Optimal Uplink Channel Estimation Algorithm for OFDM Based MmWave Massive MIMO Systems

2022

Int J Communication

Self Cite

Summary Millimeter wave (mmWave) massive multiple‐input multiple‐output (MIMO) channels are cosparse in nature. These cosparse channels share common cosparsity properties, which is interesting when it comes to estimating a large number of mmWave mMIMO channels. Therefore, the channel estimation (CE) problem can be solved by exploiting the cosparsity and common cosupport properties. Specifically, the CE problem can be formulated as a cosparse signal recovery problem, which can be solved using an analysis operator (AO) and the iterative hard thresholding (IHT) algorithm. Prior analysis‐based compressive sensing (ACS) CE techniques adopt an overcomplete AO, which leads usually to a suboptimal solution that cannot guarantee CE accuracy. In this paper, we firstly propose a projected subgradient (PSG) scheme for AO learning. Then, using the previously learned AO, we propose an iterative Newton hard thresholding (INHT)‐based ACS‐CE algorithm. The effectiveness of the proposal has been proven by computational complexity analysis and numerical simulations.

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative Newton hard thresholding‐based approach for cosparse recovery of mmWave massive MIMO channels

2022

Int J Communication

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“…To overcome this problem, the authors in [21,22] have shown that the mmW mMIMO channels were cosparse in nature and an extended cosparse analysis model was proposed to solve the mmW mMIMO CE problem. Specifically, a CS based CE method was presented to reduce training overhead by taking advantage of the common cosparsity property.…”

Section: Introductionmentioning

confidence: 99%

Joint dictionary and deep learning for cosparse recovery of the millimeter wave massive MIMO channels

The Journal of Engineering

2021

Self Cite

The channel estimation (CE) for millimeter wave (mmW) massive multiple input multiple output (mMIMO) is a challenging task because of the important number of transmit and receive antennas, which results in high pilot overhead. In conventional CE algorithms, the channel is modeled using pre-constructed dictionary. This often leads to a suboptimal solution which cannot guarantee CE accuracy. In this paper, an iterative two-stage CE algorithm is presented. In the first stage, training measurements under different conditions are collected and it is proposed to estimate the virtual sparse mmW mMIMO channel using a deep residual learning based orthogonal approximate message passing (DRL-OAMP) algorithm from these measurements. The estimated channel is used in the second stage to learn the dictionary via a projected gradient algorithm. Simulation results show that the proposal improves the CE accuracy with low pilot overhead.

Structured analysis/synthesis compressive sensing‐based channel estimation in wideband mmWave large‐scale multiple input multiple output systems

Mejri

Trans Emerging Tel Tech

Hasnaoui

2020

The hybrid architecture still be the most attractive solution for the analog‐digital beamforming in millimeter wave (mmWave) large‐scale multiple input multiple output (LS‐MIMO) systems, since it provides the best trade‐off between the achievable rate and the power consumption. When maximizing performance metrics, channel knowledge is required to optimally design the hybrid beamforming. The channel estimation (CE) is essential for wideband mmWave LS‐MIMO to substantially enhance data rates, however, is a challenging task. Practically, the antenna number and the bandwidth for such communications are very large. Therefore, the CE scheme should account for the frequency‐dependence of the array response vectors and thereby the beam squint effect. In the first part, this article addresses a structured analysis compressive sensing (CS) for CE in a frequency selective mmWave LS‐MIMO system without neglecting the beam squint effect. Compared to sparse mmWave CE approach that mostly uses on‐grid dictionaries, our proposed approach considers continually distributed angles of arrival and departure. In the second part, we show that the frequency‐selective mmWave MIMO channels share a common sparsity structure within the same subcarrier‐group. Then, we propose a block‐sparsity‐based CS approach that uses the simultaneous orthogonal matching pursuit method to estimate the mmWave channel. Moreover, we prove the partial sparsity structure shared between all the subcarrier‐groups. Hence, we develop our proposed joint subcarrier‐block‐based CE scheme exploiting these two block‐sparsity structures. Numerical results evaluate the performances in terms of normalized mean‐squared error and spectral efficiency to select the most accurate channel estimator with lower overhead.