scholarly journals Low-Complexity Soft-Output Signal Detection Based on Improved Kaczmarz Iteration Algorithm for Uplink Massive MIMO System

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1564
Author(s):  
Hebiao Wu ◽  
Bin Shen ◽  
Shufeng Zhao ◽  
Peng Gong
Keyword(s):  
Signal Detection ◽  
Output Signal ◽  
Mimo Systems ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Base Station ◽  
Matrix Inversion ◽  
Iteration Algorithm

For multi-user uplink massive multiple input multiple output (MIMO) systems, minimum mean square error (MMSE) criterion-based linear signal detection algorithm achieves nearly optimal performance, on condition that the number of antennas at the base station is asymptotically large. However, it involves prohibitively high complexity in matrix inversion when the number of users is getting large. A low-complexity soft-output signal detection algorithm based on improved Kaczmarz method is proposed in this paper, which circumvents the matrix inversion operation and thus reduces the complexity by an order of magnitude. Meanwhile, an optimal relaxation parameter is introduced to further accelerate the convergence speed of the proposed algorithm and two approximate methods of calculating the log-likelihood ratios (LLRs) for channel decoding are obtained as well. Analysis and simulations verify that the proposed algorithm outperforms various typical low-complexity signal detection algorithms. The proposed algorithm converges rapidly and achieves its performance quite close to that of the MMSE algorithm with only a small number of iterations.

scholarly journals A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 980 ◽  
Author(s):  
Hui Feng ◽  
Xiaoqing Zhao ◽  
Zhengquan Li ◽  
Song Xing
Keyword(s):  
Computational Complexity ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Estimation Algorithm ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Base Station ◽  
Damping Factor ◽  
Mimo Detection ◽  
Discrete Estimation

In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems. MIDE is a revision of the alternating direction method of multipliers (ADMM)-based algorithm, in which a self-updating method is designed with the damping factor estimated and updated at each iteration based on the Euclidean distance between the iterative solutions of the IDE-based algorithm in order to accelerate the algorithm’s convergence. Compared to the existing ADMM-based detection algorithm, the overall computational complexity of the proposed MIDE algorithm is reduced from O N t 3 + O N r N t 2 to O N t 2 + O N r N t in terms of the number of complex-valued multiplications, where Ntand Nr are the number of users and the number of receiving antennas at the base station (BS), respectively. Simulation results show that the proposed MIDE algorithm performs better in terms of the bit error rate (BER) than some recently-proposed approximation algorithms in MIMO detection of uplink massive MIMO systems.

scholarly journals ISSOR Signal Detection for Energy Efficient and Low Complexity Large Scale MIMO System

2019 ◽  
Vol 9 (1S5) ◽  
pp. 327-334
Keyword(s):  
Signal Detection ◽  
Energy Efficient ◽  
Communication Systems ◽  
Large Scale ◽  
Mimo System ◽  
Low Complexity ◽  
High Energy ◽  
Base Station ◽  
User Equipment ◽  
Multiple User

Scalable version of multiuser MIMO called Large-scale MIMO is a one of the powerful technology in future wireless communication systems in which huge amount of BS (base station) antennas utilized to process multiple user equipment. Energy consumed is high with more antennas and also it leads to increase the signal detection complexity and overall circuit power consumption. Designing energy efficient and low complexity MIMO system is considered as a challenging issue. This paper presents the ISSOR signal detection for energy efficient and low complexity large scale MIMO system. VA-GSM (Variable Antenna Generalized spatial modulation) is used in which the number of active antenna transmissions are varied for every transmission in the large scale MIMO. In transmitter side, Eigen value based approach is used for antenna selection. Then, improved symmetric successive over relaxation (ISSOR) approach is proposed for low complexity signal detection in receiver side. The number of user equipment, transmit power, as well as the amount of antennas at the base station, are considered as the optimal system parameters which are chosen for enhancing the efficiency of utilized energy in the system. The proposed scheme implemented in MATLAB software. The proposed scheme attained the high energy efficiency compared to other approaches. Moreover, the BER is utilized to estimate the performance of an offered algorithm and also compared to the previously determined algorithm of existing literatures.

scholarly journals Modified Conjugate Gradient Algorithms for Gram Matrix Inversion of Massive MIMO Downlink Linear Precoding

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2834-2840
Keyword(s):  
Conjugate Gradient ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Mimo System ◽  
Low Complexity ◽  
Matrix Inversion ◽  
Gram Matrix ◽  
Linear Precoding ◽  
Inversion Algorithm ◽  
The Matrix

This paper deals with various low complexity algorithms for higher order matrix inversion involved in massive MIMO system precoder design. The performance of massive MIMO systems is optimized by the process of precoding which is divided into linear and nonlinear. Nonlinear precoding techniques are most complex precoding techniques irrespective of its performance. Hence, linear precoding is generally preferred in which the complexity is mainly contributed by matrix inversion algorithm. To solve this issue, Krylov subspace algorithm such as Conjugate Gradient (CG) was considered to be the best choice of replacement for exact matrix inversions. But CG enforces a condition that the matrix needs to be Symmetric Positive Definite (SPD). If the matrix to be inverted is asymmetric then CG fails to converge. Hence in this paper, a novel approach for the low complexity inversion of asymmetric matrices is proposed by applying two different versions of CG algorithms- Conjugate Gradient Squared (CGS) and Bi-conjugate Gradient (Bi-CG). The convergence behavior and BER performance of these two algorithms are compared with the existing CG algorithm. The results show that these two algorithms outperform CG in terms of convergence speed and relative residue.

scholarly journals Normalized Structured Compressed Sensing Based Signal Detection in Spatial Modulation 3D-MIMO Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Wei Ren ◽  
Guan Gui ◽  
Fei Li
Keyword(s):  
Compressed Sensing ◽  
Signal Detection ◽  
Communication Systems ◽  
Mimo Systems ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Iteration Process ◽  
High Energy ◽  
Spatial Modulation ◽  
3D Mimo

Signal detection is one of the fundamental problems in three-dimensional multiple-input multiple-output (3D-MIMO) wireless communication systems. This paper addresses a signal detection problem in 3D-MIMO system, in which spatial modulation (SM) transmission scheme is considered due to its advantages of low complexity and high-energy efficiency. SM based signal transmission typically results in the block-sparse structure in received signals. Hence, structured compressed sensing (SCS) based signal detection is proposed to exploit the inherent block sparsity information in the received signal for the uplink (UL). Moreover, normalization preprocessing is considered before iteration process with the purpose of preventing the noise from being overamplified by the column vector with inadequately large elements. Simulation results are provided to show the stable and reliable performance of the proposed algorithm under both Gaussian and non-Gaussian noise, in comparison with methods such as compressed sensing based detectors, minimum mean square error (MMSE), and zero forcing (ZF).

scholarly journals Low-Complexity Soft-Output Signal Detection Based on Gauss–Seidel Method for Uplink Multiuser Large-Scale MIMO Systems

2015 ◽  
Vol 64 (10) ◽  
pp. 4839-4845 ◽  
Author(s):  
Linglong Dai ◽  
Xinyu Gao ◽  
Xin Su ◽  
Shuangfeng Han ◽  
Chih-Lin I ◽  
...  
Keyword(s):  
Signal Detection ◽  
Output Signal ◽  
Large Scale ◽  
Mimo Systems ◽  
Low Complexity ◽  
Seidel Method

A Low-Complexity Soft-Output Signal Data Detection Algorithm for UL Massive MIMO Systems

2021 ◽  
Author(s):  
Salah Berra ◽  
Mahmoud A. M. Albreem ◽  
Maha Malek ◽  
Rui Dinis ◽  
Xingwang Li ◽  
...  
Keyword(s):  
Output Signal ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Data Detection

scholarly journals Design, Analysis of 4×4 and 8×8 MIMO System with ZF, MMSE and BF Techniques

2017 ◽  
Vol 17 (2) ◽  
pp. 16-19 ◽  
Author(s):  
Arun Kumar ◽  
Piyush Vardhan ◽  
Manisha Gupta
Keyword(s):  
Signal Detection ◽  
Mimo System ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Digital Signal ◽  
Base Station ◽  
Beam Forming ◽  
High Signal ◽  
Zero Forcing ◽  
Signal Processors

AbstractThis work focuses on studying signal detection using three different equalization techniques, namely: Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Beam Forming (BF), for a 4×4 MIMO-system. Results show that ZF equalization is the simplest technique for signal detection, However, Beam Forming (BF) gives better Bit Error Rate (BER) performances at high Signal to Noise Ratio (SNR) values with some complexity in design. For more antennas at the base station, it is too complex to design the weight matrix for ZF, however, it is suitable for BF with the help of good quality digital signal processors. Performance of MIMO-system, with 8 antennas at the base station using BF equalization, is analysed to get BER values at different SNR. Results show a considerable improvement in BER for 8 antennas at the base station.

scholarly journals An Efficient Precoding Algorithm for mmWave Massive MIMO Systems

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1099 ◽  
Author(s):  
Imran Khan ◽  
Shagufta Henna ◽  
Nasreen Anjum ◽  
Aduwati Sali ◽  
Jonathan Rodrigues ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Matching Pursuit ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Global Optimum ◽  
Vital Role ◽  
Matrix Inversion ◽  
Inner Product

Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal matching pursuit (OMP) has a large complexity, as it requires matrix inversion and known candidate matrices. Therefore, we propose a bird swarm algorithm (BSA) based matrix-inversion bypass (MIB) OMP (BSAMIBOMP) algorithm which has the feature to quickly search the BSA global optimum value. It only directly finds the array response vector multiplied by the residual inner product, so it does not require the candidate’s matrices. Moreover, it deploys the Banachiewicz–Schur generalized inverse of the partitioned matrix to decompose the high-dimensional matrix into low-dimensional in order to avoid the need for a matrix inversion operation. The simulation results show that the proposed algorithm effectively improves the bit error rate (BER), spectral efficiency (SE), complexity, and energy efficiency of the mmWave massive MIMO system as compared with the existing OMP hybrid and SDRAltMin algorithm without any matrix inversion and known candidate matrix information requirement.

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.

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