Performance Improvement of Multi-User Multiple-Input Multiple-Output Protocol for WLAN

The increase in the number of devices with a massive revolution in mobile technology leads to increase the capacity of the wireless communications net-works. Multi-user Multiple-Input Multiple-Output is an advanced procedure of Multiple-Input Multiple-Output, which improves the performance of Wireless Local Area Networks. Moreover, Multi-user Multiple-Input Multiple-Output leads the Wireless Local Area Networks toward covering more areas. Due to the growth of the number of clients and requirements, researchers try to improve the performance of the Medium Access Control protocol of Multi-user Multiple-Input Multiple-Output technology to serve the user better, by supporting different data sizes, and reducing the waiting time to be able to transmit data quickly. In this paper, we propose a Clustering Multi-user Multiple-Input Multiple-Output protocol, which is an improved Medium Access Control protocol for Multi-user Multiple-Input Multiple-Out-put based on MIMOMate clustering technique and Padovan Backoff Algorithm. Utilizing MIMOMMate focuses on the signal power which only serves the user in that cluster, minimizes the energy consumption and increases the capacity. The implementation of Clustering Multi-user Multiple-Input Multiple-Output performs on the Network Simulator (NS2.34) platform. The results show that Clustering Multi-user Multiple-Input Multiple-Output protocol improves the throughput by 89.8%, and reduces the latency of wireless communication by 43.9% in scenarios with contention. As a result, the overall performances of the network are improved.

Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.

A new layered space-time detection algorithm for frequency selective fading multiple-input multiple-output channels based on particle filter

In this paper, a new observation equation of non-Gaussian frequency selective fading Bell Labs layered space time （BLAST） architecture system is proposed, which is used for frequency selective fading channels and non-Gaussian noise in an application environment of BLAST system. With othogonal matrix triangularization （QR decomposition） of the channel matrix, the static observation equation of frequency selective fading BLAST system is transformed into a dynamic state space model, and then the particle filter is used for space-time layered detection. Making the full use of the finite alphabet of the digital modulation communication signal, the optimal proposal distribution can be chosen to produce particle and update the weight. Incorporated with current method of reducing error propagation, a new space-time layered detection algorithm is proposed. Simulation result shows the validity of the proposed algorithm.

Shannon information capacity of time reversal wideband multiple-input multiple-output system based on correlated statistical channels

Utilizing channel reciprocity, time reversal（TR） technique increases the signal-to-noise ratio（SNR） at the receiver with very low transmitter complexity in complex multipath environment. Present research works about TR multiple-input multiple-output（MIMO） communication all focus on the system implementation and network building. The aim of this work is to analyze the influence of antenna coupling on the capacity of wideband TR MIMO system, which is a realistic question in designing a practical communication system. It turns out that antenna coupling stabilizes the capacity in a small variation range with statistical wideband channel response. Meanwhile, antenna coupling only causes a slight detriment to the channel capacity in a wideband TR MIMO system. Comparatively, uncorrelated stochastic channels without coupling exhibit a wider range of random capacity distribution which greatly depends on the statistical channel. The conclusions drawn from information difference entropy theory provide a guideline for designing better high-performance wideband TR MIMO communication systems.

SEMI-BLIND CHANNEL ESTIMATION OF MULTIPLE-INPUT/MULTIPLE-OUTPUT SYSTEMS BASED ON MARKOV CHAIN MONTE CARLO METHODS

This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.

Joint Channel and Multi-User Detection Empowered with Machine Learning

The numbers of multimedia applications and their users increase with each passing day.Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems.In this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two stages.The first stage estimates the channel parameters,and the second performsmulti-user detection.The proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural networks.This study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO receivers.The FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

Turbo Message Passing Based Burst Interference Cancellation for Data Detection in Massive MIMO-OFDM Systems

This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation(TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.

Low-complexity signal detection for massive MIMO systems via trace iterative method

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

Direction finding of bistatic MIMO radar in strong impulse noise

For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.

Differential Spatial Modulation Mapping Algorithms

Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.

Near-Field Beam Training for Holographic MIMO Communications: Typical Methods, Challenges and Future Directions

Holographic multiple-input multiple-output(HMIMO)has become an emerging technology for achieving ultra-high frequency spectral efficiency and spatial resolution in future wireless systems.The increasing antenna aperture leads to a more significant characterization of the spherical wavefront in near-field communications in HMIMO scenarios.Beam training as a key technique for wireless communication is worth exploring in this near-field scenario.Compared with the widely researched far-field beam training,the increased dimensionality of the search space for near-field beam training poses a challenge to the complexity and accuracy of the proposed algorithm.In this paper,we introduce several typical near-field beam training methods:exhaustive beam training,hierarchical beam training,and multi-beam training that includes equal interval multi-beam training and hash multi-beam training.The performances of these methods are compared through simulation analysis,and their effectiveness is verified on the hardware testbed as well.Additionally,we provide application scenarios,research challenges,and potential future research directions for near-field beam training.

APES算法在MIMO雷达参数估计中的稳健性研究

多输入多输出(MIMO,Multiple-Input Multiple-Output)雷达用多个发射天线同时发射多个独立信号照射目标,并使用多个接收天线接收目标回波信号.本文研究了MIMO雷达中参数估计的稳健性问题.本文应用幅度相位估计(APES,Amplitude and Phase EStimation)技术,利用目标的方位角最大似然估计值,得到了衰落向量的APES估计算法.考虑到方位角估计的不准确性,借鉴稳健的Capon波束形成器的设计思想,本文推导了衰落向量的稳健的APES估计算法.仿真实验表明,衰落向量的APES算法与稳健的APES算法性能十分接近.因此,衰落向量的APES估计算法是稳健的.

一种小型化超宽带MIMO天线设计

提出了一种基于槽天线的小型化、高隔离度的超宽带（Ultra Wideband,UWB）多入多出（Multiple-Input Multiple-Output,MIMO）天线.该MIMO天线由两个槽天线单元构成,为了增加天线阻抗带宽,每个槽天线单元由末端带有圆形贴片的微带线和末端为圆形的槽线两部分耦合馈电.采用在地板上开槽和方向图分集方法,减少地板表面波和空中电磁波影响,达到提高天线隔离度的目的.数值仿真和实验结果表明：该天线在3.1～11GHz频段内满足端口反射系数｜S11｜〈-10dB,隔离度｜S12｜在7～11GHz频段内小于-25dB,在3.1～7GHz频段内小于-16dB,并根据仿真和测试S参数计算了包络相关系数.

基于单通道MIMO雷达的毫米波安检成像

毫米波多入多出(MIMO)雷达是一种前沿的安检成像技术。然而,由于采用了大量的发射/接收通道,MIMO雷达的成本和复杂度大大高于传统的单通道雷达。为了研制低成本、低复杂度的毫米波MIMO安检成像雷达,引入了双重码分复用技术。该方法可通过单发射/接收通道实现与传统多通道MIMO雷达相似的成像性能,对安检成像领域的发展和应用具有重要意义。此外,设计了基于失配滤波器理论的解复用码,其可在干扰条件下实现最大码分集增益。进行了仿真与实验以验证所提出方法的正确性与有效性,得到了满意的结果。

Limited feedback SDMA schemes based on statistical CSI

This paper investigates the multi-beam selection algorithms for transmit correlation channels by using statistical channel state information （SCSI） and instantaneous channel state information. Unlike the conventional codebook-based transmission scheme, the proposed multi-beam selection with the single channel quality indicator （CQI） feedback （MBS- SCF） algorithm determines the preferred beam vector by exploiting the SCSI and only feeds back CQI at each timeslot. The performance of the MBS-SCF algorithm is nearly the same as that of the conventional scheme. In order to further improve the average sum rate, a novel multi-beam selection with the dual CQIs feedback （MBS-DCF） algorithm is proposed, which determines dual preferred statistical eigen- directions and feeds back dual CQIs at each timeslot. The theoretical analysis and simulation results demonstrate that the MBS-DCF algorithm can increase the multiuser diversity and multiplexing gain and exhibits a higher average sum rate.

Low complexity iterative differential decoding algorithm for multiband UWB communication systems

Due to not requiring channel state information （CSI） at both the transmitter and the receiver, noncoherent ultra-wideband （UWB） incurs a performance penalty of approximately 3 dB in the required signal to noise ratio （SNR） compared to the coherent case. To overcome the gap, an effective differential encoding and decoding scheme for multiband UWB systems is proposed. The proposed scheme employs the parallel concatenation of two recursive differential unitary space-frequency encoders at the transmitter. At the receiver, two component decoders iteratively decode information bits by interchanging soft metric values between each other. To reduce the computation complexity, a decoding algorithm which only uses transition probability to calculate the log likelihood ratios （LLRs） for the decoded bits is given. Simulation results show that the proposed scheme can dramatically outperform the conventional differential and even coherent detection at high SNR with a few iterations.

Uplink capacity analysis of single-user SA-MIMO system

A novel framework of which combines smart antennas multiple antenna systems, （SA） with multiple-input multiple-output （MIMO） at the receiver, is proposed. The uplink SA-MIMO system is investigated. The joint optimization problem corresponding to the uplink capacity of the single-user SA-MIMO system is deduced. Then the closedform expression of the capacity is obtained in the case of equal power allocation and the same direction-of-arrivals （DOAs） from different transmit antennas at the same antenna array, and an upper bound of the capacity is also given in the case of different DOAs at the same antenna array. After that, for the general case, a suboptimal method for the capacity optimization problem is presented. Some numerical results are also given to compare the capacities of conventional MIMO and SA-MIMO systems and show that the proposed method is viable.

基于MIMO技术的矿井无线通信系统的设计

文章分析了煤矿井下无线通信系统存在的主要问题,介绍了MIMO技术和正交空时分组编码,针对矿井复杂的传输环境,设计了两发多收的矿井无线通信系统,选择了适合多天线技术的正交空时分组编码技术,同时接收端进行最大似然译码。仿真结果表明,采用M IMO技术能有效的降低误码率,提高矿井无线通信系统的可靠性。

基于DCS的统计MIMO雷达信号模型及参数估计

分布式压缩感知(Distributed Compressed Sensing,DCS)将单信号的压缩采样扩展到信号群的压缩采样,利用信号内相关性和互相关性对多个信号进行联合重构。统计多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达系统通过多发多收配置,在发射机、目标以及接收机之间构成对目标的分布式探测系统。该文将DCS应用到统计MIMO雷达中,通过对该场景中目标回波的延时在距离空间稀疏性的分析,提出联合所有接收信号重构目标场景的设想,建立了接收信号的联合稀疏模型,并实现了目标参数估计的联合重构算法。仿真结果表明与基于压缩感知(Compressed Sensing,CS)的算法相比,基于DCS的算法在进一步降低采样数目的同时提高了参数估计精度,同时也验证了DCS-MIMO雷达可以有效克服目标的雷达散射截面积(Radar Cross Section,RCS)起伏。

基于延迟输出反馈的上行多小区MIMO蜂窝干扰网络自由度分析

针对具有反馈时延的上行多小区MIMO(Multiple-Input Multiple-Output)蜂窝干扰网络,提出一种基于延迟输出反馈(OF,Output Feedback)的回溯干扰重构(RIR,Retrospective Interference Reconstruction)方案.该方案首先通过在基站构造接收波束成形矩阵,将重构的OF下传至用户.然后用户对延迟的OF预编码并传输至基站,基站利用预编码信息消除小区间干扰.分析了上行任意多小区MIMO蜂窝干扰网络的RIR方案适用条件、系统和速率和可达自由度(Do F,Degrees of Freedom),并将RIR方案与回溯干扰对齐方案和TDMA方案进行对比仿真分析,结果表明,RIR方案能获得更多自由度.