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D2D communication resource allocation algorithm based on multi-agent reinforcement learning

Published: 18 April 2024 Publication History

Abstract

To solve the interference problem of device-to-device (D2D) communication in cellular network, a distributed resource allocation algorithm based on simultaneous wireless information and power transfer technology and dual deep Q-network is proposed to achieve distributed resource allocation and maximize energy efficiency of D2D links. Firstly, the resource allocation problem of D2D communication is formulated as a Markov decision process. Secondly, the allocation problem is decomposed into two sub problems: power control and channel allocation. Then, the reinforcement learning technique is introduced to model the optimization problem as a multi-agent learning optimal strategy problem. Finally, by continuously iterative updating and learning better action strategies, the optimization goals and reasonable allocation of resources are achieved. Experimental results show that the proposed algorithm can effectively improve the energy efficiency of D2D link layer and the throughput of D2D link, and has certain feasibility and effectiveness.

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ICCNS '23: Proceedings of the 2023 13th International Conference on Communication and Network Security
December 2023
363 pages
ISBN:9798400707964
DOI:10.1145/3638782
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 18 April 2024

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Author Tags

  1. Device-to-device communication
  2. Multi-agent reinforcement learning
  3. Power control
  4. Resource allocation
  5. Simultaneous wireless information and power transfer(SWIPT)

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