数据采集与处理2024，Vol.39Issue(5)：1260-1270,11.DOI:10.16337/j.1004-9037.2024.05.017
基于稀疏贝叶斯学习的混合mMIMO系统波达方向估计
Direction-of-Arrival Estimation for Hybrid mMIMO Systems via Sparse Bayesian Learning
摘要
Abstract
The direction-of-arrival(DOA)estimation is the premise of beamforming for hybrid massive multiple-input multiple-output(mMIMO)systems.The subspace methods based on covariance matrix reconstruction suffer from a large performance loss under the conditions of correlated signals and limited snapshots.To address the above challenges,this paper proposes a DOA estimation method for hybrid mMIMO systems via sparse Bayesian learning(SBL).It can be seen that the problem of DOA estimation for hybrid mMIMO systems is transformed into the issue of sparse signal recovery,bypassing the spatial covariance matrix reconstruction and avoiding the performance loss caused by the subspace methods.By using variational Bayesian inference(VBI),unknown parameters are estimated adaptively,which significantly improves the robustness of noise and correlated signals and enhances the performance of DOA estimation in the case of limited snapshots.Numerical simulation results verify the superiority of the proposed method.关键词
波达方向估计/模数混合结构/大规模多输入多输出系统/稀疏贝叶斯学习/变分贝叶斯推断Key words
direction-of-arrival(DOA)estimation/hybrid analog-digital structure/massive multiple-input multiple-output(mMIMO)systems/sparse Bayesian learning(SBL)/variational Bayesian inference(VBI)分类
信息技术与安全科学引用本文复制引用
慕欣茹,傅海军,戴继生..基于稀疏贝叶斯学习的混合mMIMO系统波达方向估计[J].数据采集与处理,2024,39(5):1260-1270,11.基金项目
国家自然科学基金(62071206). （62071206）
