  Autonomous driving may be the most important application scenario of next
generation, the development of wireless access technologies enabling reliable
and low-latency vehicle communication becomes crucial. To address this, 3GPP
has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio
(NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in
LTE-V2X, allowing direct communication between vehicles. This supplements SL
communication in LTE-V2X and represents the latest advancement in cellular V2X
(C-V2X) with improved performance of NR-V2X. However, in NR-V2X Mode 2,
resource collisions still occur, and thus degrade the age of information (AOI).
Therefore, a interference cancellation method is employed to mitigate this
impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA)
technology. In NR-V2X, when vehicles select smaller resource reservation
interval (RRI), higher-frequency transmissions take ore energy to reduce AoI.
Hence, it is important to jointly consider AoI and communication energy
consumption based on NR-V2X communication. Then, we formulate such an
optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm
to compute the optimal transmission RRI and transmission power for each
transmitting vehicle to reduce the energy consumption of each transmitting
vehicle and the AoI of each receiving vehicle. Extensive simulations have
demonstrated the performance of our proposed algorithm.
