Layer extrinsic information transferring (LEXIT) detection algorithm for diversity MIMO systems

2006 ◽  
Author(s):  
Xiumei Yang ◽  
Xiaomei Xia ◽  
Yong Xiong
Keyword(s):  
Mimo Systems ◽  
Detection Algorithm ◽  
Extrinsic Information

An Efficient Detection Algorithm of Pilot Spoofing Attack in Massive MIMO Systems

2021 ◽  
pp. 107962
Author(s):  
Jun. Zeng ◽  
Dachuan. Wang ◽  
Weiyang. Xu ◽  
Bing. Li
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Detection Algorithm ◽  
Efficient Detection

scholarly journals Deep learning-aided high-precision data detection for massive MU-MIMO systems

2021 ◽  
Vol 336 ◽  
pp. 04007
Author(s):  
Sen Yang ◽  
Zerun Li ◽  
Jinhui Wei ◽  
Zuocheng Xing
Keyword(s):  
Mimo Systems ◽  
Detection Algorithm ◽  
Iterative Detection ◽  
Data Detection ◽  
Wireless System ◽  
Precision Data ◽  
Efficient Detection ◽  
High Precision Data ◽  
Complexity Cost ◽  
Iteration Parameters

The data detector for future wireless system needs to achieve high throughput and low bit error rate (BER) with low computational complexity. In this paper, we propose a deep neural networks (DNNs) learning aided iterative detection algorithm. We first propose a convex optimization-based method for calculating the efficient detection of iterative soft output data, and then propose a method for adjusting the iteration parameters using the powerful data driven by DNNs, which achieves fast convergence and strong robustness. The results show that the proposed method can achieve the same performance as the known algorithm at a lower computation complexity cost.

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.

A New Approach of Detection Algorithm for Reducing Computation Complexity of MIMO systems

2016 ◽  
Vol 1 (1) ◽  
pp. 159
Author(s):  
Mohammed Qasim Sulttan
Keyword(s):  
Communication Systems ◽  
High Speed ◽  
Mimo Systems ◽  
Symbol Error Rate ◽  
Detection Algorithm ◽  
Practical Implementation ◽  
High Complexity ◽  
Computation Complexity ◽  
Sphere Decoder ◽  
Manhattan Metric

<p>Multiple-Input Multiple-Output (MIMO) technique is a key technology to strengthen and achieve high-speed and high-throughput wireless communications. . In recent years, it was observed that frequent detecting techniques could improve the performance (e.g., symbol error rate ‘SER’) of different modern digital communication systems. But these systems faced a problem of high complexity for the practical implementation.  To solve the problem of high complexity, this work proposed Frequent Improve K-best Sphere Decoding (FIKSD) algorithm with stopping rule depending on the Manhattan metric. Manhattan metric is proposed to use with FIKSD in order to achieve the lowest complexity. FIKSD is a powerful tool to achieve a high performance close to the maximum likelihood (ML), with less complexity. The simulation results show a good reduction in computation complexity with a cost of slight performance degradation within 1dB; the proposed FIKSD requires 0% to 94% and 82% to 97% less complexity than Improved K-best Sphere Decoder (IKSD) and K-best Sphere Decoder (KSD) respectively. This makes the algorithm more suitable for implementation in wireless communication systems.</p>

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.

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