Hybrid AI Models for QoS Estimation in α-η-μ Fading and Co-Channel Interference Environments within 6G Networks

This paper proposes a hybrid AI model for QoS estimation in 6G networks by mitigating the challenges caused due to α-η-μ fading and co-channel interference. These incorporate a variety of machine learning strategies that deliver improved accuracy with respect to forecasting and real-time updating capabilities. We evaluate it using an extensive set of simulations, including heatmaps and time series analysis; we show substantial reductions on system throughput, latency and utilization. This model makes the design highly dynamic to accommodate a wide spectrum of network conditions and thereby optimizes resources utilization in the overall performance. The comparison proves the high performance of hybrid AI model compared to conventional and state-of-the-art techniques, making it a promising resolution for QoS estimation in advanced network environments.