Lightweight Gated Recurrent Unit Variants for Real-Time Channel Prediction

Abstract

Machine-learning-based channel predictors must operate under stringent latency, memory, and computational constraints while remaining robust to noisy and time-varying observations. This paper develops a causal channel-prediction framework based on three single-layer gated recurrent unit variants: an unconstrained lightweight GRU (L-GRU), a stability-aware GRU (SA-GRU) with a spectral bound on the candidate-state recurrent matrix, and a doubly constrained lightweight GRU (DCL-GRU) with additional control of the reset-gate recurrent matrix. A sufficient condition is derived for contraction of the complete candidate-state mapping, while preserving the parameter count and inference-time structure of the baseline architecture. These guarantees apply to the candidate-state mapping and do not directly imply contraction of the complete GRU hidden-state transition. The models are trained on 2x2 MIMO channels generated using the 3GPP CDL-A model, and their hyperparameters are selected through Bayesian optimisation with Optuna's Tree-structured Parzen Estimator. Across the considered SNR range, the constrained variants retain competitive prediction accuracy and achieve optimisation runtimes close to L-GRU, with speedups of 1.72x and 1.76x relative to a five-layer GRU for SA-GRU and DCL-GRU, respectively. All audited constrained runs satisfy the prescribed spectral bounds. Under temporary observation corruption followed by recursive prediction, SA-GRU reduces the mean and peak hidden-state deviations by approximately 15.3% and 13.0%, respectively, relative to L-GRU, whereas L-GRU achieves the lowest rollout NMSE. These results highlight an explicit trade-off between prediction accuracy, empirical rollout robustness, and candidate-state stability guarantees.