低复杂度近场信道估计与子载波协作混合预编码用于宽带XL-MIMO OFDM系统
Low-Complexity Near-Field Channel Estimation and Subcarrier-Cooperative Hybrid Precoding for Wideband XL-MIMO OFDM Systems
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中文总结 AI 辅助
本文针对宽带XL-MIMO OFDM系统，提出基于序贯稀疏贝叶斯学习的低复杂度近场信道估计算法和子载波协作混合预编码框架，解决了高维参数估计和网格失配问题，仿真验证了其准确性和有效性。
中文摘要 AI 辅助
本文针对宽带极大孔径MIMO（XL-MIMO）OFDM系统，研究了精确的近场信道获取和可扩展的预编码问题，特别关注降低复杂度。在所考虑的系统中，每个MIMO多径分量由五个连续的角-距离-时延参数表征，这使得传统的稀疏恢复方法计算上不可行，并且存在网格失配问题。我们提出了一种基于序贯稀疏贝叶斯学习（SBL）的解耦离网格信道估计算法。通过利用OFDM近场原子的可分离结构，五维联合搜索被替换为一系列低维操作和活动集推断，避免了随各维网格大小的乘法缩放。基于精确边际似然，引入了连续域牛顿细化以减轻网格失配。基于估计的多径参数，我们进一步开发了一个用于多用户宽带传输的子载波协作混合预编码框架。建立了一种免大规模矩阵求逆的交替优化算法以最大化用户总速率，同时提出了一种利用参数化信道结构的非迭代低复杂度设计，该设计提供了有效的初始化。仿真结果证明了所提估计方法相对于若干基准的准确性，以及基于估计信道参数的预编码算法的有效性。
英文摘要
This paper addresses both accurate near-field channel acquisition and scalable precoding for wideband extremely large aperture MIMO (XL-MIMO) OFDM systems, with particular focus on reducing complexity. In the considered system, each MIMO multipath component is characterized by five continuous angle-distance-delay parameters, making conventional sparse recovery methods computationally infeasible while suffering from grid mismatch. We propose a decoupled off-grid channel estimation algorithm based on sequential sparse Bayesian learning (SBL). By exploiting the separable structure of the OFDM near-field atom, the five-dimensional joint search is replaced by a sequence of low-dimensional operations and active-set inference, avoiding multiplicative scaling with the per-dimension grid sizes. A continuous-domain Newton refinement is incorporated based on the exact marginal likelihood to mitigate the grid mismatch. Based on the estimated multipath parameters, we then develop a subcarrier-cooperative hybrid precoding framework for multiuser wideband transmission. A large-scale-matrix-inversion-free alternating optimization algorithm is established to maximize the sum user rate, together with a non-iterative low-complexity design that exploits the parameterized channel structure and provides an effective initialization. Simulation results demonstrate the accuracy of the estimation methods compared to several benchmarks and the effectiveness of the precoding algorithm based on estimated channel parameters.
发表机构
- Technische Universität Berlin（柏林工业大学）
- Huawei Technologies Duesseldorf GmbH（华为技术有限公司杜塞尔多夫分公司）
机构由 AI 辅助整理，请以论文原文为准。
