scholarly journals Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 382 ◽  
Author(s):  
Imran Khan ◽  
Mohammad Zafar ◽  
Majid Ashraf ◽  
Sunghwan Kim
Keyword(s):  
Channel Estimation ◽  
Signal Detection ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Matrix Inversion ◽  
Computationally Efficient ◽  
Estimation Algorithms ◽  
Multiple Input ◽  
Input Multiple Output

Traditional channel estimation algorithms such as minimum mean square error (MMSE) are widely used in massive multiple-input multiple-output (MIMO) systems, but require a matrix inversion operation and an enormous amount of computations, which result in high computational complexity and make them impractical to implement. To overcome the matrix inversion problem, we propose a computationally efficient hybrid steepest descent Gauss–Seidel (SDGS) joint detection, which directly estimates the user’s transmitted symbol vector, and can quickly converge to obtain an ideal estimation value with a few simple iterations. Moreover, signal detection performance was further improved by utilizing the bit log-likelihood ratio (LLR) for soft channel decoding. Simulation results showed that the proposed algorithm had better channel estimation performance, which improved the signal detection by 31.68% while the complexity was reduced by 45.72%, compared with the existing algorithms.

scholarly journals Signal Detection in Spatially Multiplexed MIMO Systems

2021 ◽  
Vol 9 (VI) ◽  
pp. 5002-5005
Author(s):  
Mohan Reddy
Keyword(s):  
Signal Detection ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Spatial Multiplexing ◽  
Multiple Receivers ◽  
Rate Versus ◽  
Multiple Input ◽  
The Matrix ◽  
Input Multiple Output

The transmission of several signals and reception of those signals, it requires the implementation of multiple transmitters at the transmitter side and the multiple receivers at the receiver side. This type of system is called multiple input multiple output (M.I.M.O) system. The M.I.M.O systems will result in obtaining the better use of the available spectrum for transmissions of the different signals in the same spectrum and this makes the M.I.M.O systems most dependable for the wireless communications. But the presence of several signals in the same bandwidth of spatial multiplexing matrix in M.I.M.O systems makes it difficult for the signal to get detected at the receiver end. There are plenty of techniques introduced to avoid the difficulty in sensing the signal at receiver in M.I.M.O systems. In this paper we will be discussing about the signal detection technique called minimum mean square error technique (MMSE) which uses the inversion of the matrix to retrieve the signal and the iteration-based method that is an improvised technique than MMSE technique where the matrix inversion step is avoided and provides better results. The results are obtained by plotting the bit error rate versus the signal to nose ratio using MATLAB

scholarly journals Reduced Complexity by Combining Norm Based Ordering MMSE-BSIDE Detection in MIMO Systems

2017 ◽  
Vol 63 (3) ◽  
pp. 305-308
Author(s):  
Ramya Jothikumar ◽  
Nakkeeran Rangaswamy
Keyword(s):  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Detection Methods ◽  
Mimo Detection ◽  
Detection Scheme ◽  
Tree Traversal ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Simulation Results

AbstractThe breadth first signal decoder (BSIDE) is well known for its optimal maximum likelihood (ML) performance with lesser complexity. In this paper, we analyze a multiple-input multiple-output (MIMO) detection scheme that combines; column norm based ordering minimum mean square error (MMSE) and BSIDE detection methods. The investigation is carried out with a breadth first tree traversal technique, where the computational complexity encountered at the lower layers of the tree is high. This can be eliminated by carrying detection in the lower half of the tree structure using MMSE and upper half using BSIDE, after rearranging the column of the channel using norm calculation. The simulation results show that this approach achieves 22% of complexity reduction for 2×2 and 50% for 4×4 MIMO systems without any degradation in the performance.

scholarly journals Comparison of Semidefinite Relaxation Detectors for High-Order Modulation MIMO Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Z. Y. Shao ◽  
S. W. Cheung ◽  
T. I. Yuk
Keyword(s):  
Computational Complexity ◽  
Mimo Systems ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Semidefinite Relaxation ◽  
Attractive Alternative ◽  
Computationally Efficient ◽  
Multiple Input ◽  
Key Technologies ◽  
Input Multiple Output

Multiple-input multiple-output (MIMO) system is considered to be one of the key technologies of LTE since it achieves requirements of high throughput and spectral efficiency. The semidefinite relaxation (SDR) detection for MIMO systems is an attractive alternative to the optimum maximum likelihood (ML) decoding because it is very computationally efficient. We propose a new SDR detector for 256-QAM MIMO system and compare its performance with two other SDR detectors, namely, BC-SDR detector and VA-SDR detector. The tightness and complexity of these three SDR detectors are analyzed. Both theoretical analysis and simulation results demonstrate that the proposed SDR can provide the best BLER performance among the three detectors, while the BC-SDR detector and the VA-SDR detector provide identical BLER performance. Moreover, the BC-SDR has the lowest computational complexity and the VA-SDR has the highest computational complexity, while the proposed SDR is in between.

scholarly journals Impact of Stair and Diagonal Matrices in Iterative Linear Massive MIMO Uplink Detectors for 5G Wireless Networks

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 71 ◽  
Author(s):  
Mahmoud A. Albreem ◽  
Mohammed H. Alsharif ◽  
Sunghwan Kim
Keyword(s):  
Computational Complexity ◽  
Iterative Methods ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Matrix Inversion ◽  
Multiple Input ◽  
The Matrix ◽  
Input Multiple Output ◽  
The Impact

In massive multiple-input multiple-output (M-MIMO) systems, a detector based on maximum likelihood (ML) algorithm attains optimum performance, but it exhaustively searches all possible solutions, hence, it has a very high complexity and realization is denied. Linear detectors are an alternative solution because of low complexity and simplicity in implementation. Unfortunately, they culminate in a matrix inversion that increases the computational complexity in high loaded systems. Therefore, several iterative methods have been proposed to approximate or avoid the matrix inversion, such as the Neuamnn series (NS), Newton iterations (NI), successive overrelaxation (SOR), Gauss–Siedel (GS), Jacobi (JA), and Richardson (RI) methods. However, a detector based on iterative methods requires a pre-processing and initialization where good initialization impresses the convergence, the performance, and the complexity. Most of the existing iterative linear detectors are using a diagonal matrix ( D ) in initialization because the equalization matrix is almost diagonal. This paper studies the impact of utilizing a stair matrix ( S ) instead of D in initializing the linear M-MIMO uplink (UL) detector. A comparison between iterative linear M-MIMO UL detectors with D and S is presented in performance and computational complexity. Numerical Results show that utilization of S achieves the target performance within few iterations, and, hence, the computational complexity is reduced. A detector based on the GS and S achieved a satisfactory bit-error-rate (BER) with the lowest complexity.

scholarly journals Channel Estimation for MIMO Systems

2022 ◽  
Vol 10 (1) ◽  
pp. 201-204
Author(s):  
Sonti Swapna
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Least Square ◽  
Spectrum Efficiency ◽  
Mean Square ◽  
Multiple Input ◽  
Input Multiple Output

Abstract: A combination of multiple-input multiple-output (MIMO) systems and orthogonal frequency division multiplexing (OFDM) technologies can be employed in modern wireless communication systems to achieve high data rates and improved spectrum efficiency. For multiple input multiple output (MIMO) systems, this paper provides a Rayleigh fading channel estimation technique based on pilot carriers. The channel is estimated using traditional Least Square (LS) and Minimum Mean Square (MMSE) estimation techniques. The MIMO-OFDM system's performance is measured using the Bit Error Rate (BER) and Mean Square Error (MSE) levels. Keywords: MIMO, MMSE, Channel estimation, BER, OFDM

scholarly journals A Low Complexity Channel Estimation and Detection for Massive MIMO Using SC-FDE

Telecom ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 3-17
Author(s):  
Mário Marques da Silva ◽  
Rui Dinis ◽  
João Guerreiro
Keyword(s):  
Channel Estimation ◽  
Spectral Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Data Detection ◽  
Pilot Symbols ◽  
Multiple Input ◽  
Input Multiple Output

5G Communications will support millimeter waves (mm-Wave), alongside the conventional centimeter waves, which will enable much higher throughputs and facilitate the employment of hundreds or thousands of antenna elements, commonly referred to as massive Multiple Input–Multiple Output (MIMO) systems. This article proposes and studies an efficient low complexity receiver that jointly performs channel estimation based on superimposed pilots, and data detection, optimized for massive MIMO (m-MIMO). Superimposed pilots suppress the overheads associated with channel estimation based on conventional pilot symbols, which tends to be more demanding in the case of m-MIMO, leading to a reduction in spectral efficiency. On the other hand, MIMO systems tend to be associated with an increase of complexity and increase of signal processing, with an exponential increase with the number of transmit and receive antennas. A reduction of complexity is obtained with the use of the two proposed algorithms. These algorithms reduce the complexity but present the disadvantage that they generate a certain level of interference. In this article, we consider an iterative receiver that performs the channel estimation using superimposed pilots and data detection, while mitigating the interference associated with the proposed algorithms, leading to a performance very close to that obtained with conventional pilots, but without the corresponding loss in the spectral efficiency.

scholarly journals Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2884 ◽  
Author(s):  
Kai Zhai ◽  
Zheng Ma ◽  
Xianfu Lei
Keyword(s):  
Large Scale ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Path Loss ◽  
Base Station ◽  
Mimo Networks ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Accurate Performance

In this paper, we estimate the uplink performance of large-scale multi-user multiple-input multiple-output (MIMO) networks. By applying minimum-mean-square-error (MMSE) detection, a novel statistical distribution of the signal-to-interference-plus-noise ratio (SINR) for any user is derived, for path loss, shadowing and Rayleigh fading. Suppose that the channel state information is perfectly known at the base station. Then, we derive the analytical expressions for the pairwise error probability (PEP) of the massive multiuser MMSE–MIMO systems, based on which we further obtain the upper bound of the bit error rate (BER). The analytical results are validated successfully through simulations for all cases.

Performance Improvement in Spatially Multiplexed MIMO Systems over Weibull-Gamma Fading Channel

Frequenz ◽  
2016 ◽  
Vol 70 (11-12) ◽  
Author(s):  
Keerti Tiwari ◽  
Davinder S. Saini ◽  
Sunil V. Bhooshan
Keyword(s):  
Interference Cancellation ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Optimal Performance ◽  
Detection Techniques ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Improved Performance ◽  
Ml Detection

AbstractIn multiple-input multiple-output (MIMO) systems, spatial demultiplexing at the receiver has its own significance. Thus, several detection techniques have been investigated. There is a tradeoff between computational complexity and optimal performance in most of the detection techniques. One of the detection techniques which gives improved performance and acceptable level of complexity is ordered successive interference cancellation (OSIC) with minimum mean square error (MMSE). However, optimal performance can be achieved by maximum likelihood (ML) detection but at a higher complexity level. Therefore, MMSE-OSIC with candidates (OSIC

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