Conformal Prediction and Risk-Based Optimization of Service Continuity in 6G Edge Networks

We study the problem of service continuity in 6G networks and propose a solution aligned with current mobile network standards that integrates conformal prediction and risk-aware throughput estimation to anticipate short-term degradations and, in addition, to optimize application and session continuity during network topology changes. Our pipeline combines throughput prediction over short horizons with calibration based on split conformal prediction to produce sharp intervals with coverage guarantees, and a throughput degradations estimator that provides the probability that throughput falls below the SLA threshold on each link. A risk-aware optimizer then selects associations that balance expected throughput, reliability, and continuity. Experiments on emulated mobile network traces demonstrate that the performance improves by incorporating uncertainty and calibrated risk estimation. Experiments on emulated mobile network traces demonstrate that the degradation risk model is well calibrated with a Brier score of 0.095, an Expected Calibration Error of 0.068, and a PR–AUC of 0.810, and that incorporating uncertainty and calibrated risk improves control decisions. The risk-aware controller consistently attains higher expected throughput than the commonly used point-estimate baseline at matched reassociation rates, and it can identify network operating regimes with low SLA degradation, e.g., 7.9% violations at 999 Mb expected throughput for ε = 0.40.