Channel Estimation for MmWave Massive MIMO With Hybrid Precoding Based on Log-Sum Sparse Constraints

Abstract

Channel estimation is essential for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems with hybrid precoding. However, accurate channel estimation is a challenging task as the number of antennas is huge, while the number of RF chains is limited. Traditional methods of compressed sensing for channel estimation lead to serious loss of accuracy due to channel angle quantization. In this paper, we propose a new iterative reweight-based log-sum constraint channel estimation scheme. Specifically, we exploit the structure sparsity of the mmWave channels by formulating the channel estimation problem as an objective optimization problem. We utilize the log-sum as a constraint, via optimizing an objective function through the gradient descent method, the proposed algorithm can iteratively move the channel estimated angle-ofarrivals (AOAs) and angle-of-departures (AODs) towards the optimal solutions, and finally improve the angle estimation performance significantly. In addition, to ensure the accuracy of channel estimation, we introduce a dynamic regularization factor to control the tradeoff between the channel sparsity and the data fitting error. Numerical experiments demonstrate that the proposed algorithm achieves better convergence behavior than conventional sparse signal recovery solutions.

Recovery Analysis of Log-Sum Minimization Under Mutual Coherence Condition

“…x ∪S 2 x, we now consider exact recovery of sparse signal x by solving the global minimum solution to (8). The following result shows that a sufficiently small parameter ε can make the inequality f (x) > f (x) true.…”

“…Throughout this paper, all the problems are discussed based on the assumption that the columns of Φ are standardized to have unit ℓ 2 norm. Since the optimization ( 1) is a NP-hard problem that has computational complexity growing exponentially with the signal dimension, many improved efficient methods have been proposed recently, such as, orthogonal matching pursuit (OMP) [4], the log-sum (q= 1, 2) minimization [5][6][7][8][9] min…”

An Iterative Threshold Algorithm of Log-Sum Regularization for Sparse Problem

“…The sparse problems encountered in many domains of scientific research and engineering practice have attracted extensive attention in recent years, such as MmWave Massive MIMO channel estimation [1]- [3], machine learning [4], [5], jammer detection [6], [7], image processing [8]- [12]. The canonical form of this problems can be expressed as…”

Massive MIMO Array Design With High Isolation by Using Decoupling Cavity

“…HIS massive multiple-input multiple-output (MIMO) systems have been widely used in current and future wireless communications [1][2][3]. During the implementation progress, the mutual coupling of the array seriously degrades the antenna performance, such as signal-to-noise ratio, active voltage standing wave ratio (VSWR), impedance match, and channel capacity [4][5][6]. Thus, researchers have paid much effort to the isolation enhancement for massive MIMO arrays.…”