Meta-Learning-Driven QoS Estimation for Self-Optimizing 6G Networks in Stochastic α-η-µ Fading Channels with Multi-Layer Selection Combining and Cross-Layer Interference Adaptation

The authors proposed to use a meta-learningbased approach for QoS optimization in future 6G networks that are self-optimizing sections, focusing on stochastic α − η − µ fading channels. The proposed model utilizes information learned in the past so that it can rapidly adapt to these changes as they arrive, providing much better area adaptation, energy efficiency and interference mitigation than traditional learning approaches. You push the best and QoS, network throughput, latency abased simulation results are provided under high mobility in addition to severe fading conditions. The metalearning model showed accelerated convergence, adaptive resource allocation and improved interference mitigation which naturally led to QoS improvement up to 80% as well energy efficiency increased up to 65−90%, network reliability were enhanced. This step helps build green and robust communication mechanisms in the next-generation networks.