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.

scholarly journals An Efficient Precoding Scheme for Millimeter-Wave Massive MIMO Systems

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 927 ◽  
Author(s):  
Alemaishat ◽  
Saraereh ◽  
Khan ◽  
Affes ◽  
Li ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Beam Control ◽  
Low Computational Complexity ◽  
Multiple Input ◽  
Input Multiple Output

Aiming at the problem of high computational complexity due to a large number of antennas deployed in mmWave massive multiple-input multiple-output (MIMO) communication systems, this paper proposes an efficient algorithm for optimizing beam control vectors with low computational complexity based on codebooks for millimeter-wave massive MIMO systems with split sub-arrays hybrid beamforming architecture. A bidirectional method is adopted on the beam control vector of each antenna sub-array both at the transmitter and receiver, which utilizes the idea of interference alignment (IA) and alternating optimization. The simulation results show that the proposed algorithm has low computational complexity, fast convergence, and improved spectral efficiency as compared with the state-of-the-art algorithms.

scholarly journals Deep double-pilot-based hybrid precoding in UAV-enabled mmWave massive MIMO

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Ziyao Hong ◽  
Ting Li ◽  
Fei Li
Keyword(s):  
Deep Learning ◽  
Communication System ◽  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
The Other ◽  
Time Varying ◽  
Hybrid Precoding ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Aerial Vehicle

Abstract Unmanned aerial vehicle (UAV)-enabled communication system provides flexibility and reliability compared to conventional ones. Millimeter wave (mmWave) and massive multiple-input–multiple-output (MIMO) have widely been researched since recent years, which are promising techniques for the next and even the later generation communication system. Hybrid precoding, as a method to reduce the high cost in hardware and power brought by massive antenna array, develops fiercely and is often combined to deep learning, a kind of popular optimization tool, which brings an overwhelming performance. On the other hand, there are not so many attentions about the hybrid precoding in time-varying mmWave massive MIMO, which is necessary to be considered in a UAV-enabled communication scenario because the performance will degrade seriously if the channel changed while the transmitter and receiver use the precoding matrix corresponding to the expired channel, yet. In this paper, we propose a double-pilot-based hybrid precoding system, which completes analog precoding and digital precoding separately—predicting the previous one using deep learning structure and updating equivalent channel frequently for the post one by enhancing the frequency of equivalent channel estimation.

scholarly journals Capacity Analysis and Linear Detection in Massive MIMO for 5G Wireless Systems

2019 ◽  
Vol 8 (9) ◽  
pp. 2938-2943
Keyword(s):  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
Symbol Error Rate ◽  
Base Station ◽  
Matlab Simulation ◽  
Maximum Ratio Combining ◽  
Single Antenna ◽  
Air Interface ◽  
Multiple Input ◽  
Input Multiple Output

Multiple Input Multiple Output (MIMO) is an attractive air interface solution which is used in the 4 th generation wireless networks to achieve higher data rate. With a very large antenna array in Massive MIMO the capacity will increase drastically. In this paper channel capacity comparison for MIMO using known Channel State Information (CSI) and unknown CSI has been carried out for a higher number of antennas at transmitter and receiver side. It has shown that at lower SNR known CSI will give better performance compared to unknown CSI. At higher SNR known CSI and unknown CSI will provide similar results. Capacity comparison has been evaluated with help of MATLAB for known CSI and unknown CSI from a small number of antennas to hundred of antennas. Also, the performance evaluated with MATLAB simulation of linear detectors zero-forcing (ZF) and maximum ratio combining (MRC) method for large number of antennas at Base station (BS) which are serving a small number of single antenna users. Performance is evaluated in terms of Symbol Error Rate (SER) for ZF and MRC, and results show that ZF will outperform MRC. It has also been analyzed that increasing the antennas at BS for a small number of users will also help to reduce SER.

scholarly journals Deep Learning Based Energy Efficient Scheme for Massive MIMO

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1776-1780
Keyword(s):  
Energy Efficiency ◽  
Deep Learning ◽  
Energy Efficient ◽  
Massive Mimo ◽  
Multipath Propagation ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Input Multiple Output ◽  
Efficient Scheme ◽  
Output System

This paper proposes a Deep Learning Energy Efficient Scheme (DLEE) for a massive multiple input multiple output system (MIMO). Massive MIMO is deployed using large number of antennas for multiple users. The proposed DLEE, learns the relationship between spatial beamforming pattern and the power consumption in a base station. In this work, we design a novel learning method where the spatial correlation across UE antennas are taken as input feature vector and find the output labels which give us the energy efficiency in a BS. Due to multipath propagation, other methods only try to address the energy efficiency problem through the bit rate and the power required for the throughput to be efficient. This paper discusses the unsupervised algorithm DLEE which is similar to an autoencoder by combining the power consumed due to radiation pattern through beamforming and the DL framework to address the energy efficiency to an extent of 12% in a BS.

scholarly journals Deep Learning-Based Symbol-Level Precoding for Large-Scale Antenna System

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Changxu Xie ◽  
Huiqin Du ◽  
Xialing Liu
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Back Propagation ◽  
Symbol Error Rate ◽  
Antenna System ◽  
Alternating Direction ◽  
Input Multiple Output ◽  
Deep Learning Network ◽  
Optimum Solution

In this work, we consider a multiple input multiple-output system with large-scale antenna array which creates unintended multiuser interference and increases the power consumption due to the large number of radio frequency (RF) chains. The antenna selective symbol level precoding design is developed by minimizing the symbol error rate (SER) with limits of available RF chains. The ℓ 0 -norm constrained nonconvex problem can be approximated as ℓ 1 -minimization, which is further solved by alternating direction method of multipliers (ADMM) approach. The basic ADMM scheme is mapped into iterative construction process where the optimum solution is obtained by taking deep learning network as building block. Moreover, because that the standard ADMM algorithm is sensitive to the selection of hyperparameters, we further introduce the back propagation process to train the parameters. Simulation results show that the proposed deep learning ADMM scheme can achieve significantly low SER performance with small activated subset of transmit antennas.

scholarly journals Performance Analysis on Spatial MFSK Modulation with Energy Detection

2021 ◽  
Author(s):  
Shuaijun Li ◽  
Hongbing Qiu ◽  
lin zheng ◽  
Chao Yang
Keyword(s):  
Mimo System ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Energy Detection ◽  
Symbol Error Rate ◽  
Spatial Multiplexing ◽  
Optimal Decision ◽  
High Signal ◽  
Mimo Detection ◽  
Input Multiple Output

Abstract Noncoherent multiple-input multiple-output （MIMO）detection in fast fading environments has received attention in recent years since less influence by factors such as phase fluctuations and the low requirements for channel estimation and synchronization. Spatial MFSK modulation with energy detection is different from conventional noncoherent MIMO in that it can obtain higher spatial multiplexing, but with the introduction of the nonlinear square-law operation, the analysis of its detection performance needs to be solved. This paper analyzes the theoretical symbol error rate (SER) performance of the Spatial MFSK modulation with energy detection. The noise of the MIMO system by energy detection conform to the generalized gamma distribution. Based on this distribution, the optimal decision rule of the system and the theoretical SER formula are derived. Numerical results show that the theoretical SER formula fits well with the simulation results of the system under the condition of high signal-to-noise ratio (SNR).

Review on various detectors in massive MIMO technology: a performance analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manju V.M. ◽  
Ganesh R.S.
Keyword(s):  
Performance Analysis ◽  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
Performance Measure ◽  
Research Issues ◽  
Content Type ◽  
Detection Algorithms ◽  
Data Rates ◽  
High Spectral Efficiency ◽  
Input Multiple Output

Purpose Multiple-input multiple-output (MIMO) combined with multi-user massive MIMO has been a well-known approach for high spectral efficiency in wideband systems, and it was targeted to detect the MIMO signals. The increasing data rates with multiple antennas and multiple users that share the communication channel simultaneously lead to higher capacity requirements and increased complexity. Thus, different detection algorithms were developed for the Massive MIMO. Design/methodology/approach This paper focuses on the various literature analyzes on various detection algorithms and techniques for MIMO detectors. Here, it reviews several research papers and exhibits the significance of each detection method. Findings This paper provides the details of the performance analysis of the MIMO detectors and reveals the best value in the case of each performance measure. Finally, it widens the research issues that can be useful for future researchers to be accomplished in MIMO massive detectors Originality/value This paper has presented a detailed review of the detection of massive MIMO on different algorithms and techniques. The survey mainly focuses on different types of channels used in MIMO detections, the number of antennas used in transmitting signals from the source to destination, and vice-versa. The performance measures and the best performance of each of the detectors are described.

scholarly journals Deep Learning Based Analog Beamforming Design for Millimetre Wave Massive MIMO System

2021 ◽  
Author(s):  
Rajdeep Singh Sohal ◽  
Vinit Grewal ◽  
Jaipreet Kaur ◽  
Maninder Lal Singh
Keyword(s):  
Deep Learning ◽  
Massive Mimo ◽  
Large Scale ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Design Procedure ◽  
Base Station ◽  
Small Scale ◽  
Millimetre Wave ◽  
Input Multiple Output

Abstract Analog beamforming (ABF) architectures for both large-scale antennas at base station (BS) and small-scale antennas at user side in millimetre wave (mmWave) channel are constructed and investigated in this paper with the aid of deep learning (DL) techniques. Transmit and receive beamformers are selected through offline training of ABF network that accepts input as channel. The joint optimization of both beamformers based on DL for maximization of spectral efficiency (SE) for massive multiple input multiple output (M-MIMO) system has been employed. This design procedure is carried out under imperfect channel state information (CSI) conditions and the proposed design of precoders and combiners shows robustness to imperfect CSI. The simulation results verify the superiority in terms of SE of deep neutral network (DNN) enabled beamforming (BF) design of mmWave massive MIMO system compared with the conventional BF algorithms while lessening the computational complexity.

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.

A companding approach for PAPR suppression in OFDM based massive MIMO system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Kumar Yadav ◽  
Pritam Keshari Sahoo ◽  
Yogendra Kumar Prajapati
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Base Station ◽  
Convex Optimization Problem ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm

Abstract Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results.

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