Abstract
Millimeter-wave massive MIMO can effectively improve the signal-to-noise ratio, but the high-dimensional channel matrix significantly increases the complexity of the classic channel estimation algorithm. On the other hand, millimeter-wave massive MIMO has low rank and sparsity properties in the angle domain. Combining these two properties can effectively improve the channel estimation accuracy. This article proposes a novel millimeter-wave sparse channel estimation method based on joint nuclear norm and $\ell _{1-2}$ -regularization. The basic idea of the proposed algorithm is to formulate the channel estimation problem as a compressed sensing problem. This method constructs an objective function consisted of $\ell _{1-2} $ -regularization, and the resulting nuclear norm minimization problems is optimized via the alternating direction method of multipliers (ADMM) algorithm. The simulation results verified that the proposed method can provide better estimation accuracy compared with the state-of-the-art compressed sensing-based channel estimation methods.
Highlights
Mobile communication systems have been constantly evolving due to new applications and demands
Previous works proposed a method for millimeter wave channel estimation that exploits both the low-rank and sparsity properties of millimeter-wave massive MIMO channel via two-step, which first uses the low-rank property of the channel to recover the received signal, followed by the use of compressed sensing algorithm to estimate the millimeter wave channel gain matrix [18]–[21]
The complexity increases with the dimension of the Algorithm 1 Jointly Nuclear Norm and 1−2-Regularization Based Millimeter wave (mmWave) Channel Estimation Input: received matrix Y, sensing matrix, weight parameters τY, τS, ρ, iteration times Imax output: H 1: Initialization X(0) = V(20) = 0 ∈ CNR×T ; s(0) = z(0) = v(10) = 0 ∈ CNRNT ×1; k = 0 2: repeat
Summary
Mobile communication systems have been constantly evolving due to new applications and demands. Previous works proposed a method for millimeter wave channel estimation that exploits both the low-rank and sparsity properties of millimeter-wave massive MIMO channel via two-step, which first uses the low-rank property of the channel to recover the received signal, followed by the use of compressed sensing algorithm to estimate the millimeter wave channel gain matrix [18]–[21]. Additional works combined these two characteristics simultaneously for millimeter wave channel estimation at a same time [22]–[24]. IN represents N × N dimensional identity matrix; represents the selection matrix composed of 0 and 1; F is the F−norm of the matrix
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