Dynamic Power Allocation for Integrated Sensing and Communication-Enabled Vehicular Networks

• Published in: IEEE transactions on wireless communications Vol. 23; no. 9; pp. 12313 - 12330
• Main Authors: Yang, Heng, Wang, Lin, Feng, Zhiyong, Wei, Zhiqing, Peng, Jinlin, Yuan, Xin, Quek, Tony Q. S., Zhang, Ping
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3GPP; Communication; Genetic algorithms; Heuristic methods; Integrated sensing and communication; Interference; Lyapunov optimization; network stability; Onboard equipment; Optimization techniques; Particle swarm optimization; power allocation; Quality of service; Resource management; Stochastic programming; Transportation; Transportation applications; Transportation networks; Vehicle-to-infrastructure; Vehicles; Vehicular networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2024.3391354

To realize higher-level autonomous driving and advanced transportation applications, the introduction of the integrated sensing and communication (ISAC) technique in vehicular networks is indispensable. Different from the existing works, this paper investigates the power allocation problem for onboard ISAC systems of vehicles, during the vehicle-to-infrastructure communication, vehicle-to-vehicle communication and sensing progress, in case of the time-varying communication channel gains, the time-varying impulse responses of sensed targets, and the stochastic traffic. Note that both the inter-beam interference of a single vehicle and the inter-vehicle interference are important considerations. Specifically, we formulate a stochastic programming problem, which optimizes the sensing performance, subject to constraints on the network stability, power limits and quality-of-service requirements. Leveraging the Lyapunov optimization technique, this stochastic programming problem is transformed into a single-time slot non-convex problem. Taking advantages of genetic algorithm and particle swarm optimization (PSO), a hybrid meta-heuristic algorithm is designed to solve the non-convex problem. Typically, we improve the traditional PSO to balance the global search ability and local search ability of particles. Finally, a dynamic power allocation strategy is proposed. The theoretical analysis and simulation results show that this strategy achieves a communication performance-sensing performance tradeoff of [<inline-formula> <tex-math notation="LaTeX"> {\mathrm {O(}}1/V{\mathrm {)}} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX"> {\mathrm {O(}}V{\mathrm {)}} </tex-math></inline-formula>] with <inline-formula> <tex-math notation="LaTeX"> V </tex-math></inline-formula> being a control parameter.