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Literature review and research direction towards channel estimation and hybrid pre-coding in mmWave massive MIMO communication systems

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Abstract

Owing to the higher frequency, bigger network capacity, and reduced latency, fifth generation mobile communication (5G) has garnered a lot of interest with the rapid advancement of technology. It incorporates a variety of technologies, including “millimeter-wave (mmWave) transmission and massive Multiple-Input Multiple-Output (MIMO)”. The existing digital precoding is too expensive to implement in mmWave communication systems. As a result, hybrid precoding, which mixes digital and analog precoding, is a superior option. It is generally built on one of two types of structures: “fully connected and sub-connected. The fully connected structure” has been intensively explored in the academic community because it can approach the theoretical ideal spectrum efficiency. The high antenna size, on the other hand, complicates the necessity for a low-complexity "channel estimation and hybrid precoding design". Hybrid precoding, in specific, may necessitate “matrix operations on a scale of antenna size”, which is often significant in mmWave transmission. In order to learn more about the latest developments in mmWave large MIMO communication systems, this paper aims to undergo a critical review on channel estimation and hybrid precoding in mmWave massive MIMO communication systems. This review evaluates the different algorithms to be implemented for both “channel estimation and hybrid pre-coding in communication systems”. In addition, the performance measures concentrated in each contribution are observed and categorized. Finally, the conventional strategies will relieve the existing research gaps and challenges with new research directions to be used for the future professionals to maintain the “channel estimation and hybrid pre-coding of mmWave massive MIMO system” at a good level.

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Abbreviations

AoA/AoD:

Angles of arrival or departure

CSI:

Channel state information

ML:

Machine learning

FDD:

Frequency division duplex

DL:

Deep learning

CNN:

Convolutional neural network

DGMP:

Distributed grid matching pursuit

LOS:

Line of sight

OMP:

Orthogonal matching pursuit

FSF:

Frequency selective fading

PAPR:

Peak-to-average power ratio

CP:

Candecomp/Parafac

CRB:

Cramér–Rao bound

FCE:

Fast channel estimation

PEE:

Probability of estimation error

RACE:

Rate-adaptive channel estimation

TS:

Training sequence

MAP:

Maximum a posteriori

MSE:

Mean square error

LDAMP:

Learning denoising-based approximate message passing

DFT:

Discrete Fourier transform

MMSE:

Minimal mean square error

NUPA:

Non-uniform planar arrays

CS:

Compressive sensing

SDP:

Semi-definite programme

SF-CNN:

Spatial-frequency convolutional neural network

FFDNet:

Flexible denoising convolutional neural network

SBEM:

Spatial basis expansion model

OSBS:

Optimized semi-blind sparse

SFT-CNN:

Spatial-frequency-temporal convolutional neural network

PSA:

Pulse shaping algorithm

ISI:

Inter-symbol interference

IFFT:

Inverse fast Fourier transforms

AWGN:

Additive white Gaussian noise

EDE:

Enhanced differential evolution

CC:

Channel capacity

SER:

Symbol error rate

GraSP:

Gradient support pursuit

FFT:

Fast Fourier transform

CBDNet:

Convolutional blind denoising network

ZF:

Zero-forcing

SIC:

Successive interference cancelation

GraHTP:

Gradient hard thresholding Pursuit

AltMin:

Alternate minimization

RF:

Radio frequency

BD:

Block diagonalization

AP:

Analog precoder

ADC:

Analog to digital converter

TMAs:

Time-modulated arrays

SE:

Spectral efficiency

LcHPC:

Low-complexity hybrid precoder and combiner

SdMP:

Stage-determined matching pursuit

MIMO:

Multiple input multiple output

DNN:

Deep neural network

CEO:

Cross-entropy optimization

ASR:

Achievable sum-rate

GS:

Gram–Schmidt

IF:

Interference free

PMA:

Phase modulation arrays

MA-FAHP:

Matching assisted fully adaptive hybrid precoding

AS:

Antenna selection

AO:

Alternating optimization

mmWave:

MillimeterWave

P2P:

Point-to-point

QCQP:

Quadratically constrained quadratic programming

ULS:

Unit-modulus least-squares

SDR-AO:

Semi-definite relaxation-oriented alternating optimization

PCA:

Principle component analysis

FD:

Full-dimensional

ACMF-AO:

Analytical constant modulus factorization based alternating optimization

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Authors and Affiliations

  1. Embedded Technology, Vellore Institute of Technology, Vellore, India

    G. Navabharat Reddy & C. V. Ravikumar

  2. School of Electrical and Electronics Engineering, SASTRA University, Thanjavur, India

    A. Rajesh

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  1. G. Navabharat Reddy
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  3. A. Rajesh
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Reddy, G.N., Ravikumar, C.V. & Rajesh, A. Literature review and research direction towards channel estimation and hybrid pre-coding in mmWave massive MIMO communication systems. J Reliable Intell Environ 9, 241–260 (2023). https://doi.org/10.1007/s40860-022-00174-5

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