Prediction-Aided Adaptive Semantic Communication for IoT Sensor Streams Under Outdated Channel State Information

Adaptive semantic communication can save radio resources by matching the representation rate to channel quality, yet its decisions become unreliable when channel state information reaches the transmitter with delay. This study examines that overlooked failure mode and develops a prediction-aided framework for rate-adaptive transmission of Internet of Things sensor streams. A compact long short-term memory network forecasts a distribution of the receive signal-to-noise ratio (SNR), and a risk-aware controller selects the smallest rate whose expected semantic distortion under that forecast meets a fidelity target. The controller is independent of the semantic codec and accounts for forecast uncertainty rather than relying on a single predicted value. The framework is evaluated on six real sensor streams, over time-correlated fading channels and on SNR logs measured in a commercial 5G/LTE network, with all schemes held to the same average bandwidth and with confidence intervals over independent channel realizations. Adaptation based on stale channel information loses its advantage over fixed-rate transmission as mobility or feedback delay increases, both in simulation and on measured logs. Channel prediction recovers a substantial part of this loss, a single predictor serves the whole mobility range without retraining, and the learned forecast remains reliable when the channel statistics assumed by a model-based predictor are wrong. Prediction also keeps its advantage over stale channel information under drifting mobility, varying channel-estimation quality and frequency-selective multipath, and a predictor trained for one mobility regime can be adapted to another within seconds. A second-order analysis, whose assumptions are verified on the trained codec, explains how forecast uncertainty and the curvature of the codec's distortion response affect the rate decision, including how often uncertainty changes the choice among discrete rates. The resulting framework is lightweight and suitable for practical semantic links operating with delayed feedback.