arXiv 2409.166642024-09-25eess.SP

基于卷积字典学习的XL-RIS辅助大规模MIMO系统混合场信道估计

Convolutional Dictionary Learning Based Hybrid-Field Channel Estimation for XL-RIS-Aided Massive MIMO Systems

Peicong Zheng, Xuantao Lyu, Ye Wang, Yi Gong

AI总结：

针对XL-RIS辅助大规模MIMO系统中远近场共存的混合场信道估计问题，提出基于卷积字典学习的CNN-CDL方法及Convolutional ISTA-Net架构，通过引入可学习模块优化双层问题，有效提升了信道估计性能。

摘要：

极大规模可重构智能表面（XL-RIS）正成为6G系统的一项极具前景的关键技术。为充分发挥XL-RIS的全部潜力，准确的信道估计至关重要。本文研究了在远场和近场信道共存的混合场场景下，XL-RIS辅助的大规模MIMO系统中的信道估计问题。我们使用字典学习来构建该问题，从而实现对字典和估计信道的联合优化。为处理XL-RIS信道的高维特性，我们专门采用了卷积字典学习（CDL）公式化方法。CDL公式化被构建为一个双层优化问题，我们使用基于梯度的方法对其进行求解。为解决计算上层目标函数梯度的挑战，我们引入了一种基于近端梯度下降（PGD）及其特例迭代软阈值算法（ISTA）的展开优化方法。我们提出了两种神经网络架构：Convolutional ISTA-Net及其增强版Convolutional ISTA-Net+，用于CDL的端到端优化。为克服线性卷积滤波器在捕获复杂混合场信道结构方面的局限性，我们提出了CNN-CDL方法，该方法在其梯度下降步骤中用CNN块替代线性卷积滤波器，在其近端映射步骤中采用可学习的近端映射模块，并整合了跨层特征集成。仿真结果证明了所提方法在混合场XL-RIS系统信道估计中的有效性。

英文摘要：

Extremely large reconfigurable intelligent surface (XL-RIS) is emerging as a promising key technology for 6G systems. To exploit XL-RIS's full potential, accurate channel estimation is essential. This paper investigates channel estimation in XL-RIS-aided massive MIMO systems under hybrid-field scenarios where far-field and near-field channels coexist. We formulate this problem using dictionary learning, which allows for joint optimization of the dictionary and estimated channel. To handle the high-dimensional nature of XL-RIS channels, we specifically adopt a convolutional dictionary learning (CDL) formulation. The CDL formulation is cast as a bilevel optimization problem, which we solve using a gradient-based approach. To address the challenge of computing the gradient of the upper-level objective, we introduce an unrolled optimization method based on proximal gradient descent (PGD) and its special case, the iterative soft-thresholding algorithm (ISTA). We propose two neural network architectures, Convolutional ISTA-Net and its enhanced version Convolutional ISTA-Net+, for end-to-end optimization of the CDL. To overcome the limitations of linear convolutional filters in capturing complex hybrid-field channel structures, we propose the CNN-CDL approach, which enhances PGD by replacing linear convolution filters with CNN blocks in its gradient descent step, employing a learnable proximal mapping module in its proximal mapping step, and incorporating cross-layer feature integration. Simulation results demonstrate the effectiveness of the proposed methods for channel estimation in hybrid-field XL-RIS systems.

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