Abstract
The availability of millimeter wave (mm-Wave) band in conjunction with massive multiple-input-multiple-output (MIMO) technology is expected to boost the data rates of the fifth-generation (5G) cellular systems. However, in order to achieve high spectral efficiencies, an accurate channel estimate is required, which is a challenging task in massive MIMO. By exploiting the small number of paths that characterize the mm-Wave channel, the estimation problem can be solved by compressed-sensing (CS) techniques. In this paper, we propose a novel CS channel estimation method based on the accelerated gradient descent with adaptive restart (AGDAR) algorithm exploiting a ℓ1-norm approximation of the sparsity constraint. Moreover, a modified re-weighted compressed-sensing (RCS) technique is considered that iterates AGDAR using a weighted version of the ℓ1-norm term, where weights are adapted at each iteration. We also discuss the impact of cell sectorization and tracking on the channel estimation algorithm. We compare the proposed solutions with existing channel estimations with an extensive simulation campaign on downlink third-generation partnership project (3GPP) channel models.
Highlights
Due to its huge spectrum availability, the millimeter wave band is currently considered for the fifth generation (5G) of cellular networks [1,2,3]
We propose a novel sparse channel estimation method based on the accelerated gradient descent with adaptive restart (AGDAR) algorithm [17]
We compare various channel estimation techniques for an average signal to noise ratio (SNR) of 10 dB. At this intermediate SNR value, we observe that the proposed AGDAR and re-weighted compressed-sensing (RCS) significantly outperform both single peak cancelation (SPC) and joint peak cancelation (JPC) for all the considered channel models, by 6 to 7 dB
Summary
Due to its huge spectrum availability, the millimeter wave (mm-Wave) band is currently considered for the fifth generation (5G) of cellular networks [1,2,3]. The mm-Wave MIMO channel comprises a small number of dominant clusters of paths and even with many antennas a small set of parameters characterizes the entire channel. A sparsity adaptive matching pursuit (SSAMP) approach is instead used in [15], while in [16] the LASSO problem is solved by applying a generalized approximate message passing (GAMP) algorithm exploiting the BernoulliGaussian distribution of paths in the virtual channel. Focusing on a scenario where the receiver obtains first the LS estimate of the narrowband mm-Wave MIMO channel, we relax the sparse optimization problem using LASSO, wherein the 0-norm is replaced by the 1-norm. In order to further enhance the channel estimation procedure, a re-weighted 1-norm problem is considered leading to the re-weighted compressed-sensing (RCS) algorithm [18], which iterates AGDAR with different weights of the 1-norm term.
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