Hybrid Offline-online Resource Allocation Mechanism for D2D-NOMA Systems

TANG Rui; HE Zuhan; ZHANG Ruizhi; YUE Shibo; PANG Chuanlin; HE Jinpu

doi:10.13976/j.cnki.xk.2023.2307

Volume 52 Issue 5

Oct. 2023

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INFORMATION AND CONTROL > 2023 > 52(5): 574-587. > DOI: 10.13976/j.cnki.xk.2023.2307 CSTR: 32166.14.xk.2023.2307

TANG Rui, HE Zuhan, ZHANG Ruizhi, YUE Shibo, PANG Chuanlin, HE Jinpu. Hybrid Offline-online Resource Allocation Mechanism for D2D-NOMA Systems[J]. INFORMATION AND CONTROL, 2023, 52(5): 574-587. DOI: 10.13976/j.cnki.xk.2023.2307

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Hybrid Offline-online Resource Allocation Mechanism for D2D-NOMA Systems

1.
School of Electronic Information Engineering, China West Normal University, Nanchong 637002, China
2.
College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu 610059, China
3.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

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Received Date: July 06, 2022
Revised Date: November 15, 2022
Accepted Date: November 14, 2022
Available Online: October 22, 2023

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Abstract

Abstract

A device-to-device (D2D) communication-empowered nonorthogonal multiple access system is associated with complex co-channel interference. In this study, we optimize both mode selection and power control in order to maximize the sum proportional bit rate to balance spectral efficiency and user fairness. Accordingly, we propose a hybrid offline-online mechanism to cope with the original mixed-integer non-convex optimization problem. In offline training, variable transformation is used to equivalently transform the power control subproblem into a convex optimization problem. Its global optimum can be readily obtained in milliseconds by using the sophisticated convex optimization toolbox. According to the obtained optimization results, the deep Q-learning algorithm is then applied to build up the mapping relationship from the mode selection scheme and channel state information to the optimal mode adjustment policy. The trained resource allocation mechanism is suitable for online implementation as it involves only simple algebraic operations and a single convex optimization problem. The simulation results show that the proposed mechanism strikes a good balance between performance and operation time. Particularly, it cuts down the average operation time by 94.54% while suffering approximately 10% performance loss compared with the global optimum obtained by the exhausting search.
- device-to-device communication,
- non-orthogonal multiple access,
- deep reinforcement learning,
- mode selection,
- power control,
- convex optimization

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References

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