Journal
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
Volume 5, Issue 4, Pages 1140-1154Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCCN.2019.2949589
Keywords
Artificial neural networks; distributed algorithms; energy harvesting; learning (artificial intelligence); multi-agent systems; multiple access
Categories
Funding
- ERC-PoC CacheMire project [727682]
- H2020 MSCA IF BESMART [749336]
- European Research Council (ERC) [727682] Funding Source: European Research Council (ERC)
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In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain online power control policies for a large energy harvesting (EH) multiple access channel, when only causal information about the EH process and wireless channel is available. In the proposed framework, we model the online power control problem as a discrete-time mean-field game (MFG), and analytically show that the MFG has a unique stationary solution. Next, we leverage the fictitious play property of the mean-field games, and the deep reinforcement learning technique to learn the stationary solution of the game, in a completely distributed fashion. We analytically show that the proposed procedure converges to the unique stationary solution of the MFG. This, in turn, ensures that the optimal policies can be learned in a completely distributed fashion. In order to benchmark the performance of the distributed policies, we also develop a deep neural network (DNN) based centralized as well as distributed online power control schemes. Our simulation results show the efficacy of the proposed power control policies. In particular, the DNN based centralized power control policies provide a very good performance for large EH networks for which the design of optimal policies is intractable using the conventional methods such as Markov decision processes. Further, performance of both the distributed policies is close to the throughput achieved by the centralized policies.
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