Sarmad K. Ibrahim1,*, Saif A. Abdulhussien2, Hazim M. ALkargole1, Hassan H. Qasim1
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5223-5238, 2025, DOI:10.32604/cmc.2025.066548
- 30 July 2025
Abstract The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and Massive Machine Type Communication (mMTC)—present tremendous challenges to conventional methods of bandwidth allocation. A new deep reinforcement learning-based (DRL-based) bandwidth allocation system for real-time, dynamic management of 5G radio access networks is proposed in this paper. Unlike rule-based and static strategies, the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput, fairness, and compliance with QoS requirements. By using… More >
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