Call muting-unexpected silences during voice calls due to extended RTP packet loss is a major challenge in high-mobility 5G environments, severely degrading Mean Opinion Score (MOS) and user experience. We propose DRLCQ, a Deep Reinforcement Learning-based framework that dynamically tunes Cell Individual Offset (CIO) in real time to reduce mute events and enhance voice quality. Integrated as an xApp within the O-RAN Near-RT RIC, DRLCQ leverages live network KPIs (e.g., SINR, jitter, packet loss) to learn optimal handover decisions. Evaluated against static and heuristic baselines, DRLCQ achieves over 20% fewer call mute incidents and up to 85% higher MOS, demonstrating a scalable and intelligent solution for AI-native RAN control.

https://ieeexplore.ieee.org/document/11432639