Energy-Efficient User Association and Resource Allocation for Decentralized Mutual Learning

Xiao Lu, Jiantao Yuan, Chao Chen, Xianfu Chen, Celimuge Wu, Rui Yin

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


In this paper, a novel decentralized mutual learning (DML) network is designed, where each mobile device can share knowledge with its neighbour devices via bidirectional device-to-device (D2D) communication. We subdivide and discuss mutual learning scenarios, and investigate the user association and resource allocation problems for the one-to-many scenario. With constraints on power, bandwidth and communication latency, we formulate a non-convex optimization problem to minimize the average communication energy consumption for sharing new knowledge. On the basis, a two-layer iterative algorithm is proposed, which consists of an outer layer algorithm based on particle swarm optimisation (PSO) for searching a suitable user association strategy and an inner layer algorithm based on sum-of-ratios optimization for achieving a globally optimal allocation of communication resource. Numerical results are presented to verify the fast convergence and the effectiveness of the proposed algorithm in terms of a trade-off between energy consumption and knowledge sharing efficiency.

Original languageEnglish
Title of host publication2022 IEEE Global Communications Conference, GLOBECOM 2022
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)978-1-6654-3540-6
ISBN (Print)978-1-6654-3541-3
Publication statusPublished - 2023
MoE publication typeA4 Article in a conference publication
EventIEEE Global Communications Conference, GLOBECOM 2022: Accelerating the Digital Transformation through Smart Communications - Hybrid: In-Person and Virtual Conference, Rio de Janeiro, Brazil
Duration: 4 Dec 20228 Dec 2022


ConferenceIEEE Global Communications Conference, GLOBECOM 2022
CityRio de Janeiro


  • D2D communication
  • Decentralized network
  • Mutual learning
  • Resource allocation
  • User association


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