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

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

doi:10.1109/GLOBECOM48099.2022.10001627

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

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

BA6401 Connectivity solutions

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

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 language	English
Title of host publication	2022 IEEE Global Communications Conference, GLOBECOM 2022
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	867-872
ISBN (Electronic)	978-1-6654-3540-6
ISBN (Print)	978-1-6654-3541-3
DOIs	https://doi.org/10.1109/GLOBECOM48099.2022.10001627
Publication status	Published - 2023
MoE publication type	A4 Article in a conference publication
Event	IEEE 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 2022 → 8 Dec 2022

Conference

Conference	IEEE Global Communications Conference, GLOBECOM 2022
Country/Territory	Brazil
City	Rio de Janeiro
Period	4/12/22 → 8/12/22

Funding

This work was supported in part by the Zhejiang province commonweal projects(Grant No. LGG22F010008), in part by ROIS NII Open Collaborative Research 22S0601, in part by JSPS KAKENHI grant numbers 20H00592, and 21H03424, in part by the Zhejiang Lab Open Program under Grant 2021LC0AB06.

Keywords

D2D communication
Decentralized network
Mutual learning
Resource allocation
User association

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/GLOBECOM48099.2022.10001627

Cite this

@inproceedings{109bd0ed4c8c4bb0bc55a64472936416,

title = "Energy-Efficient User Association and Resource Allocation for Decentralized Mutual Learning",

abstract = "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.",

keywords = "D2D communication, Decentralized network, Mutual learning, Resource allocation, User association",

author = "Xiao Lu and Jiantao Yuan and Chao Chen and Xianfu Chen and Celimuge Wu and Rui Yin",

note = "Funding Information: This work was supported in part by the Zhejiang province commonweal projects(Grant No. LGG22F010008), in part by ROIS NII Open Collaborative Research 22S0601, in part by JSPS KAKENHI grant numbers 20H00592, and 21H03424, in part by the Zhejiang Lab Open Program under Grant 2021LC0AB06. Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE Global Communications Conference, GLOBECOM 2022 : Accelerating the Digital Transformation through Smart Communications ; Conference date: 04-12-2022 Through 08-12-2022",

year = "2023",

doi = "10.1109/GLOBECOM48099.2022.10001627",

language = "English",

isbn = "978-1-6654-3541-3",

pages = "867--872",

booktitle = "2022 IEEE Global Communications Conference, GLOBECOM 2022",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

}

Lu, X, Yuan, J, Chen, C, Chen, X, Wu, C & Yin, R 2023, Energy-Efficient User Association and Resource Allocation for Decentralized Mutual Learning. in 2022 IEEE Global Communications Conference, GLOBECOM 2022. IEEE Institute of Electrical and Electronic Engineers, pp. 867-872, IEEE Global Communications Conference, GLOBECOM 2022, Rio de Janeiro, Brazil, 4/12/22. https://doi.org/10.1109/GLOBECOM48099.2022.10001627

Energy-Efficient User Association and Resource Allocation for Decentralized Mutual Learning. / Lu, Xiao; Yuan, Jiantao; Chen, Chao et al.
2022 IEEE Global Communications Conference, GLOBECOM 2022. IEEE Institute of Electrical and Electronic Engineers, 2023. p. 867-872.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Energy-Efficient User Association and Resource Allocation for Decentralized Mutual Learning

AU - Lu, Xiao

AU - Yuan, Jiantao

AU - Chen, Chao

AU - Chen, Xianfu

AU - Wu, Celimuge

AU - Yin, Rui

N1 - Funding Information: This work was supported in part by the Zhejiang province commonweal projects(Grant No. LGG22F010008), in part by ROIS NII Open Collaborative Research 22S0601, in part by JSPS KAKENHI grant numbers 20H00592, and 21H03424, in part by the Zhejiang Lab Open Program under Grant 2021LC0AB06. Publisher Copyright: © 2022 IEEE.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - D2D communication

KW - Decentralized network

KW - Mutual learning

KW - Resource allocation

KW - User association

UR - https://www.scopus.com/pages/publications/85146928689

U2 - 10.1109/GLOBECOM48099.2022.10001627

DO - 10.1109/GLOBECOM48099.2022.10001627

M3 - Conference article in proceedings

AN - SCOPUS:85146928689

SN - 978-1-6654-3541-3

SP - 867

EP - 872

BT - 2022 IEEE Global Communications Conference, GLOBECOM 2022

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - IEEE Global Communications Conference, GLOBECOM 2022

Y2 - 4 December 2022 through 8 December 2022

ER -

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

Abstract

Conference

Funding

Keywords

UN SDGs

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