Beam Domain Channel Estimation for Spatial Non-Stationary Massive MIMO Systems

TL;DR

Massive MIMO CSI is challenged by spatial non-stationarity (SNS) and power leakage in beam-domain representations. The authors introduce a realistic SNS beam-domain channel model (BDCM) that uses visibility regions (VR) to capture cluster visibility and leakage, and propose a beam-domain sparsity adaptive matching pursuit (BDS-SAMP) algorithm that exploits cross-block sparsity and a power-ratio threshold to iteratively recover the beam-domain channel from compressed measurements. The method transforms array-domain channels to the beam domain and recovers ${\mathbf{h}}_{B,u}$ from measurements ${\mathbf{y}}_u = {\mathbf{\Phi}} {\mathbf{h}}_{B,u} + {\tilde{\mathbf{n}}}_u$, accounting for leakage and partially visible clusters. Simulation results show that BDS-SAMP yields substantial NMSE improvements over SAMP, BOMP, and ASD for SNS MIMO with reduced pilot overhead, and remains competitive to oracle LS in some regimes. The work offers a practical channel estimation framework for SNS massive MIMO by combining a VR-based channel model with a sparsity-aware, leakage-tolerant recovery algorithm.

Abstract

In massive multiple-input multiple-output (MIMO) systems, the channel estimation scheme is subject to the spatial non-stationarity and inevitably power leakage in the beam domain. In this paper, a beam domain channel estimation scheme is investigated for spatial non-stationary (SNS) massive MIMO systems considering power leakage. %a novel beam domain channel estimation scheme is proposed for spatial non-stationary (SNS) massive MIMO systems. Specifically, a realistic massive MIMO beam domain channel model (BDCM) is introduced to capture the spatial non-stationarity considering power leakage by introducing the illustration of visibility region (VR). Then, a beam domain structure-based sparsity adaptive matching pursuit (BDS-SAMP) scheme is proposed based on the cross-block sparse structure and power ratio threshold of beam domain channel. Finally, the simulation results validate the accuracy of proposed BDS-SAMP scheme with low pilot overhead and reasonable complexity by comparing with conventional schemes.

Figures (7)

• Figure 1: System structure diagram for massive MIMO systems.
• Figure 2: Flowchart of generating the SNS BDCM.
• Figure 3: Sparse structure of SNS massive MIMO beam domain channel.
• Figure 4: Envelop of ${f_{I_{s}^h,I_{e}^h}} \left( {\theta_0^{az} - \tilde{\theta} _j^{az}} \right)$ ($\theta_0^{az} = 0, I_{s}^h = P_h/4, I_{e}^h = P_h/2-1$).
• Figure 5: NMSE performance comparison of different channel estimation schemes for SNS massive MIMO systems ($P_h$ = 32, $P_v$ = 32, $K$ = 256, $\rho = 0.45$).