Weighted cooperative reinforcement learning‐based energy‐efficient autonomous resource selection strategy for underlay D2D communication. Issue 14 (1st August 2019)

• Record Type: Journal Article
• Authors: Sharma, Sandeepika; Singh, Brahmjit
• Is Part Of: IET communications. Volume 13:Issue 14(2019)
• Page Start: 2078
• Page End: 2087
• Publication Date: 2019-08-01
• Subjects: multi‐agent systems -- channel allocation -- resource allocation -- learning (artificial intelligence) -- cellular radio -- 5G mobile communication -- telecommunication computing -- cooperative communication -- energy conservation -- telecommunication power management -- cochannel interference; 5G cellular networks -- optimal channel allocation -- multiagent reinforcement learning‐based autonomous channel selection scheme -- WCopQL‐RS -- learning agent -- independent learning -- average D2D user throughput -- energy consumption -- fairness value -- underlay D2D communication -- device‐to‐device communication -- high spectral energy efficiency -- ultra‐low latency -- resource pool -- data rate -- weighted cooperative reinforcement learning‐based energy‐efficient autonomous resource selection strategy -- weighted cooperative Q‐Learning -- co‐channel interference management; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2018.6028
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : Underlay Device‐to‐Device (D2D) communication is a key technology responsible for high data rate, ultra‐low latency with high spectral and energy efficiency in 5G cellular networks. But to achieve its full potential, optimal channel allocation and effective co‐channel interference management must be accomplished. To address this challenge, we propose a multi‐agent reinforcement learning based autonomous channel selection scheme for D2D communication. The proposed scheme, Weighted Cooperative Q ‐Learning based Resource Selection (WCopQLRS), allows a D2D pair to learn to select a channel from the available resources autonomously. Learning process of each D2D transmitter involves cooperation from neighboring D2D agents by exchanging their latest Q ‐values. An additional parameter called cooperation range is used to determine the neighboring pairs whose Q ‐values can be used for learning the optimal policy. The limited prior information prevents a linear increase in the dimensions of Q ‐value matrix of each learning agent when the number of D2D pairs within the cell is huge. Though WCopQL‐RS involves additional information exchange among agents as compared to independent learning but also provides improved system throughput and convergence speed. It is shown through simulation results that WCopQL‐RS outperforms other existing schemes in terms of average D2D user throughput, energy consumption and fairness value.