Extremely large-scale massive multiple-input multiple-output (XL-MIMO) systems introduce the much higher channel dimensionality and incur the additional near-field propagation effect, aggravating the computation load and the difficulty to acquire the prior knowledge for channel estimation. In this article, an XL-MIMO channel network (XLCNet) is developed to estimate the high-dimensional channel, which is a universal solution for both the near-field users and far-field users with different channel statistics. Furthermore, a compressed XLCNet (C-XLCNet) is designed via weight pruning and quantization to accelerate the model inference as well as to facilitate the model storage and transmission. Simulation results show the performance superiority and universality of XLCNet. Compared to XLCNet, C-XLCNet incurs the limited performance loss while reducing the computational complexity and model size by about $10 \times$ and $36 \times$, respectively.

翻译：超大规模多输入多输出系统带来了更高的信道维度并引入了额外的近场传播效应，加剧了计算负荷和获取信道估计先验知识的难度。本文提出了一种超大规模MIMO信道网络，用于估计高维信道，该方案对具有不同信道统计特性的近场用户和远场用户均具有普适性。进一步地，通过权重剪枝与量化设计了一种压缩型XLCNet，以加速模型推理并便于模型存储与传输。仿真结果表明XLCNet具有优越的性能和普适性。与XLCNet相比，C-XLCNet在将计算复杂度和模型尺寸分别降低约10倍和36倍的同时，仅带来有限的性能损失。