Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems

Massive MIMO (multiple-input-multiple-output) is one of the key technologies of 5G mobile cellular networks, which can form a huge antenna array by providing a large number of antennas at the cell base station. It will greatly improve the channel capacity and spectrum utilization and has become a hotspot in the field of wireless communications in recent years. Aiming at the high complexity of channel estimation algorithm for massive MIMO system, a sparse channel estimation algorithm with low complexity is proposed based on the inherent sparsity of wireless communication channel. The algorithm separates the channel taps from the noise space on the basis of the traditional discrete Fourier transform (DFT) channel estimation, so that the channel estimation only needs to calculate the part of the channel tap, so the computational complexity of the algorithm is greatly reduced. The simulation results show that the proposed algorithm can achieve near minimum mean square error (MMSE) performance while maintaining low complexity. Moreover, the Bit Error Rate and Inter-Cell Interference also indicates that the proposed improved algorithm shows better overall performance than the conventional algorithms which makes it suitable from practical perspective.     

Symbol error rate performance of nonlinear OFDM receiver with peak value threshold over frequency selective fading channel

Nonlinear blanking and clipping methods are widely used in Orthogonal Frequency Division Multiplexing (OFDM) receiver to mitigate impulse interference. To quantitatively analyze the reliability performance of nonlinear OFDM receivers with pulse blanking and clipping based on peak value threshold, the symbol error rate (SER) performance of nonlinear OFDM receiver over frequency selective Rayleigh and Ricean fading channels is presented. Firstly, the analytical expressions of instantaneous output signal-to-interference and noise ratio (SINR) for nonlinear OFDM receivers with regular method, peak value blanking and peak value clipping are derived. Then, the SER performance of nonlinear OFDM receiver over frequency selective Rayleigh and Ricean fading channels is given based on the SINR expressions. Finally, simulation results are demonstrated to show good agreement with theoretical results. It has been observed that the peak value blanking method has achieved the best SER performance, and the inter-carrier interference based on the peak value blanking and peak value clipping will lead to error floor.     

面向5G的大规模MIMO技术综述

未来第5代移动通信系统(5G)中无线数据业务量的爆发性增长推动着研究人员发展新的颠覆性技术.作为5G的关键候选技术之一,大规模多入多出(MIMO)在基站使用远超激活终端数的天线,能增加一个数量级的频谱效率并大幅降低发射功率.首先介绍了大规模MIMO的系统模型和理论性能,其次分析和归纳了在信道测量与建模、信道信息获取、传输方法的研究成果,然后简述了实验和测试进展,最后讨论了未来研究方向.     

An efficient hybrid strategy for uplink channel estimation in massive MIMO systems based on spatially correlated channels

The channel estimation (CE) process is an important phase that has a considerable influence on the performance of massive multiple-input multiple-output systems, in particular, in a more realistic scenario where the channels are spatially correlated (ScD). Thereby, in this work, the uplink (UL) CE process and channel hardening (CH) feature is addressed for ScD Rayleigh fading channels using the statistical Bayesian minimum mean square error estimator. The spatial correlation (SC) of the channels is described using different models, namely, the Gaussian local scattering (GLS) model, the uniform local scattering model, and the proposed hybrid model. Each model (i.e., GLS model and the uniform local scattering model) is studied using two arrangements, that is, for a uniform linear array (ULA) and uniform planar array (UPA). Moreover, the CH feature is investigated under SC of the channels using different models. Furthermore, this study proposes an efficient hybrid strategy based on SC of the channels for UL CE; that is, this work proposes a hybrid covariance matrix (CM) for UPA arrangement by relying on the Kronecker product of the CMs generated through two ULA arrangements, where the first CM is generated through horizontal ULA using GLS model, whereas the second CM is generated through vertical ULA using uniform local scattering model (i.e., one-ring model). Numerical results regarding CE and CH are provided to assert the theoretical expressions, where the CE is evaluated using the normalized mean square error, whereas the CH is assessed using the variance of CH.     

Over-sampling basis expansion model aided channel estimation for OFDM systems with ICI

The rapid variation of channel can induce the intercarrier interference in orthogonal frequency-division multiplexing （OFDM） systems. Intercarrier interference will significantly increase the difficulty of OFDM channel estimation because too many channel coefficients need be estimated. In this article, a novel channel estimator is proposed to resolve the above problem. This estimator consists of two parts： the channel parameter estimation unit （CPEU）, which is used to estimate the number of channel taps and the multipath time delays, and the channel coefficient estimation unit （CCEU）, which is used to estimate the channel coefficients by using the estimated channel parameters provided by CPEU. In CCEU, the over-sampling basis expansion model is resorted to solve the problem that a large number of channel coefficients need to be estimated. Finally, simulation results are given to scale the performance of the proposed scheme.     

快衰落信道下OFDM系统的信道估计简化算法

在快衰落时变信道中，正交频分复用（OFDM：Orthogonal Frequency Division Multiplexing）系统子载波间的正交性会遭到破坏，由此引起的载波间干扰（ici：Inter-Carrier Interferellce）会对系统性能带来很大的影响。本文提出了一种利用时域线性模型的信道估计算法，利用随时间线性变化的模型描述信道。仿真结果表明，本算法能够有效地消除载波间干扰，降低系统的BER。与类似算法相比，当系统的BER性能相同时，算法复杂度大大的降低。     

Time domain leakage in DFT‐based channel estimation for OFDM systems with guard bands

In this paper, a mathematical analysis of the time domain leakage problem in the discrete Fourier transform (DFT) based channel estimation technique is presented. The time domain leakage is because of the absence of pilots in the guard band. Several solutions to this problem were previously proposed based on reducing the leakage in the frequency domain. These solutions significantly increase the receiver complexity. In this paper, the root cause of the leakage problem is linked to the time domain and a modification is proposed carrying slight additional complexity over the conventional DFT‐based estimator. Performance evaluation is assessed in terms of the bit error rate and the mean square error. The proposed modification significantly decreases the error floor of the conventional DFT‐based channel estimation technique. In addition, its performance is comparable to other more complex techniques recently proposed.Copyright © 2013 John Wiley & Sons, Ltd.     

A low complexity DFT-matrix based pilot allocation algorithm for sparse channel estimation in OFDM systems

The accuracy of channel estimation is very important for Orthogonal Frequency Division Multiplexing (OFDM) systems. In a high speed wideband wireless communication, the channel can be modeled as a sparse one. Therefore, the Compressed Sensing (CS) technique can be used for the estimation of the channel. In this paper, the problem of deterministic pilot allocation in OFDM systems is considered and a new criterion which is based on minimizing the summation of the correlations between the columns of the Discrete Fourier Transform (DFT) sub-matrix is proposed. It will be shown that the proposed criterion is a simple version of the well-known but complex criterion, Restricted Isometry Property (RIP). In addition, the pilot pattern design, using our proposed scheme, indicates better recovery performance than other proposed coherence based criteria in terms of the reconstruction mean square error (MSE) and successful channel recovery percentage. Simulation results confirm our analysis.     

一种改进的单音干扰下OFDM系统盲信道估计算法

在相关检测的OFDM系统中,信道估计具有十分重要的意义。研究了一种在单音干扰环境下的OFDM系统中基于预编码的盲信道估计方案。利用接收的干扰信号的频域信息对单音干扰信号进行准确估计与重构,有效地消除了干扰信号的影响,提高了单音干扰下盲信道估计的准确性。与其他盲信道估计算法相比,基于预编码的盲信道估计算法具有较低的运算复杂度以及较快的收敛速度。仿真结果表明了该算法在干扰估计以及信道估计两方面都取得了较好的性能。     

OFDM系统中一种实用的干扰抑制信道估计算法

针对OFDM系统中IS信道估计在噪声消除和干扰抑制上的不足,提出一种干扰抑制信道估计算法.算法利用OFDM信号的时频二维特性,在LS信道估计基础上通过3次FFT/IFFT实现信道噪声消除和窄带干扰抑制,算法复杂度低,具有较好的工程可行性.算法仿真结果表明,这种干扰抑制的信道估计性能比传统LS信道估计在多径信道下的性能提升3 dB以上,在有窄带干扰的场景下系统性能损失在1.5 dB以内.     

On channel estimation in cell-free massive MIMO for spatially correlated channels with correlated shadowing under Rician fading

This work addresses channel estimation (CE) in the uplink phase for a cell-free massive multiple-input multiple-output system operating under the time division duplex protocol. Considering that, channels are spatially correlated under the Rician fading model, where the investigated model is composed of two components: deterministic and nondeterministic, with the deterministic component describing the line-of-sight paths and the nondeterministic component describing the non-line-of-sight paths. Additionally, we dealt with correlated shadow fading that represents the most realistic situation. On the other hand, this work introduces a dynamic cooperation cluster framework in which the user is not served with the whole network ( i.e., all access points [APs]) but only the APs that present the best channel conditions regarding that user. In other words, this work proposes partial CE for each user because only APs with the best channel conditions are allowed to compute channel estimates. Consequently, we proposed partial channel estimators that perform the CE process with low complexity, namely, a partial minimum mean square error estimator and a partial element-wise minimum mean square error estimator. In addition, a simple pilot assignment technique is proposed in order to reduce interference signals so that each user experiences low interference from other users. Furthermore, the computational complexity required by each estimator is derived, where it is represented by the number of complex multiplications that each estimator requires in each consistency block. Theoretical and simulated results are provided, where the performance of each estimator is evaluated and analyzed using the normalized mean-square error metric.     

Randomized Kaczmarz algorithm for massive MIMO systems with channel estimation and spatial correlation

To exploit the benefits of massive multiple‐input multiple‐output (M‐MIMO) technology in scenarios where base stations (BSs) need to be cheap and equipped with simple hardware, the computational complexity of classical signal processing schemes for spatial multiplexing of users shall be reduced. This calls for suboptimal designs that perform well the combining/precoding steps and simultaneously achieve low computational complexities. An approach on the basis of the iterative Kaczmarz algorithm (KA) has been recently investigated, assuring well execution without the knowledge of second order moments of the wireless channels in the BS, and with easiness since no tuning parameters, besides the number of iterations, are required. In fact, the randomized version of KA (rKA) has been used in this context because of global convergence properties. Herein, modifications are proposed on this first rKA‐based attempt, aiming to improve its performance‐complexity trade‐off solution for M‐MIMO systems. We observe that long‐term channel effects degrade the rate of convergence of the rKA‐based schemes. This issue is then tackled herein by means of a hybrid rKA initialization proposal, which lands within the region of convexity of the algorithm and assures fairness to the communication system. The effectiveness of our proposal is illustrated through numerical results, which bring more realistic system conditions in terms of channel estimation and spatial correlation than those used so far. We also characterize the computational complexity of the proposed rKA scheme, deriving upper bounds for the number of iterations. A case study focused on a dense urban application scenario is used to gather new insights on the feasibility of the proposed scheme to cope with the inserted BS constraints.     

基于直接判决和导频跟踪的OFDM系统快时变信道估计

提出了一种基于直接判决的OFDM系统的快时变信道估计方法。采用了直接判决算法进行信道估计，并从中选择有效的估计结果，联合导频信号进行信道跟踪。将基于训练序列的信道估计结果作为直接判决算法的初始值，利用传输信号直接判决的统计特性进行了信道估计，并利用改进的导频算法进一步地跟踪信道在时间上的变化。Simulink仿真结果表明，该估计算法适用于时变信道，比基于导频的信道估计方法和基于训练序列的信道估计方法效果都要好。     

Particle filtering based channel estimation in OFDM power line communication

Particle filtering （PF） algorithm has the powerful potential for coping with difficult non-linear and non-Gaussian problems. Aiming at non-linear, non-Gaussian and time-varying characteristics of power line channel, a time-varying channel estimation scheme combined PF algorithm with decision feedback method is proposed. In the proposed scheme, firstly the indoor power line channel is measured using the pseudo-noise （PN） correlation method, and a first-order dynamic autoregressive （AR） model is set up to describe the measured channel, then, the channel states are estimated dynamically from the received signals by exploiting the proposed scheme. Meanwhile, due to the complex noise distribution of power line channel, the performance of channel estimation based on the proposed scheme under the Middleton class A impulsive noise environment is analyzed. Comparisons are made with the channel estimation scheme respectively based on least square （LS）, Kalman filtering （KF） and the proposed algorithm. Simulation indicates that PF algorithm dealing with this power line channel estimation difficult non-linear and non-Gaussian problems performance is superior to those of LS and KF respectively, so the proposed scheme achieves higher estimation accuracy. Therefore, it is confirmed that PF algorithm has its own unique advantage for power line channel estimation.     

Channel estimation and channel tracking for correlated block-fading channels in massive MIMO systems

This paper presents a channel estimation and tracking method for correlated block-fading channels in massive MIMO wireless cellular systems. In order to conserve resources, the proposed algorithm requires the uplink pilot signal only once, at the start of communication. By utilizing the temporal correlation between consecutive Resource Blocks (RBs) and the error correction capability of turbo codes, the channel matrix in subsequent RBs is estimated at the Base Station (BS) itself using the uplink data of current the RB and the estimated channel matrix of previous the RB. Compared to existing blind estimation methods, the proposed method places fewer limitations on the system settings such as the number of BS antennas, the number of users, and the number of coherent channel usage compared to existing blind estimation methods. Simulation results show that the proposed algorithm provides better performance for a moderate RB size, a high-order of QAM scheme, and a smaller ratio of the number of BS antennas and mobile terminals (N/K). For a reasonably small N/K (order of 10), the proposed scheme achieves a lower symbol error probability than the conventional pilot-based estimation approach.     

Kalman filter‐based channel estimation and ICI suppression for high‐mobility OFDM systems

The use of orthogonal frequency division multiplexing (OFDM) in frequency‐selective fading environments has been well explored. However, OFDM is more prone to time‐selective fading compared with single‐carrier systems. Rapid time variations destroy the subcarrier orthogonality and introduce inter‐carrier interference (ICI). Besides this, obtaining reliable channel estimates for receiver equalization is a non‐trivial task in rapidly fading systems. Our work addresses the problem of channel estimation and ICI suppression by viewing the system as a state‐space model. The Kalman filter is employed to estimate the channel; this is followed by a time‐domain ICI mitigation filter that maximizes the signal‐to‐interference plus noise ratio (SINR) at the receiver. This method is seen to provide good estimation performance apart from significant SINR gain with low training overhead. Suitable bounds on the performance of the system are described; bit error rate (BER) performance over a time‐invariant Rayleigh fading channel serves as the lower bound, whereas BER performance over a doubly selective system with ICI as the dominant impairment provides the upper bound. Copyright © 2008 John Wiley & Sons, Ltd.     

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

The capacity of a massive MIMO cellular network depends on user and antenna selection algorithms, and also on the acquisition of perfect Channel State Information (CSI). Low computational cost algorithms for user and antenna selection significantly may enhance the system capacity, as it would consume a smaller bandwidth out of the total bandwidth for downlink transmission. The objective of this paper is to maximize the system sum-rate capacity with efficient user and antenna selection algorithms and linear precoding. We consider in this paper, a slowly fading Rayleigh channel with perfect acquisition of CSI to explore the system sum-rate capacity of a massive MIMO network. For user selection, we apply three algorithms, namely Semi-orthogonal user selection (SUS), Descending Order of SNR-based User Scheduling (DOSUS), and Random User Selection (RUS) algorithm. In all the user selection algorithms, the selection of Base Station (BS) antenna is based on the maximum Signal-to-Noise Ratio (SNR) to the selected users. Hence users are characterized by having both Small Scale Fading (SSF) due to slowly fading Rayleigh channel and Large-Scale Fading (LSF) due to distances from the base station. Further, we use linear precoding techniques, such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE), and Maximum Ratio Transmission (MRT) to reduce interferences, thereby improving average system sum-rate capacity. Results using SUS, DOSUS, and RUS user selection algorithms with ZF, MMSE, and MRT precoding techniques are compared. We also analyzed and compared the computational complexity of all the three user selection algorithms. The computational complexities of the three algorithms that we achieved in this paper are O(1) for RUS and DOSUS, and O(M²N) for SUS which are less than the other conventional user selection methods.     

5G网络中无人机-车辆非平稳信道模型与统计特性分析

针对无人机到车辆通信场景,提出了一种椭圆-圆柱-半球几何非平稳无人机-车辆信道模型。为了描述收发端周围散射体分布情况,一次散射径的散射体分布用同心椭圆面模拟;在二次散射径建模中,无人机侧散射体分布采用同心圆柱体模拟,并且车辆侧散射体分布使用同心半球体模拟;地面一次反射径的反射点分布则通过半球底面同心圆来模拟。为了进一步研究信道的非平稳特性,在各路径分量的二维角度服从Von Mises分布、Laplacian分布以及Von Mises Fisher分布的基础上,推导了时间自相关函数、空间互相关函数和多普勒频谱密度函数。仿真结果表明,理论值与测量值具有较好的拟合度,所提出的信道模型可以作为无人机到车辆非平稳信道建模的设计方案。     

5G系统中基于小区分群的干扰管理

为了降低5G系统中超密度小区网络的小区间干扰（ICI）,提出了一种基于小区分群的干扰管理方案,其核心思想是将宏小区覆盖下的小小区分群,对群内干扰和群间干扰分别采用基于多小区协作和频域或时域的干扰避免。针对小区分群的关键即干扰权值的计算,提出了一种基于用户的信干噪比（SINR）和干扰泄露（SOIR）计算连续干扰权值的方案,该权值不仅反映用户间的干扰关系,而且自适应地调整干扰用户之间对资源的争夺策略;同时,把距离调和平均数作为小区分群的附加准则。仿真结果表明,与传统的小区分群方案相比,新方案的吞吐量增加20%~30%,小区性能和系统整体性能都有提升。     

基于射线追踪传播模型实现5G室内分布系统仿真的研究

室内分布系统的建设已经成为解决深度覆盖问题的有效手段。目前,5G网络建设发展迅速,室内分布系统建设呈爆炸式增长,室内分布系统的网络规划和设计需要更加精细化。如何有效评估室内信号覆盖效果,成为当前室内分布系统规划设计的重要任务之一。以某大楼为研究对象,用射线追踪传播模型仿真计算覆盖性能指标,并将结果与现有网络的路测数据进行对比。经过校正相关模型参数实现仿真与实测一致,论证仿真为网络部署提供了参考依据。