Liang, Guangming and Hu, Jie and Zhao, Yizhe and Yang, Kun (2024) Intelligent Link Adaptation for Integrated Data and Energy Transfer: An Enhanced DRL Approach for Long-Term Constraints. IEEE Transactions on Communications. p. 1. DOI https://doi.org/10.1109/tcomm.2024.3407204
Liang, Guangming and Hu, Jie and Zhao, Yizhe and Yang, Kun (2024) Intelligent Link Adaptation for Integrated Data and Energy Transfer: An Enhanced DRL Approach for Long-Term Constraints. IEEE Transactions on Communications. p. 1. DOI https://doi.org/10.1109/tcomm.2024.3407204
Liang, Guangming and Hu, Jie and Zhao, Yizhe and Yang, Kun (2024) Intelligent Link Adaptation for Integrated Data and Energy Transfer: An Enhanced DRL Approach for Long-Term Constraints. IEEE Transactions on Communications. p. 1. DOI https://doi.org/10.1109/tcomm.2024.3407204
Abstract
Modulation scheme and power control simultaneously impact the performance of integrated data and energy transfer (IDET). Therefore, some efforts have been invested in deep reinforcement learning (DRL) algorithms to realize adaptive modulation (AM) and adaptive power control (APC), in order to achieve long-term performance improvement. However, the optimal DRL algorithm design for the long-term performance optimization having long-term constraints is still a challenge, while the optimal patterns of IDET-oriented joint AM and APC are not fully understood. This paper aims to maximize the long-term performance of energy harvesting (EH), while satisfying the long-term constraints of spectrum efficiency, bit-error-rate and transmit power, by jointly optimizing the modulation selection and transmit power allocation. Then, a novel DRL algorithm, named constrained parameterized action deep deterministic policy gradient (C-PADDPG), is proposed to find the feasible policy of joint AM and APC for the transformed constraint satisfaction problem. Meanwhile, the optimal policy is searched for via bisection method. Simulation results demonstrate that our solution can achieve significant gain on the long-term EH performance, compared to the traditional genetic algorithm-based solution and other DRL benchmark. Moreover, the communication-efficient and EH-efficient patterns of joint AM and APC generated by the C-PADDPG algorithm are explicitly illustrated and analyzed.
Item Type: | Article |
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Uncontrolled Keywords: | Integrated data and energy transfer (IDET); intelligent link adaptation; joint adaptive modulation and adaptive power control; deep reinforcement learning (DRL); long-term constraints |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 04 Jul 2024 10:15 |
Last Modified: | 04 Jul 2024 10:15 |
URI: | http://repository.essex.ac.uk/id/eprint/38716 |
Available files
Filename: Accepted_Manuscript.pdf