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

Abstract

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.

Performance Analysis of Channel Estimation in 5G millimeter-Wave-MIMO Heterogeneous Systems

“…This indicated that the independent treatment of each stage did not allow the joint utilization of the channel sparsity and low-rank properties [27].The practical implementation of large antenna arrays, hybrid precoding, and the sparsity of mm-Wave channel prevented traditional CE methods designed for a lower-frequency MIMO system applicable for the mm-Wave -Ma-MIMO system [28]. Hence, specific CE techniques for mm-Wave-Ma-MIMO systems were proposed in [29][30][31][32][33].In [28], [30,31], the Eigen-decomposition based algorithms using the availability of low rank channel covariance matrices were designed for CE.…”

“…Unfortunately, the complex nature of these CE algorithms was high to a large extent and required large overhead to obtain reliable channel covariance matrices. To eliminate this problem, [32,33] proposed the CS based CE methods by exploiting the sparsity of mm-Wave channel in the angular domain and incorporating the hybrid architecture, while dealing with the management of multi-cell inter-cell and multi-user intra-cell interference in ultra-dense heterogeneous networks as well as beam squint effect. But, the complexity was still of high level due to the nonlinear optimization; their effectiveness highly depended on the Restricted Isometry Property (RIP) as well as estimation of the whole cosparse mm-Wave channel instead of its gain only.…”

“…In the last, the conclusion is provided in section 5. (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) dB loss [39] and materials such as brick can weaken signals by as much as 40-80 dB [40]. The Doppler spread of over 3 KHz at 60 km/h at 60 GHz will change the channel in the order of hundreds of microseconds, which is much faster than today's cellular systems.…”

“…In paper[17], proposed Sparsity Update Compressive Sampling Matching Pursuit (SUCoSaMP) algorithm was used in OFDM symbol transmitted by each antenna in the mentioned antenna sub-group. In paper[33], subcarrier groupingbased analysis cosparse recovery namely Reweighed Structured Analysis Cosparsity Update Subspace Pursuit (ReCoSUSP) algorithm was used for CE in wideband mm-Wave-MIMO output system. The paper[25] can be extended by incorporating mm-Wave with Ma-MIMO and performing the analysis of beamspace CE using the CS technique.…”

Optimization of the quality‐to‐power ratio of scalable video code video transmission in millimeter‐wave massive multiple‐input multiple‐output systems

“…However, massive MIMO systems cannot be directly adopted in mmWave frequency band, because each antenna needs to be connected to a radio frequency (RF) chain, which results in high hardware cost and power consumption of massive MIMO systems 19,20 . In order to solve this problem, one feasible solution is to employ the discrete lens antenna (DLA) array 21 .…”