Citation

Dipa, Mahfida Amjad and Yakoob, Syamsuri and Rasid, Fadlee and Ahmad, Faisul Arif and Mahmud, Azwan (2025) Deep Reinforcement Learning Based Load Balancing Scheme in Dense Cellular Network Using RoF Technology. Journal of Communications Software and Systems, 21 (3). pp. 317-326. ISSN 18456421

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Abstract

In a dense cellular network, the small cell size and limited frequency make it hard to control the traffic, and hence, there is a necessity for the transmission points to know how much traffic they can handle. To fix this problem in the network, this study suggests a Load Balancing (LB) scheme based on Reinforcement Learning (RL) named DRL-LB adopting a Deep Deterministic Policy Gradient (DDPG) RL approach for a dense cellular network utilizing the RoF technologies. The DRL-LB technique is based on self-exploration in the continuous action space to speed up the execution process. The SNR of the dense network has been taken into account to increase the network spectral efficiency concerning the number of users. The number of users per base station satisfying the minimum SNR value acts as the LB constraints in the scheme. The result analysis shows that it can achieve the required 10 dB of SNR value with 1.6 bits/s/Hz spectral efficiency. It attains a higher spectral efficiency and rewards around 78% compared to the non-LB approach in the scheme. Furthermore, the simulation process also depicts that DRL-LB is 73% more efficient in running time.

Item Type:	Article
Uncontrolled Keywords:	reinforcement learning, deep deterministic policy gradient, DDPG, load balancing, radio over fiber, RoF, dense network
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	27 Aug 2025 03:40
Last Modified:	27 Aug 2025 03:40
URII:	http://shdl.mmu.edu.my/id/eprint/14429

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