Abstract: Extremely Large-scale Multiple-Input Multiple-Output (XL-MIMO) systems are considered as one of the key technologies to realize 6G communications. However, due to the significant increase in the number of antennas in XL-MIMO systems, the channel exhibits hybrid field characteristics, thus posing a great challenge to channel estimation. To address this problem, this paper proposes a deep learning-based Adaptive Frequency Filter Parallel Joint Convolutional Network (AFF-PJCN) channel estimation algorithm. Firstly, the received signal is processed by the adaptive frequency filter network, which is equipped with learnable filters that can automatically optimize the filtering parameters according to the input data, enabling adaptive signal analysis and modeling within the frequency domain, and effectively filtering out noise interference. Then, through the parallel joint convolutional network, the multi-scale convolutional operation of the parallel structure can effectively capture the global and local features of the received signal, further enhancing the channel estimation performance. To enhance the generalization ability of the model, a segmented hybrid data training strategy is adopted. The training set is constructed by independently sampling randomly in different signal-to-noise ratio intervals, ensuring that the model maintains robust performance under diverse channel conditions. The experimental results show that the proposed AFF-PJCN algorithm not only achieves superior estimation accuracy but also demonstrates stronger generalization and robustness compared with other existing channel estimation schemes in the hybrid field channel model of XL-MIMO systems.

摘要： 超大规模多输入多输出(XL-MIMO)系统被认为是实现6G通信的关键技术之一。然而，由于XL-MIMO系统天线数量大幅增加，导致信道呈现混合场特性，从而给信道估计带来巨大挑战。针对这一问题，提出了一种基于深度学习的自适应频率滤波并行联合卷积网络(AFF-PJCN)信道估计算法。首先利用自适应频率滤波网络对接收信号进行处理，该网络配备可学习的滤波器，能够依据输入数据自动优化滤波参数，在频域中实现自适应的信号分析与建模，并有效滤除噪声干扰。再通过并行联合卷积网络，并行结构的多尺度卷积操作可以有效捕获接收信号的全局和局部特征，进一步提升信道估计性能。为了增强模型的泛化能力，采用分段混合数据训练策略，通过在不同信噪比区间内独立随机采样构建训练集，确保模型在多样化的信道条件下均能保持稳健性能。实验结果表明，在XL-MIMO系统的混合场信道模型中，所提出的AFF-PJCN算法与现有的其他信道估计方案相比，不仅具有更优的估计精度并且展现出更强的普适性、鲁棒性。