Channel prediction is a vital technique that can be used to support adaptive transmissions and mitigate the feedback delay of the channel state information (CSI) in the wireless communications, especially in the frequency division duplex (FDD) system. In this paper, we focus on the channel prediction issue in multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems. First, we introduce the general channel prediction framework based on the temporal and spatial correlations in MIMO-OFDM systems. Then, we combine the broad learning idea and recurrent computation, and further introduce the broad echo state network (BESN) for channel prediction in MIMO-OFDM systems. Third, to estimate the output weight matrix, we offer two versions of the BESN, i.e., the basic BESN (B-BESN) and the group forward variable selection (GFVS)-based BESN (GFVS-BESN). In the latter, we develop the GFVS strategy to further extract useful information from those collected features in the BESN and make the BESN more able to process the CSI samples. Fourth, inspired by deep learning, we import the conjugate gradient descent backpropagation (CGDBP) technique to fine-tune the random weights and biases in the BESN and give the related derivations. Then, we prove the echo state property in the BESN and analyze the computational complexity. In the simulation section, we comprehensively evaluate the prediction performances for the standard Extended Vehicular A model (EVA) and Extended Typical Urban model (ETU) under different signal-to-noise ratios (SNRs), different antenna configurations, different spatial correlations, different maximum Doppler shifts and different channel prediction methods. The simulation results indicate that the BESN has excellent prediction performance in MIMO-OFDM systems.

中文翻译：

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用于 MIMO-OFDM 系统中信道预测的宽回波状态网络


信道预测是一种重要的技术，可用于支持自适应传输并减轻无线通信中信道状态信息（CSI）的反馈延迟，特别是在频分双工（FDD）系统中。在本文中，我们重点研究多输入多输出正交频分复用（MIMO-OFDM）系统中的信道预测问题。首先，我们介绍了 MIMO-OFDM 系统中基于时间和空间相关性的通用信道预测框架。然后，我们结合广泛的学习思想和循环计算，进一步介绍了用于MIMO-OFDM系统中信道预测的广泛回波状态网络（BESN）。第三，为了估计输出权重矩阵，我们提供了两个版本的BESN，即基本BESN（B-BESN）和基于组前向变量选择（GFVS）的BESN（GFVS-BESN）。在后者中，我们开发了 GFVS 策略，以进一步从 BESN 中收集的特征中提取有用信息，并使 BESN 更有能力处理 CSI 样本。第四，受深度学习的启发，我们引入共轭梯度下降反向传播（CGDBP）技术来微调BESN中的随机权重和偏差，并给出相关的推导。然后，我们证明了BESN中的回波状态属性并分析了计算复杂度。在仿真部分，我们综合评估了标准扩展车辆A模型（EVA）和扩展典型城市模型（ETU）在不同信噪比（SNR）、不同天线配置、不同空间相关性、不同最大多普勒频移和不同的信道预测方法。 仿真结果表明BESN在MIMO-OFDM系统中具有优异的预测性能。