NDLI: Weighted cooperative reinforcement learning-based energy-efficient autonomous resource selection strategy for underlay D2D communication

Content Provider	IET Digital Library
Author	Sharma, Sandeepika Singh, Brahmjit
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.
Starting Page	2078
Ending Page	2087
Page Count	10
ISSN	17518628
Volume Number	13
e-ISSN	17518636
Issue Number	Issue 14, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/13/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2018.6028
Journal	IET Communications
Publisher Date	2019-05-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	5G Cellular Networks 5G Mobile Communication Average D2D User Throughput Cellular Radio Channel Allocation Co-channel Interference Management Cochannel Interference Communication Computing Cooperative Communication Data Rate Device-to-device Communication Electromagnetic Compatibility Energy Conservation Energy Consumption Fairness Value High Spectral Energy Efficiency Independent Learning Interference Knowledge Engineering Technique Learning Agent Learning in AI Mobile Radio System Multi-agent System Multiagent Reinforcement Learning-based Autonomous Channel Selection Scheme Optimal Channel Allocation Resource Allocation Resource Pool Telecommunication Computing Telecommunication Power Management Ultra-low Latency Underlay D2D Communication WCopQL-RS Weighted Cooperative Q-Learning Weighted Cooperative Reinforcement Learning-based Energy-efficient Autonomous Resource Selection Strategy
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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