论文标题
基于长期期限通道分解，通过元学习的线性过滤器预测多端频率选择通道
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition
论文作者
论文摘要
多数Antenna频率选择通道的有效数据驱动的预测策略必须基于少数试验符号进行操作。本文提出了新的通道预测算法，该算法通过将转移和元学习与信道的降低参数化来解决此目标。提出的方法通过利用先前帧的数据来优化线性预测指标，这些数据通常以不同的传播特性为特征，以便在当前帧的时槽上快速训练。拟议的预测因素依赖于线性预测模型的新型长期分解（LSTD），该模型利用该通道将通道分解为长期时空特征和褪色振幅。我们首先基于转移/元学习的二次正则化开发单恒定频率 - 平板通道的预测因子。然后，我们为基于LSTD的预测模型引入了转移和元学习算法，该算法建立在平衡传播（EP）和交流最小二乘（ALS）的基础上。 3GPP 5G标准通道模型下的数值结果证明了转移和元学习对减少通道预测的飞行员数量的影响以及所提出的LSTD参数化的优点。
An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel prediction algorithms that address this goal by integrating transfer and meta-learning with a reduced-rank parametrization of the channel. The proposed methods optimize linear predictors by utilizing data from previous frames, which are generally characterized by distinct propagation characteristics, in order to enable fast training on the time slots of the current frame. The proposed predictors rely on a novel long-short-term decomposition (LSTD) of the linear prediction model that leverages the disaggregation of the channel into long-term space-time signatures and fading amplitudes. We first develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Then, we introduce transfer and meta-learning algorithms for LSTD-based prediction models that build on equilibrium propagation (EP) and alternating least squares (ALS). Numerical results under the 3GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization.
