Sum throughput maximization scheme for NOMA-enabled D2D groups using deep reinforcement learning in 5G and beyond networks

Khan, M. A. A., Kaidi, H. M., Ahmad, N. and Ur Rehman, M. (2023) Sum throughput maximization scheme for NOMA-enabled D2D groups using deep reinforcement learning in 5G and beyond networks. IEEE Sensors Journal, 23(13), pp. 15046-15057. (doi: 10.1109/jsen.2023.3276799)

Abstract

Device-to-Device (D2D) communication underlaying cellular network is a capable system for advancing the spectrum’s efficiency. However, in this condition, D2D generates cross-channel and co-channel interference for cellular and other D2D users, which creates an excessive technical challenge for allocating the spectrum. Despite this, massive connectivity is another issue in the 5G and beyond networks that need to be addressed. To overcome this problem, non-orthogonal multiple access (NOMA) is integrated with the D2D groups (DGs). In this paper, our target is to maximize the sum throughput of the overall network while maintaining the signal-to-interference noise ratio (SINR) of the cellular and D2D users. To achieve the target, a discriminated spectrum distribution framework dependent on multi-agent deep reinforcement learning (MADRL), termed a deep deterministic policy gradient (DDPG) is proposed. Here, it shares the global historical states, actions, and policies using the duration of central training. Furthermore, the proximal online policy scheme (POPS) is used to decrease the computation complexity of training. It utilized the clipping substitute technique for the modification and reduction of complexity at the training stage. The simulation results demonstrated that the proposed scheme POPS attains 16.67%, 24.98%, and 59.09% higher performance than the DDPG, Deep Dueling and deep Q-network (DQN).

• Item Type: Articles
• Additional Information: This work was funded/supported by the Ministry of Higher Education under Fundamental Research Grant Scheme (FRGS/1/2021/ICT09/UTM/02/1) and the NICT under ASEAN IVO (http://www.nict.go.jp/en/asean_ivo/index.html) project 2022: Agricultural IoT based on Edge Computing (S.K130000.7656.4X796)
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ur Rehman, Dr Masood
• Authors: Khan, M. A. A., Kaidi, H. M., Ahmad, N., and Ur Rehman, M.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Sensors Journal
• Publisher: IEEE
• ISSN: 1530-437X
• ISSN (Online): 1558-1748
• Published Online: 17 May 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Sensors Journal 23(13):15046 - 15057
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher