Reducing Computational Complexity and Enhancing Performance of IKSD Algorithm for Encoded MIMO Systems

2016 ◽  
Vol 2 (3) ◽  
pp. 636 ◽  
Author(s):  
Mohammed Qasim Sulttan
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
High Performance ◽  
Mimo Systems ◽  
Practical Implementation ◽  
Mimo Detection ◽  
Detection Algorithms ◽  
Sphere Detection ◽  
Manhattan Metric ◽  
Main Challenge

<p>The main challenge in MIMO systems is how to design the MIMO detection algorithms with lowest computational complexity and high performance that capable of accurately detecting the transmitted signals. In last valuable research results, it had been proved the Maximum Likelihood Detection (MLD) as the optimum one, but this algorithm has an exponential complexity especially with increasing of a number of transmit antennas and constellation size making it an impractical for implementation. However, there are alternative algorithms such as the K-best sphere detection (KSD) and Improved K-best sphere detection (IKSD) which can achieve a close to Maximum Likelihood (ML) performance and less computational complexity. In this paper, we have proposed an enhancing IKSD algorithm by adding the combining of column norm ordering (channel ordering) with Manhattan metric to enhance the performance and reduce the computational complexity. The simulation results show us that the channel ordering approach enhances the performance and reduces the complexity, and Manhattan metric alone can reduce the complexity. Therefore, the combined channel ordering approach with Manhattan metric enhances the performance and much reduces the complexity more than if we used the channel ordering approach alone. So our proposed algorithm can be considered a feasible complexity reduction scheme and suitable for practical implementation.</p>

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 Low-Complexity Detection Algorithms for Spatial Modulation MIMO Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Xinhe Zhang ◽  
Yuehua Zhang ◽  
Chang Liu ◽  
Hanzhong Jia
Keyword(s):  
Computational Complexity ◽  
Mimo Systems ◽  
Optimal Solution ◽  
Low Complexity ◽  
Search Tree ◽  
Spatial Modulation ◽  
Algorithm Analysis ◽  
Detection Algorithms ◽  
Search Order ◽  
Ber Performance

In this paper, the authors propose three low-complexity detection schemes for spatial modulation (SM) systems based on the modified beam search (MBS) detection. The MBS detector, which splits the search tree into some subtrees, can reduce the computational complexity by decreasing the nodes retained in each layer. However, the MBS detector does not take into account the effect of subtree search order on computational complexity, and it does not consider the effect of layers search order on the bit-error-rate (BER) performance. The ost-MBS detector starts the search from the subtree where the optimal solution is most likely to be located, which can reduce total searches of nodes in the subsequent subtrees. Thus, it can decrease the computational complexity. When the number of the retained nodes is fixed, which nodes are retained is very important. That is, the different search orders of layers have a direct influence on BER. Based on this, we propose the oy-MBS detector. The ost-oy-MBS detector combines the detection order of ost-MBS and oy-MBS together. The algorithm analysis and experimental results show that the proposed detectors outstrip MBS with respect to the BER performance and the computational complexity.

scholarly journals Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8200
Author(s):  
Jonathan Aguiar Soares ◽  
Kayol Soares Mayer ◽  
Fernando César Comparsi de Castro ◽  
Dalton Soares Arantes
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
Cost Effective ◽  
Performance Comparison ◽  
Practical Implementation ◽  
Limiting Factor ◽  
Rbf Neural Networks ◽  
Maximum Likelihood Decoding ◽  
Mimo Channels ◽  
Mimo Ofdm

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

REDUCING THE COMPUTATIONAL COMPLEXITY OF SIGNAL DETECTION IN MIMO SYSTEMS

2021 ◽  
Author(s):  
М.Г. БАКУЛИН ◽  
Т.Б.К. БЕН РАЖЕБ ◽  
В.Б. КРЕЙНДЕЛИН ◽  
А.Э. СМИРНОВ
Keyword(s):  
Computational Complexity ◽  
Signal Detection ◽  
Massive Mimo ◽  
Communication Systems ◽  
Noise Immunity ◽  
Mimo Systems ◽  
Detection Algorithm ◽  
Wireless Communication Systems ◽  
Detection Algorithms ◽  
Mimo Technology

С развитием технологии MIMO и появлением технологии massive MIMO возросла сложность обработки сигнала на приемной стороне. Применение известных алгоритмов детектирования сигнала на приемной стороне становится трудно реализуемым из-за высокой вычислительной сложности. Предлагается новая реализация известного алгоритма МСКО, которая позволяет снизить вычислительную сложность детектирования без потерь в помехоустойчивости в системах беспроводной связи, использующих технологию massive MIMO. With the development of MIMO technology and the appearance of the massive MIMO technology, the computational complexity of signal processing on the receiving side has increased. The application of known signal detection algorithms used in MIMO systems becomes difficult or even impossible to implement in massive MIMO systems because of computational complexity. We offer a new realization technique of the well-known MMSE detection algorithm with less computational complexity and without any loss in noise immunity in wireless communication systems using massive MIMO technology.

scholarly journals Computationally Efficient Multi-Antenna Techniques for Multi-User Two-Way Wireless Relay Networks

2018 ◽  
Vol 8 (3) ◽  
pp. 1684 ◽  
Author(s):  
Samer Alabed
Keyword(s):  
Computational Complexity ◽  
Maximum Likelihood ◽  
High Performance ◽  
Relay Networks ◽  
Symbol Error Rate ◽  
Wireless Relay Networks ◽  
Computationally Efficient ◽  
Low Computational Complexity ◽  
Wireless Relay ◽  
And Performance

In this work, we are interested in implementing, developing and evaluating multi-antenna techniques used for multi-user two-way wireless relay networks that provide a good tradeoff between the computational complexity and performance in terms of symbol error rate and achievable data rate. In particular, a variety of newly multi-antenna techniques is proposed and studied. Some techniques based on orthogonal projection enjoy low computational complexity. However, the performance penalty associated with them is high. Other techniques based on maximum likelihood strategy enjoy high performance, however, they suffer from very high computational complexity. The Other techniques based on randomization strategy provide a good trade-off between the computational complexity and performance where they enjoy low computational complexity with almost the same performance as compared to the techniques based on maximum likelihood strategy.

scholarly journals A System View on Iterative MIMO Detection: Dynamic Sphere Detection versus Fixed Effort List Detection

VLSI Design ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Christina Gimmler-Dumont ◽  
Frank Kienle ◽  
Bin Wu ◽  
Guido Masera
Keyword(s):  
Communication Systems ◽  
Closed Loop ◽  
Mimo Systems ◽  
Open Loop ◽  
Spatial Multiplexing ◽  
Loop System ◽  
Mimo Detection ◽  
Closed Loop System ◽  
Closed Loop Systems ◽  
Sphere Detection

Multiple-antenna systems are a promising approach to increase the data rate of wireless communication systems. One efficient possibility is spatial multiplexing of the transmitted symbols over several antennas. Many different MIMO detector algorithms exist for this spatial multiplexing. The major difference between different MIMO detectors is the resulting communications performance and implementation complexity, respectively. Particularly closed-loop MIMO systems have attained a lot of attention in the last years. In a closed-loop system, reliability information is fed back from the channel decoder to the MIMO detector. In this paper, we derive a basic framework to compare different soft-input soft-output MIMO detectors in open- and closed-loop systems. Within this framework, we analyze a depth-first sphere detector and a breadth-first fixed effort detector for different application scenarios and their effects on area and energy efficiency on the whole system. We present all system components under open- and closed-loop system aspects and determine the overall implementation cost for changing an open-loop system in a closed-loop system.

scholarly journals Multisegment Mapping Network for Massive MIMO Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yongzhi Yu ◽  
Jianming Wang ◽  
Limin Guo
Keyword(s):  
Deep Learning ◽  
Computational Complexity ◽  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
Activation Function ◽  
Symbol Error Rate ◽  
Mimo Detection ◽  
Residual Structure ◽  
Detection Algorithms ◽  
Input Multiple Output

The massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a significant challenge to traditional detection algorithms. However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). MsNet is obtained by optimizing the prior detection networks that are termed as DetNet and ScNet. MsNet further simplifies the sparse connection structure and reduces network complexity, which also changes the coefficients of the residual structure in the network into trainable variables. In addition, this paper designs an activation function to improve the performance of massive MIMO detection in high-order modulation scenarios. The simulation results show that MsNet has better symbol error rate (SER) performance and both computational complexity and the number of training parameters are significantly reduced.

Performance analysis of various massive MIMO detection algorithms in 5G Wireless technologies

2019 ◽  
Vol 10 ◽  
Author(s):  
Shihab Jimaa ◽  
Jawahir Al-Ali
Keyword(s):  
Massive Mimo ◽  
Spectrum Efficiency ◽  
System Capacity ◽  
Network Coverage ◽  
Mimo Detection ◽  
Infinity Norm ◽  
Detection Algorithms ◽  
Alternating Direction ◽  
5G Systems ◽  
Efficient Power

Background: The 5G will lead to a great transformation in the mobile telecommunications sector. Objective: The huge challenges being faced by wireless communications such as the increased number of users have given a chance for 5G systems to be developed and considered as an alternative solution. The 5G technology will provide a higher data rate, reduced latency, more efficient power than the previous generations, higher system capacity, and more connected devices. Method: It will offer new different technologies and enhanced versions of the existing ones, as well as new features. 5G systems are going to use massive MIMO (mMIMO), which is a promising technology in the development of these systems. Furthermore, mMIMO will increase the wireless spectrum efficiency and improve the network coverage. Result: In this paper we present a brief survey on 5G and its technologies, discuss the mMIMO technology with its features and advantages, review the mMIMO capacity and energy efficiency and also presents the recent beamforming techniques. Conclusion: Finally, simulation of adopting different mMIMO detection algorithms are presented, which shows the alternating direction method of multipliers (ADMM)-based infinity-norm (ADMIN) detector has the best performance.

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