Reinforcement Learning Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks

Omidkar A, Khalili A, Nguyen HH, Shafiei H (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Internet of Things Institute of Electrical and Electronics Engineers (IEEE)

DOI: 10.1109/JIOT.2022.3151001

Abstract

This paper proposes a novel approach to improve the Energy Efficiency (EE) of an Energy-Harvesting (EH)-enabled IoT network supported by Simultaneous Wireless Information and Power Transfer (SWIPT). More specifically, the Device-to-Device (D2D) users harvest ambient energy throughout their communication with the time switching (TS) technique, while the Internet of Things (IoT) users harvest energy from the Base Station (BS) based on the power splitting (PS) method. We study the EE optimization problem that takes into account transmit power feasibility conditions for D2D users and IoT users, the minimum data rate requirements for D2D and IoT users, joint spectrum sharing block, and time allocation for the D2D links. The underlying problem is highly non-convex mixed integer non-linear problem (MINLP) and the global optimal solution is intractable. To handle it, we decompose the original problem into three subproblems: (i) joint subchannel allocation and power splitting, (ii) power control, and (iii) time allocation. Since it is difficult to find an exact state model approach in a dynamic environment with a large state space, we exploit a Q-learning method based on reinforcement learning (RL) to solve the first sub-problem. To solve the second sub-problem, we apply a conventional convex optimization technique based on the majorization-minimization (MM) approach and Dinkelbach method. Simulation results not only demonstrate the superiority of our proposed algorithm as compared to other methods in the literature but also confirm impressive EE gains through spectrum sharing and harvested energy from D2D and IoT users.

Involved external institutions

University of Saskatchewan CA Canada (CA) Institute for Research in Fundamental Sciences (IPM) / پژوهشگاه دانش‌های بنیادی IR Iran, Islamic Republic of (IR) Khajeh Nasir Toosi University of Technology (KNTU) / دانشگاه صنعتی خواجه نصيرالدين طوسی‎‎ IR Iran, Islamic Republic of (IR)

How to cite

APA:

Omidkar, A., Khalili, A., Nguyen, H.H., & Shafiei, H. (2022). Reinforcement Learning Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks. IEEE Internet of Things. https://dx.doi.org/10.1109/JIOT.2022.3151001

MLA:

Omidkar, Atefeh, et al. "Reinforcement Learning Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks." IEEE Internet of Things (2022).

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