${\ell}_{1/2}$ -Regularization-Based Super-Resolution Sparse Channel Estimation for MmWave Massive MIMO Systems

Abstract

Channel state information (CSI) is required for both precoding at the transmitter and detection at the receiver in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Accurate channel estimation poses significant technique challenges for designing the mmWave MIMO systems. Considering the channel sparsity in mmWave massive MIMO systems with hybrid precoding, this paper proposes an 1/2 -regularization-based sparse channel estimation method. The basic idea of the proposed method is to formulate the sparse channel estimation problem as a compressed sensing problem. Specifically, the method firstly constructs an objective function, which is a weighted sum of the 1/2 -regularization and error constraint term. It is then optimized via the gradient descent method iteratively and the weight parameter in the function is also updated in each iteration. In contrast to conventional algorithms, our proposed method can avoid the quantization error and finally realize super-resolution performance. The simulation experiments verified that the proposed method can achieve better performance than traditional ones.INDEX TERMS Sparse channel estimation, 1/2 -regularization, iterative reweighted methods, massive MIMO, millimeter-wave (mmWave).

Massive Access for 5G and Beyond

“…Firstly, the mmW/THz channel is very sparse, such that CS and Bayesian methods can be used to acquire the CSI required for the design of the precoding matrix. In [123], to obtain CSI, the l 1,2 -regularization-based CS method was applied, which can avoid quantization errors and provide superresolution performance. By modeling the channel coefficients as Laplacian distributed random variables, a GAMP algorithm was used to find the entries of the unknown mmWave MIMO channel matrix in [124].…”

A 3D Wideband Two-Cluster Channel Model for Massive MIMO Vehicle-to-Vehicle Communications in Semi-Ellipsoid Environments

“…For designing and evaluating the performance of vehicleto-vehicle (V2V) communication systems in fifth-generation (5G) networks, we should gain insight into the statistical channel characteristics of V2V communications between a mobile transmitter (MT) and a mobile receiver (MR) [1]. As one of the key technologies in 5G communication systems, massive multiple-input and multiple-output (MIMO) has been widely used in V2V communications [2], [3]. To create a way of testing, measuring, and validating the system performance in a realistic, repeatable, and reproducible manner, it is of vital importance to introduce massive MIMO channel models for V2V communications [4], [5].…”

Improved Primal–Dual Interior-Point Method Using the Lawson-Norm for Inverse Problems

“…To overcome such drawbacks, the l 1 -norm (e.g. the total variation (TV) regularization [13]) acting as the regularization term has been applied in many reconstructions as it reduces the redundancy information and yields a sparse parameter representation which leads to better edge sharpness and fewer image artifacts [10], [14]- [16]. Thus, the better ability of reconstructing sharp changes can improve the recognition of the boundaries and the accuracy of the estimated results [17].…”

“…the minimum support (MS) functionals, the minimum gradient support (MGS) functionals [12]) regularizations have been proposed. The fraction norms are also studied to improve the reconstruction resolution [15], [16]. In order to get stable and accurate results, hydrid norms inverse methods are proposed in some works.…”