#5959. Online policies for throughput maximization of backscatter assisted wireless powered communication via reinforcement learning approaches

July 2026publication date
Proposal available till 19-05-2025
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Journal’s subject area:
Computer Networks and Communications;
Computer Science Applications;
Information Systems;
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Abstract:
In this paper, we consider the design of online policies in a backscatter assisted wireless powered communication system, aiming at maximizing the longterm average throughput. We consider a complete data life cycle, from sampling, compression, transmission to reception and decompression. Practical constraints including finite battery capacity, time-varying uplink channel and nonlinear energy harvesting model are considered. An optimization problem is formulated in a Markov decision process framework to maximize the longterm average throughput by a hybrid of mode switching, time and power allocation, and compression ratio selection. Capitalizing on this, we first adopt value iteration (VI) algorithm to find offline optimal solution as benchmark. Then, we propose Q-learning (QL) and deep Q-learning (DQL) algorithms to obtain online solutions without prior information.
Keywords:
Backscatter communication; Deep Q-learning; Longterm average throughput; Markov decision process; Wireless powered communication

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