用于实时信道预测的轻量级门控循环单元变体
Lightweight Gated Recurrent Unit Variants for Real-Time Channel Prediction
AI总结：
研究基于机器学习的实时信道预测，开发了含三种单层门控循环单元变体的因果信道预测框架，推导相关条件，经训练和优化，受约束变体有竞争力，不同模型在精度、鲁棒性和稳定性间有明确权衡。
AI中文摘要：
基于机器学习的信道预测器必须在严格的延迟、内存和计算约束下运行，同时对噪声和时变观测保持鲁棒性。本文基于三种单层门控循环单元变体开发了一种因果信道预测框架：无约束轻量级门控循环单元（L-GRU）、候选状态循环矩阵具有谱界的稳定性感知门控循环单元（SA-GRU）和对重置门循环矩阵有额外控制的双重约束轻量级门控循环单元（DCL-GRU）。推导了完整候选状态映射收缩的充分条件，同时保留了基线架构的参数数量和推理时间结构。这些保证适用于候选状态映射，并不直接意味着完整门控循环单元隐藏状态转换的收缩。模型在使用3GPP CDL-A模型生成的2x2 MIMO信道上进行训练，其超参数通过Optuna的树状结构帕曾估计器进行贝叶斯优化。在所考虑的信噪比范围内，受约束的变体保持有竞争力的预测精度，并实现接近L-GRU的优化运行时间，SA-GRU和DCL-GRU相对于五层门控循环单元的加速比分别为1.72倍和1.76倍。所有审核的受约束运行都满足规定的谱界。在临时观测损坏后进行递归预测时，SA-GRU相对于L-GRU分别将平均和峰值隐藏状态偏差降低了约15.3%和13.0%，而L-GRU实现了最低的展开归一化均方误差。这些结果突出了预测精度、经验展开鲁棒性和候选状态稳定性保证之间的明确权衡。
英文摘要：
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.
