Joint Power Optimization of BS and UE in Wireless Networks

• Published in: 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall) pp. 1 - 5
• Main Authors: Zhang, Dongpo, Tao, Ye, Ding, Lei, Zhu, Lina, Luan, Hao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.10.2023
• Subjects: Age of information; Base stations; Deep learning; Deep Reinforcement Learning(DRL); Power allocation; Reinforcement learning; Simulation; Transforms; Vehicular and wireless technologies; Wireless networks
• ISSN: 2577-2465
• DOI: 10.1109/VTC2023-Fall60731.2023.10333647

The optimization of power control for base station (BS) and user equipment (UE) is crucial to enhance network performance in the dynamic landscape of wireless communication. With the advent of Sixth Generation (6G) communication systems, the demand for real-time communication has surged, leading to a pressing need to develop power optimization strategies that can tackle network latency. We propose a joint power optimization method for both BS and user UE based on the Age of Information (AoI), and address the challenge of power allocation in multi-user communication systems. We begin by modeling a single BS and studying the impact of its power on the AoI. Next, we design a UE power allocation algorithm that considers limited conditions. To achieve this, we transform the problem into a Markov decision problem and solve for the optimal solution using a deep learning algorithm. Our proposed method provides an effective approach to optimize power allocation in multi-user communication systems. The simulation results demonstrate that our proposed algorithm can effectively minimize AoI and achieve on-demand power allocation. Compared to the mean algorithm for power allocation, our proposed algorithm has better performance.