Improved MMSE channel estimation in massive MIMO system with a method for the prediction of channel correlation matrix

Almamori, Aqiel; Mohan, Seshadri

doi:10.1109/ccwc.2018.8301699

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…A low complex Least Square (LS) estimation is presented in Reference [74], but the accuracy of the method is not optimal. Linear Minimum Mean Square Error (MMSE) algorithm is proposed in References [75,76] and several improvements of the MMSE algorithm are discussed in References [77,78]. Although MMSE provides optimal accuracy, the computational complexity is increased with more number of antennas.…”

Section: Channel Estimationmentioning

confidence: 99%

“…Least Square [74], MMSE [75,76], Improved MMSE [77,78], Blind Estimation [80,81], Compresses Sensing [82,83], MICED [84], Untraind Deep Neural Network [85], Compressed Sensing [86], Convolutional Blind Denoising [87], VAMP [88], Deep Learning based Sparse Estimation [89], CNN based Estimation [150], Machine Learning based Estimate [151,158], Deep Learning based Estimation [153,155] Precoding DPP [93], TH [94,95], VP [96], MRC [97], ZF [98,99], WF [100], MMSE [101,102] User Scheduling ZF [105], MMSE [106], DPC [92], RR [107], PF [108], Greedy [109], Multi-user Grouping [112], Gibbs Distribution Scheme [114], Pilot Efficient Scheduling [115], Machine Learning based Scheduling [159] Hardware Impairments Digital Pre-Distortion [118,119], PAPR [120],…”

Section: Channel Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction

Chataut

Akl

2020

Sensors

374

150

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The global bandwidth shortage in the wireless communication sector has motivated the study and exploration of wireless access technology known as massive Multiple-Input Multiple-Output (MIMO). Massive MIMO is one of the key enabling technology for next-generation networks, which groups together antennas at both transmitter and the receiver to provide high spectral and energy efficiency using relatively simple processing. Obtaining a better understating of the massive MIMO system to overcome the fundamental issues of this technology is vital for the successful deployment of 5G—and beyond—networks to realize various applications of the intelligent sensing system. In this paper, we present a comprehensive overview of the key enabling technologies required for 5G and 6G networks, highlighting the massive MIMO systems. We discuss all the fundamental challenges related to pilot contamination, channel estimation, precoding, user scheduling, energy efficiency, and signal detection in a massive MIMO system and discuss some state-of-the-art mitigation techniques. We outline recent trends such as terahertz communication, ultra massive MIMO (UM-MIMO), visible light communication (VLC), machine learning, and deep learning for massive MIMO systems. Additionally, we discuss crucial open research issues that direct future research in massive MIMO systems for 5G and beyond networks.

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Section: Channel Estimationmentioning

confidence: 99%

Section: Channel Estimationmentioning

confidence: 99%

Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction

Chataut

Akl

2020

Sensors

374

150

View full text Add to dashboard Cite

show abstract

“…Aqiel and Seshadri [3] had suggested updating channel correlation matrix based on last known CSI and temporal properties for MMSE estimation. A two-stage channel estimation is proposed to estimate the cascaded channel matrix by utilizing special structure of the received signal in the destination [4].…”

Section: Related Workmentioning

confidence: 99%

Performance Optimization of LS/LMMSE Using Swarm Intelligence in 3D MIMO-OFDM Systems

Bhange¹,

Dethe²

2020

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The capability of Least square (LS) and least minimum mean squared error (LMMSE) channel estimation techniques are limited due to one or two factors (inherent additive Gaussian noise and Inter Carrier Interference, higher computational complexity). These factors tend to be severe when the system grows in terms of numbers of transmitting and receiving antennas, channel parameters, noise etc. Accurate channel parameters estimation using these techniques is still not possible even with smaller Multi input multi output (MIMO) systems at higher signal to noise ratios (SNR) due to complex nature of channel parameters. Swarm Intelligence consisting of agents spread in search space having limited capabilities and random behaviour when interacts with each other and within their own locality are capable of finding solution for a complex problem. When the constructive behaviour of such particles in particle swarm optimization (PSO) within the search space limited to some constraint is applied to optimize the performance of 3D-Pilot Aided Channel Estimation (3D-PACE) of MIMO-OFDM system, results showed that the bit error rate (BER) is significantly decreased. The channel parameters at the receiver obtained using LS and LMMSE are further optimized using PSO with proper and careful setting of PSO initial parameters. Keywords:bit error rate, 3D-PACE, multi input multi output, orthogonal frequency division multiplexing, particle swarm optimization

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“…Compared with [24], our paper focuses on how to optimize the energy efficiency of the system through resource allocation and pilot design with channel estimation information, rather than calculating the upper channel capacity limit in the case of multiple antenna arrays. We would like to mention that the pilots and data are in different slots rather than within the channel coherence time, and we may reduce the additional pilot power cost and obtain higher EE, which is different from [25]- [27]. In addition, we note that the analytical expression of the channel data rate is more complicated in our paper.…”

Section: Introductionmentioning

confidence: 98%

Energy-Efficient Channel Estimation

et al. 2020

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We investigate the energy-efficient channel estimation in wireless networks, where one pilot is inserted for every several data slots to estimate the channel coefficients. Both the channel state information estimation error and the time-varying model are considered. We first formulate the energy-efficient channel estimation problem into a mixed integer nonlinear programming (MINLP) problem, where the variables include the transmitted power and the number of data slots. Due to the NP-hard nature, we degenerate the MINLP problem into a series of non-concave optimization problems without integer variables. Then we solve these problems using successive convex approximation, geometric programming, and the Dinkelbach algorithm to obtain a point satisfying the Karush-Kuhn-Tucker (KKT) conditions. Furthermore, we develop a low-complexity sub-optimal scheme through binary variable relaxation to obtain a solution, and the convergence point satisfies the KKT conditions of the relaxed non-concave problem. Simulation results demonstrate the convergence and effectiveness of our proposed schemes.

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Improved MMSE channel estimation in massive MIMO system with a method for the prediction of channel correlation matrix

Cited by 8 publications

References 15 publications

Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction

Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction

Performance Optimization of LS/LMMSE Using Swarm Intelligence in 3D MIMO-OFDM Systems

Energy-Efficient Channel Estimation

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