Handover-Aware Conformal Throughput Intervals for Risk-Sensitive Rate Selection in Mobile 5G

Abstract

Mobile applications that must commit to a data rate—video streaming, guaranteed-rate admission, uplink scheduling—can stall or breach a service-level agreement whenever they promise more than the radio link can deliver. What protects them is not an accurate average-throughput forecast but a trustworthy lower bound on the rate that will actually be available. Conformal prediction (CP) can produce such bounds without distributional assumptions, but mobile 5G throughput is piecewise non-stationary: it jumps at cell handovers and radio-access-technology (RAT) transitions, exactly where a rate decision is most fragile. A natural fix is to condition the CP calibration on the mobility-regime state (RAT and time since the last handover). Using 188,711 s of real commercial 5G traces, we show that whether this conditioning helps is task-dependent. It helps two-sided prediction intervals, whose error scale varies by an order of magnitude across RATs, and it removes the post-handover under-coverage of online adaptive CP at no width cost. But for the one-sided lower bound that actually governs rate selection, a single global CP bound is better: it delivers higher link utilization at the same stall budget, and this advantage is robust to the data’s zero-inflation. The practical lesson for rate-control designers is to match the conditioning to the tail the decision uses, and to judge uncertainty methods by the operational outcome rather than by coverage alone. All results are reproducible from a released pipeline.