Joint Optimization Packet Scheduling and Energy Harvesting for Energy Conservation in D2D Networks: A Decentralized DRL Approachopen access
- Authors
- Muy, Sengly; Han, Eun-Jeong; Lee, Jung-Ryun
- Issue Date
- 2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Device-to-device communication; Distributed D2D; Energy efficiency; energy efficiency; joint optimization; Linear programming; Long short term memory; multi-agent DRL; Multi-agent systems; Optimization; proportional fair; Resource management; Throughput
- Citation
- IEEE Access, v.12, pp 90971 - 90978
- Pages
- 8
- Journal Title
- IEEE Access
- Volume
- 12
- Start Page
- 90971
- End Page
- 90978
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/75192
- DOI
- 10.1109/ACCESS.2024.3417935
- ISSN
- 2169-3536
2169-3536
- Abstract
- This study investigates the optimization of proportional fair (PF) and energy efficiency in simultaneous wireless information and power transfer (SWIPT)-based device-to-device (D2D) networks considering the residual battery levels of D2D users to increase the network lifetime. We establish an optimization model that determines the subchannel allocation and transmission power levels for D2D users, to maximize an objective function that combines user fairness and energy efficiency. To tackle this problem in a distributed manner, we propose a multi-agent deep reinforcement learning (DRL) model. Given that fairness considerations necessitate information about other agents, we employ the long short-term memory (LSTM) algorithm to estimate the parameters of other D2D pairs within the state space of the multi-agent DRL model. Through simulations, we compare the performance of our proposed algorithm with that of existing iterative algorithms, namely, exhaustive search (ES) and gradient search (GS). The results demonstrate that the proposed multi-agent DRL approach achieves a solution that is nearly globally optimal, while maintaining a lower computational complexity. Furthermore, the proposed algorithm reduces the standard deviation of residual battery levels among D2D pairs and contributes to an increased network lifetime. Authors
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