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

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...

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Bibliographic Details
Published in:GLOBECOM 2022 - 2022 IEEE Global Communications Conference
Main Authors: Lu, Xiao, Yuan, Jiantao, Chen, Chao, Chen, Xianfu, Wu, Celimuge, Yin, Rui
Format: Article in Journal/Newspaper
Language:English
Published: IEEE Institute of Electrical and Electronic Engineers 2023
Subjects:
D2D communication
Decentralized network
Mutual learning
Resource allocation
User association
DML
Online Access:https://cris.vtt.fi/en/publications/109bd0ed-4c8c-4bb0-bc55-a64472936416
https://doi.org/10.1109/GLOBECOM48099.2022.10001627
http://www.scopus.com/inward/record.url?scp=85146928689&partnerID=8YFLogxK
Description
Summary: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.

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