Power Efficient Resource Allocation for ISAC: Combing Lyapunov Optimization and DRL

Radar and wireless communication systems are two important applications of modern electromagnetic theory. However, the serious shortage of spectrum resources in recent years poses a significant challenge for the development of both systems. Integrated sensing and communication (ISAC) has emerged as...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2023 IEEE Globecom Workshops (GC Wkshps) s. 1135 - 1140
Hlavní autoři: Wang, Haodong, Wang, Zifan, Chen, Yawen, Lu, Zhaoming, Wen, Xiangming
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 04.12.2023
Témata:
Computational modeling
Deep Reinforcement Learning
Integrated Sensing And Communication
Lyapunov Optimization
Network Stability
Power demand
Radar detection
Resource Allocation
Simulation
Stability analysis
Wireless communication
Wireless sensor networks
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Radar and wireless communication systems are two important applications of modern electromagnetic theory. However, the serious shortage of spectrum resources in recent years poses a significant challenge for the development of both systems. Integrated sensing and communication (ISAC) has emerged as a promising solution to address this issue. Due to shared infrastructure and spectrum, optimal resource allocation is essential to meet the quality requirements of both sensing and communication, while maximizing the system efficiency. This paper presents a combinatorial online optimization framework based on Lyapunov theory and deep reinforcement learning (DRL) for a typical ISAC system with time-varying wireless channels and random user arrivals. The framework aims to minimize power consumption while ensuring the performance of both communication and radar detection. It first applies Lyapunov optimization to decouple a long-run stochastic mixed-integer nonlinear programming problem into deterministic subproblems in each time frame. Subsequently, it combines model-based optimization with model-free DRL to solve these subproblems. Simulation results demonstrate that the proposed framework can stabilize the data backlog queue within a short computation time and significantly reduce power consumption.
DOI:10.1109/GCWkshps58843.2023.10464593