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

波达方向估计是混合mMIMO系统波束成形得以应用的前提,基于协方差矩阵重构的子空间方法在相干信号和有限快拍数条件下性能损失较大。为了应对上述挑战,提出了一种基于稀疏贝叶斯学习的混合mMIMO系统波达方向估计方法,主要创新之处在于:将混合mMIMO系统的波达方向估计问题转化为稀疏信号恢复问题,从而绕过空间协方差矩阵重构,避免了其带来的性能损失。为了便于进行贝叶斯推断,进一步利用变分贝叶斯近似思想,在恢复稀疏信号的同时,自适应估计出未知参数,显著改善了对噪声和相干信号的鲁棒性,提升了有限快拍数情况下的波达方向估计性能。数值模拟结果验证了所提方法的优越性。

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.