International Journal of Science, Engineering and TechnologyAuthors: Dr. S. Vijayakumar, C. Prabhu
Abstract: The coexistence of 5G New Radio (NR) and Wi-Fi 6 in unlicensed spectrum presents significant challenges due to contention-based medium access, cross-technology interference, and heterogeneous quality-of-service (QoS) requirements. Conventional call admission control (CAC) mechanisms reduce congestion and blocking probability, but their reliance on static admission thresholds limits adaptability under dynamic traffic and channel conditions. This paper proposes a reinforcement learning (RL) enhanced hybrid CAC integrated with a collision-resolution listen-before-talk (CR-LBT) mechanism to enable intelligent and adaptive spectrum sharing between 5G NR-U and Wi-Fi 6 systems. The RL agent dynamically tunes admission thresholds based on observed network state parameters, including offered load, signal-to-noise ratio (SNR), and service demand, while the CR-LBT mechanism mitigates contention-induced collisions during channel access. A system-level MATLAB simulation model evaluates the proposed framework under varying traffic loads. Simulation results demonstrate that the proposed scheme achieves 20–25% higher throughput and approximately 15% increased system capacity compared with conventional CAC, while significantly reducing bit error rate (BER). In addition, the framework maintains ultra-low end-to-end latency below 1 ms and near-zero blocking probability for delay-sensitive services such as VoIP and real-time video. These results confirm that the proposed approach provides a scalable and intelligent solution for next-generation heterogeneous wireless networks operating in unlicensed spectrum.
DOI: https://doi.org/10.5281/zenodo.19913041
