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.

A Low-Complexity Signal Detection Utilizing AOR Iterative Method for Massive MIMO Systems

Massive multiple-input multiple-output(MIMO) system is capable of substantially improving the spectral efficiency as well as the capacity of wireless networks relying on equipping a large number of antenna elements at the base stations. However, the excessively high computational complexity of the signal detection in massive MIMO systems imposes a significant challenge for practical hardware implementations. In this paper, we propose a novel minimum mean square error(MMSE) signal detection using the accelerated overrelaxation(AOR) iterative method without complicated matrix inversion, which is capable of reducing the overall complexity of the classical MMSE algorithm by an order of magnitude. Simulation results show that the proposed AOR-based method can approach the conventional MMSE signal detection with significant complexity reduction.

Energy-efficient resource management for CCFD massive MIMO systems in 6G networks

This paper presents a co-time co-frequency fullduplex(CCFD)massive multiple-input multiple-output(MIMO)system to meet high spectrum efficiency requirements for beyond the fifth-generation(5G)and the forthcoming the sixth-generation(6G)networks.To achieve equilibrium of energy consumption,system resource utilization,and overall transmission capacity,an energy-efficient resource management strategy concerning power allocation and antenna selection is designed.A continuous quantum-inspired termite colony optimization(CQTCO)algorithm is proposed as a solution to the resource management considering the communication reliability while promoting energy conservation for the CCFD massive MIMO system.The effectiveness of CQTCO compared with other algorithms is evaluated through simulations.The results reveal that the proposed resource management scheme under CQTCO can obtain a superior performance in different communication scenarios,which can be considered as an eco-friendly solution for promoting reliable and efficient communication in future wireless networks.

Hybrid orthogonal and non-orthogonal pilot distribution based channel estimation in massive MIMO system

How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station(BS),which is a generalization of the existing pilot distribution scheme,is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation(GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.

MUSIC-Based Pilot Decontamination and Channel Estimation in Multiuser Massive MIMO System

This paper addresses the problem of channel estimation in a multiuser multi-cell wireless communications system in which the base station(BS)is equipped with a very large number of antennas(also referred to as"massive multiple-input multiple-output(MIMO)").We consider a time-division duplexing(TDD)scheme,in which reciprocity between the uplink and downlink channels can be assumed.Channel estimation is essential for downlink beamforming in massive MIMO,nevertheless,the pilot contamination effect hinders accurate channel estimation,which leads to overall performance degradation.Benefitted from the asymptotic orthogonality between signal and interference subspaces for non-overlapping angle-of arrivals(AOAs)in the large-scale antenna system,we propose a multiple signals classification(MUSIC)based channel estimation algorithm during the uplink transmission.Analytical and numerical results verify complete pilot decontamination and the effectiveness of the proposed channel estimation algorithm in the multiuser multi-cell massive MIMO system.

Optimal Multipoint-to-Multipoint Transceiver Design with Massive MIMO Relay Station in OFDM Systems

Based on massive MIMO(multiple-input multiple-output)multipoint-to-multipoint(M2M) systems,in order to avoid pilot contamination and improve the performance of capacity,a pilot training transmission scheme was designed for pilot decontamination by utilizing orthogonal subcarriers of OFDM(orthogonal frequency division multiplexing) during pilot transmission phase and a joint optimized transceiver design for multi-antenna user pairs was proposed during the data transmission phase.The massive M2 M system included a single relay station,multiple paired source nodes and destination nodes.Source nodes precoding matrices and relay station precoding matrix were jointly optimized by maximizing the weighted sum-rate in OFDM systems.After some mathematical manipulation to sum-rate,the cost function of sum-rate was expressed as quadratic optimizing expressions which could be solved by regular convex optimization softwares.Different from existing algorithms,the proposed precoding design was based on massive MIMO OFDM systems with multi-antenna users pairs together pilot decontamination transmission arrangement.Simulations indicate the effectiveness of the proposed optimal precoding system.The proposed scheme not only can reduce pilot contamination,but also can improve performance of bit-error-rate(BER) as well as sumrate contrast to existing algorithms.In addition,it shows that the proposed M2 M massive MIMO system works steadily when the number of users increases in large scale.

Pilot Contamination Elimination in Massive MIMO Systems with an Improved Time-Shifted Scheme

Pilot contamination can bring up a grave impairment in the performance of massive multiple-input multiple-output(MIMO)systems.In this paper,an improved time-shifted pilot scheme is proposed to reduce the pilot contamination,where orthogonal pilots are employed in the same group to eliminate the residual intragroup interference existing in the original time-shifted pilot scheme.Meanwhile,the rigorous closed-form expressions of both downlink and uplink transmission rates with a finite number of antennas are derived,and it is shown that the intra-group interference can be completely eliminated by the proposed scheme.Simulation results demonstrate that both downlink and uplink transmission rates are significantly improved by employing the proposed scheme.

Robust Fingerprint Localization Based on Massive MIMO Systems

Location-based services have become an important part of the daily life.Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering environment.In this paper,a single-site multiple-input multiple-output(MIMO)orthogonal frequency division multiplexing(OFDM)system is modeled,from which an angle delay channel power matrix(ADCPM)is extracted.Considering the changing environment,auto encoders are used to generate new fingerprints based on ADCPM fingerprints to improve the robustness of the fingerprints.When the scattering environment has changed beyond a certain extent,the robustness will not be able to make up for the positioning error.Under this circumstance,an updating of the fingerprint database is imperative.A new fingerprint database updating algorithm which combines a new clustering method and an updating rule based on probability is proposed.Simulation results show the desirable performance of the proposed methods.

A survey on user-centric cell-free massive MIMO systems

The mobile data traffic has been exponentially growing during the last several decades.This was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service variations.Cell-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this issue.In such systems,each User Equipment(UE)is served by a preferred set of surrounding APs cooperatively.In this paper,we provide a survey of the state-of-the-art literature on CF mMIMO.As a starting point,the significance and the basic properties of CF mMIMO are highlighted.We then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and algorithms.After that,we discuss the practical issues in implementing CF mMIMO and point out the potential future directions.Finally,we conclude this paper with a summary of the key lessons learned in this field.

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.

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.

Ensemble-transfer-learning-based channel parameter prediction in asymmetric massive MIMO systems

Asymmetric massive multiple-input multiple-output(MIMO)systems have been proposed to reduce the burden of data processing and hardware cost in sixth-generation mobile networks(6G).However,in the asymmetric massive MIMO system,reciprocity between the uplink(UL)and downlink(DL)wireless channels is not valid.As a result,pilots are required to be sent by both the base station(BS)and user equipment(UE)to predict doubledirectional channels,which consumes more transmission and computational resources.In this paper we propose an ensemble-transfer-learning-based channel parameter prediction method for asymmetric massive MIMO systems.It can predict multiple DL channel parameters including path loss(PL),multipath number,delay spread(DS),and angular spread.Both the UL channel parameters and environment features are chosen to predict the DL parameters.Also,we propose a two-step feature selection algorithm based on the SHapley Additive exPlanations(SHAP)value and the minimum description length(MDL)criterion to reduce the computation complexity and negative impact on model accuracy caused by weakly correlated or uncorrelated features.In addition,the instance transfer method is introduced to support the prediction model in new propagation conditions,where it is difficult to collect enough training data in a short time.Simulation results show that the proposed method is more accurate than the back propagation neural network(BPNN)and the 3GPP TR 38.901 channel model.Additionally,the proposed instancetransfer-based method outperforms the method without transfer learning in predicting DL parameters when the beamwidth or the communication sector changes.

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。

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.

Finite sensor selection algorithm in distributed MIMO radar for joint target tracking and detection

Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound(PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization(MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search(ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.

Non-fluctuating target detection in low-grazing angle with MIMO radar

The non-fluctuating target detection in low-grazing angle using multiple-input multiple-output(MIMO) radar systems was studied, where the multipath effects are very abundant. The performance of detection can be improved via utilizing the multipath echoes. First, the reflection coefficient considering the curved earth effect is derived. Then, the general signal model for MIMO radar is introduced for non-fluctuating target in low-grazing angle. Using the generalized likelihood ratio test(GLRT) criterion, the detector of non-fluctuating target with multipath was analyzed. The simulation results demonstrate that the MIMO radar outperforms the conventional radar in non-fluctuating target detection and show that the performance can be enhanced markedly when the multipath effects are considered.