Aerial STAR-RIS Empowered MEC: A DRL Approach for Energy Minimization (2312.08714v1)
Abstract: Multi-access Edge Computing (MEC) addresses computational and battery limitations in devices by allowing them to offload computation tasks. To overcome the difficulties in establishing line-of-sight connections, integrating unmanned aerial vehicles (UAVs) has proven beneficial, offering enhanced data exchange, rapid deployment, and mobility. The utilization of reconfigurable intelligent surfaces (RIS), specifically simultaneously transmitting and reflecting RIS (STAR-RIS) technology, further extends coverage capabilities and introduces flexibility in MEC. This study explores the integration of UAV and STAR-RIS to facilitate communication between IoT devices and an MEC server. The formulated problem aims to minimize energy consumption for IoT devices and aerial STAR-RIS by jointly optimizing task offloading, aerial STAR-RIS trajectory, amplitude and phase shift coefficients, and transmit power. Given the non-convexity of the problem and the dynamic environment, solving it directly within a polynomial time frame is challenging. Therefore, deep reinforcement learning (DRL), particularly proximal policy optimization (PPO), is introduced for its sample efficiency and stability. Simulation results illustrate the effectiveness of the proposed system compared to benchmark schemes in the literature.
- P. Zhan, K. Yu, and A. L. Swindlehurst, “Wireless relay communications with unmanned aerial vehicles: Performance and optimization,” IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no. 3, pp. 2068–2085, Jul. 2011.
- C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah, and C. Yuen, “Reconfigurable intelligent surfaces for energy efficiency in wireless communication,” IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 4157–4170, Jun. 2019.
- X. Mu, Y. Liu, L. Guo, J. Lin, and R. Schober, “Simultaneously transmitting and reflecting STAR RIS aided wireless communications,” IEEE Transactions on Wireless Communications, vol. 21, no. 5, pp. 3083–3098, Oct. 2021.
- Z. Zhai, X. Dai, B. Duo, X. Wang, and X. Yuan, “Energy-efficient uav-mounted ris assisted mobile edge computing,” IEEE Wireless Communications Letters, vol. 11, no. 12, pp. 2507–2511, Sep. 2022.
- Q. Zhang, Y. Wang, H. Li, S. Hou, and Z. Song, “Resource allocation for energy efficient STAR-RIS aided MEC systems,” IEEE Wireless Communications Letters, vol. 12, no. 4, pp. 610–614, Jan. 2023.
- J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017.
- Y. K. Tun, Y. M. Park, N. H. Tran, W. Saad, S. R. Pandey, and C. S. Hong, “Energy-efficient resource management in UAV-assisted mobile edge computing,” IEEE Communications Letters, vol. 25, no. 1, pp. 249–253, Sep. 2020.
- P. S. Aung, L. X. Nguyen, Y. K. Tun, Z. Han, and C. S. Hong, “Deep reinforcement learning based joint spectrum allocation and configuration design for STAR-RIS-assisted V2X communications,” IEEE Internet of Things Journal, Nov. 2023.
- T. Bai, C. Pan, Y. Deng, M. Elkashlan, A. Nallanathan, and L. Hanzo, “Latency minimization for intelligent reflecting surface aided mobile edge computing,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 11, pp. 2666–2682, Jul. 2020.
- B. Liu, C. Liu, and M. Peng, “Resource allocation for energy-efficient MEC in NOMA-enabled massive IoT networks,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 4, pp. 1015–1027, 2020.
- P. S. Aung, Y. M. Park, Y. K. Tun, Z. Han, and C. S. Hong, “Energy-efficient communication networks via multiple aerial reconfigurable intelligent surfaces: DRL and optimization approach,” IEEE Transactions on Vehicular Technology, Oct. 2023.
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