scholarly journals Computationally Efficient Soft Detection Schemes for Coded Massive MIMO Systems

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 344
Author(s):  
Meixiang Zhang ◽  
Zhi Zhang ◽  
Satya Chan ◽  
Sooyoung Kim
Keyword(s):  
Computational Complexity ◽  
Information Exchange ◽  
Interference Cancellation ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Gram Matrix ◽  
Computationally Efficient ◽  
Detection Scheme ◽  
Soft Detection

This paper presents a computationally efficient soft detection scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme adopts joint iterative detection and decoding (JIDD) methods for their capacity limiting performances. In addition, the minimum mean square error parallel interference cancellation (MMSE-PIC)-based detection scheme is used for soft information exchange. We propose a number of techniques to reduce the computational complexity, while keeping almost the same performance as the conventional ones. First, a technique is proposed to approximate the Gram matrix to a constant valued diagonal matrix. This proposal can lead to elimination of complex matrix inversion process and multiple layer dependent estimations, resulting in huge complexity reduction. Second, compact equations to estimate soft-symbol values for M-ary (quadrature amplitude modulation) QAM are derived. From the investigation example of 2 8 -QAM in this paper, this proposal showed more than two orders of less computations compared to the conventional scheme. The simulation results demonstrate that the proposed method can achieve approximating performance to the conventional method with a largely reduced computational complexity.

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 Low Complexity Angular-Domain Detection for the Uplink of Multi-User mmWave Massive MIMO Systems

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 795
Author(s):  
Xiaoxuan Xia ◽  
Wence Zhang ◽  
Yinkai Fu ◽  
Xu Bao ◽  
Jing Xia
Keyword(s):  
Energy Efficiency ◽  
Computational Complexity ◽  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Digital Processing ◽  
Low Complexity ◽  
Angular Domain

To compromise between the system performance and hardware cost, millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been regarded as an enabling technology for the fifth generation of mobile communication systems (5G). This paper considers a low-complexity angular-domain compressing based detection (ACD) for uplink multi-user mmWave massive MIMO systems, which involves hybrid analog and digital processing. In analog processing, we perform angular-domain compression on the received signal by exploiting the sparsity of the mmWave channel to reduce the dimension of the signal space. In digital processing, the proposed ACD scheme works well with zero forcing (ZF)/maximum ratio combining (MRC)/minimum mean square error (MMSE) detection schemes. The performance analysis of the proposed ACD scheme is provided in terms of achievable rates, energy efficiency and computational complexity. Simulations are carried out and it shows that compared with existing works, the proposed ACD scheme not only reduces the computational complexity by more than 50 % , but also improves the system’s achievable rates and energy efficiency.

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.

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

scholarly journals Low-complexity sparse-aware multiuser detection for large-scale MIMO systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Rong Ran ◽  
Hayoung Oh
Keyword(s):  
Large Scale ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Finite Alphabet ◽  
Multiuser Detectors ◽  
Input Multiple Output ◽  
Error Detector ◽  
Significant Performance

AbstractSparse-aware (SA) detectors have attracted a lot attention due to its significant performance and low-complexity, in particular for large-scale multiple-input multiple-output (MIMO) systems. Similar to the conventional multiuser detectors, the nonlinear or compressive sensing based SA detectors provide the better performance but are not appropriate for the overdetermined multiuser MIMO systems in sense of power and time consumption. The linear SA detector provides a more elegant tradeoff between performance and complexity compared to the nonlinear ones. However, the major limitation of the linear SA detector is that, as the zero-forcing or minimum mean square error detector, it was derived by relaxing the finite-alphabet constraints, and therefore its performance is still sub-optimal. In this paper, we propose a novel SA detector, named single-dimensional search-based SA (SDSB-SA) detector, for overdetermined uplink MIMO systems. The proposed SDSB-SA detector adheres to the finite-alphabet constraints so that it outperforms the conventional linear SA detector, in particular, in high SNR regime. Meanwhile, the proposed detector follows a single-dimensional search manner, so it has a very low computational complexity which is feasible for light-ware Internet of Thing devices for ultra-reliable low-latency communication. Numerical results show that the the proposed SDSB-SA detector provides a relatively better tradeoff between the performance and complexity compared with several existing detectors.

MIMO Detectors: A Comprehensive Performance Analysis

2021 ◽  
Vol 16 (3) ◽  
pp. 24-27
Author(s):  
E. Obi ◽  
B.O. Sadiq ◽  
O.S . Zakariyya ◽  
A. Theresa
Keyword(s):  
Error Rate ◽  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Symbol Error Rate ◽  
Signal To Noise ◽  
High Data ◽  
Noise Ratio ◽  
Better Than

Multiple-input multiple-output (MIMO) systems are increasingly becoming popular due to their ability to multiply data rates without any expansion in the bandwidth. This is critical in this era of high-data rate applications but limited bandwidth. MIMO detectors play an important role in ensuring effective communication in such systems and as such the performance of the following are compared in this paper with respect to symbol error rate (SER) versus signal-to-noise ratio (SNR): maximum likelihood (ML), zero forcing (ZF), minimum mean square error (MMSE) and vertical Bell laboratories layered space time (VBLAST). Results showed that the ML has the best performance as it has the least Symbol Error Rate (SER) for all values of Signal to Noise Ratio (SNR) as it was 91.9% better than MMSE, 99.6% better than VBLAST and 99.8% better than ZF at 20db for a 2x2 antenna configuration., it can also be deduced that the performance increased with increase in number of antenna for all detectors except the V-BLAST detector.

scholarly journals Advanced Noncoherent Detection in Massive MIMO Systems via Digital Beamspace Preprocessing

Telecom ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 211-227
Author(s):  
Stephan Bucher ◽  
Christian Waldschmidt
Keyword(s):  
Mimo Systems ◽  
Near Field ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Base Station ◽  
Coherent Detection ◽  
Noncoherent Detection ◽  
Detection Scheme ◽  
Channel Knowledge ◽  
Residual Performance

Noncoherent detection in massive multiple-input/multiple-output (MIMO) uplink systems provides a low-complexity alternative to its coherent counterpart. Requiring no actual channel knowledge but the per-user induced power at the base station, comparable performance as channel-estimation-based detection can be achieved when the users are located in the near-field of the base station. However, noncoherent detection fails in scenarios where users are in the far-field due to an insufficient capability to separate the users in terms of their spatially induced power. For this purpose, a dielectric lens or an analog beamforming structure can be employed, which are capable to focus the power of the incident waves to a smaller subset of the antennas at the base station. These so-called analog beamspace techniques have been demonstrated to enable again the noncoherent detection scheme. Analogous to a spatial Fourier transform, beamspace techniques can be also realized in the digital domain offering more flexibility. Its applicability to noncoherent detection is studied in this paper. It is shown numerically that by means of digital beamspace preprocessing, considerable performance gains can be achieved. Applied in dominant line-of-sight channels, a large number of users can be accommodated and the residual performance gap to coherent detection with perfect channel knowledge is minimal.

scholarly journals An Improved Jacobi-Based Detector for Massive MIMO Systems

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 165 ◽  
Author(s):  
Xiaoqing Zhao ◽  
Zhengquan Li ◽  
Song Xing ◽  
Yang Liu ◽  
Qiong Wu ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Correction Method ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Input Multiple Output ◽  
Mmse Detector

Massive multiple-input-multiple-output (MIMO) is one of the key technologies in the fifth generation (5G) cellular communication systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) presents a near-optimal performance. Due to the required direct matrix inverse, however, the MMSE detection algorithm becomes computationally very expensive, especially when the number of users is large. For achieving the high detection accuracy as well as reducing the computational complexity in massive MIMO systems, we propose an improved Jacobi iterative algorithm by accelerating the convergence rate in the signal detection process.Specifically, the steepest descent (SD) method is utilized to achieve an efficient searching direction. Then, the whole-correction method is applied to update the iterative process. As the result, the fast convergence and the low computationally complexity of the proposed Jacobi-based algorithm are obtained and proved. Simulation results also demonstrate that the proposed algorithm performs better than the conventional algorithms in terms of the bit error rate (BER) and achieves a near-optimal detection accuracy as the typical MMSE detector, but utilizing a small number of iterations.

scholarly journals PCA-Aided Linear Precoding in Massive MIMO Systems with Imperfect CSI

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Van-Khoi Dinh ◽  
Minh-Tuan Le ◽  
Vu-Duc Ngo ◽  
Chi-Hieu Ta
Keyword(s):  
Principal Component Analysis ◽  
Computational Complexity ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Linear Precoding ◽  
Block Diagonalization

In this paper, a low-complexity linear precoding algorithm based on the principal component analysis technique in combination with the conventional linear precoders, called Principal Component Analysis Linear Precoder (PCA-LP), is proposed for massive MIMO systems. The proposed precoder consists of two components: the first one minimizes the interferences among neighboring users and the second one improves the system performance by utilizing the Principal Component Analysis (PCA) technique. Numerical and simulation results show that the proposed precoder has remarkably lower computational complexity than its low-complexity lattice reduction-aided regularized block diagonalization using zero forcing precoding (LC-RBD-LR-ZF) and lower computational complexity than the PCA-aided Minimum Mean Square Error combination with Block Diagonalization (PCA-MMSE-BD) counterparts while its bit error rate (BER) performance is comparable to those of the LC-RBD-LR-ZF and PCA-MMSE-BD ones.

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