a distribution of resources that minimizes the total
received power will be computed. The intuition behind this approach is that
subchannels with low received power may be more suitable as they might be
unoccupied or experiencing negligible interference. In this case, the vertices
of the graph are either vehicles or subchannels and the edge weights are
represented by the RSSI values of the subchannels.

###  II-B Maximization of Subchannel Re-usage Range

The objective of this approach is to attain maximal re-usage distance for
every subchannel in the system. Intuitively, this will help in mitigating co-
channel interference. In this case, the edge weights are determined by the
minimum distance at which other vehicles are reusing a particular subchannel.

##  III 3GPP-based Scheduling for Mode-4

In the following, we briefly describe the 3GPP mode-4 scheduling scheme. For a
more detailed explanation, the reader is referred to [1] [4]. It consists of
the following stages.

TABLE I: Simulation parameters Description | Symbol | Value | Units
---|---|---|---
Number of RBs per subchannel (per subframe) | - | 30 | -
Number of sub-bands | $F$ | 3 | -
Number of subchannels | - | 300 | -
CAM message rate | $\Delta_{CAM}$ | 10 | Hz
CAM size | $M_{CAM}$ | 190 | bytes
MCS | - | 7 | -
Transmit power per CAM | - | 23 | dBm
SINR threshold | $\gamma_{T}$ | 3.98 | dB
Scheduling period [4] | $T_{SPS}$ | 0.5-1.5 | s
Antenna gain | $G_{t},G_{r}$ | 3 | dB
Shadowing standard deviation | $\mathcal{X}_{\sigma}$ | 7 | dB
Shadowing correlation distance | - | 10 | m

###  III-A Power Sensing

The vehicles continuously monitor the received power on each subchannel except
on those where monitoring was not possible, e.g. due to half-duplex
limitation.

###  III-B Subchannels Categorization

Some subchannels will be excluded from selection based on the average PSSCH-
RSRP. This is an iterative stage where a threshold is gradually increased in
order to admit more subchannels with incremented interference until there are
at least 20% of candidate subchannels for selection.

###  III-C Subchannel Selection

Among the pre-selected candidates in the previous stage, a subchannel is
randomly chosen.

##  IV Simulations

In this section, we compare the standardized 3GPP scheduling method against
the two proposed approaches. We assess a freeway scenario with 600 vehicles
over 40 seconds of simulation. The simulations have been performed at least 5
times with different seeds in order to evaluate deviations. The relevant
parameters for the experiments are shown in Table I.

We can observe in 1 the PRR curves for the mentioned approaches. Furthermore
in shaded colors, we depict the deviations which are originated due to
performance variations when using different seeds. These representations can
be regarded as intervals with 99.999% of confidence. We can observe that the
scheduling scheme based on distance maximization outperforms mode-4 across all
the distances. However, the approach based on minimization of the sum of
received powers can only outperform mode-4 in the near field—which is a
critical region. In the far-field (150 m an beyond) its performance is
comparable to mode-4.

$50$$100$$150$$200$$250$$300$0.50.60.70.80.91.0$D_{x}$ [meters] Packet
Reception Ratio (PRR) Proposed - Min. Sum Powers Proposed - Max. Reusage
Distance 3GPP Mode-4 Figure 1: Packet Reception Ratio (PRR)

##  V Conclusion

We have proposed two centralized scheduling schemes for C-V2X mode-3. We can
conclude that only the distance-based approach performs better than thee
distributed mode across all the distances. For further study, we aim at
combining both RSSI values and distance information to improve the performance
of the centralized approach.

## References

  * [1] ”3GPP TS 36.213; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures; (Release 15) V15.0.0,” December 2017.
  * [2] L. F. Abanto-Leon, A. Koppelaar, and S. M. Heemstra de Groot, ”Graph-based resource allocation with conflict avoidance for V2V broadcast communications”, IEEE PIMRC 2017, October 2017.
  * [3] L. F. Abanto-Leon, A. Koppelaar, and S. M. Heemstra de Groot, ”Subchannel Allocation for Vehicle–to–Vehicle Broadcast Communications in Mode-3,” IEEE WCNC 2018, April 2018.
  * [4] ”3GPP TS 36.331; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC); Protocol specification (Release 15) V15.0.1,” January 2018.