Page 49 - ITU Journal Future and evolving technologies Volume 3 (2022), Issue 2 – Towards vehicular networks in the 6G era

ITU Journal on Future and Evolving Technologies, Volume 3 (2022), Issue 2

Hai proposes a movement control algorithm which fails to model the interference behavior of C-V2X
simulates the attractive force and repulsive force in systems. This is because generic Wi-Fi at 2.4 GHz
nature, so that each robot only needs to follow the only has three non-overlapping channels and so it is
synthetic virtual force to move. The paper finds a hard to allocate resources in terms of frequency. All
way for each robot to control its own movement transmitting nodes are regarded as co-channel
distributedly. However, it assumes a simplified link interferers to others in general. On the contrary, in
model where the reception rate of a packet is purely C-V2X systems, resources are divided in Resource
defined by the distance, without considering the Blocks (RBs) frequency and time in the OFMDA
concurrent interference in a multi-hop network. structure. Nodes may transmit at the same time but
using different ‘resource blocks’ in the frequency
Some studies use machine learning to predict and
adjust vehicle trajectory more accurately, and give domain. In this case, concurrent transmissions are
the optimal solution of vehicle trajectory not necessarily interfering with each other. The
adjustment. With the help of reinforcement learning, physical layer performance of several Vehicle to
the study in [13] realizes the preliminary Vehicle (V2V) communication technologies is
deployment and trajectory optimization technology evaluated and compared [18-20]. However, there is
of stable communication between train and no research work on realistic link modeling of C-
multiple UAVs under UAV energy constraints. V2X to assist the self-optimization of vehicular
Support vector machine is also used for optimal networks.
initial deployment according to the maximum UAV In this paper, we formulate the optimization
communication distance data of train speed and problem of packet reception rate maximization for
UAV energy. However, this research is mainly to the road safety scenarios using a C-V2X sidelink
provide a 5G-based VR / AR experience for mode 4 abstraction and regression results from a C-
passengers on the high-speed trains, without V2X network-level simulation. Under the
considering road safety where the packet reception optimization framework, we devise a controlled
rate is the true objective to guarantee Ultra-Reliable mobility algorithm for transmission nodes to
and Low-Latency Communications (URLLC). In adaptively adjust its position to maximize the
order to reduce the collision probability between aggregated PRR and utility gain using one-hop
vehicles and Vulnerable Road Users (VURs), information only. The contributions in this paper is
consider using the mobile phone position of VURs, a summarized in Table 1.
new vehicle service based on a regression algorithm Table 1 – Contributions compared to the existing works
is proposed, which uniquely uses Cartesian
coordinates to predict the trajectory of vehicles and Mobility or
VURs [14]. The above two methods do not consider Research Network C-V2X link position
the use of C-V2X in vehicular networks. In terms of work Optimization model used control
controlling nodes’ mobility in C-V2X newtorks, [15]
regards the UAV as a mobile Roadside Unit (RSU) [7-11,   
and proposes an algorithm to optimize the position 16-17]
and height of the UAV, so as to achieve good
visibility of the current position of the target vehicle. [12-14]   
However, this is based on the IEEE 802.11p wireless
interface between vehicles. [15]   
In order to acquire specific link performance in C- [4-6,   
V2X networks, it is required to set up a physical 18-20]
layer abstraction model to identify the co-channel
interference. Co-channel interference comes from This paper   
all other concurrent transmissions that physically
use the same frequency and time resource as the
link under consideration. However, previous work 3. SYSTEM SETTING
made assumption that all the concurrent As shown in Fig. 1, we consider a square Region of
transmissions are interfering sources, regardless of Interest (RoI) consisting of 4x4 two-way roads. N
whether they are transmitting on the same vehicles are dropped on the roads and move at a
frequency subchannels or not [16-17]. This random speed. In the road model, the relative
assumption works well for Wi-Fi based systems but distance , and relative velocity , between

© International Telecommunication Union, 2022 37