Quaternion-MUSIC for near-field strictly noncircular sources with large-scale polarization array

In this paper, a novel localization method is proposed for DOA, range and polarization estimation of near-field noncircular sources in massive multiple-input-multiple-output (MIMO) systems. Compared with traditional MUSIC-based algorithms, the proposed algorithms can separate the polarization parameters from the spatial spectrum function, avoiding the four-dimensional (4-D) spectrum search and realizing the fast localization of the near-field source with high accuracy. First, the dimension-reduced MUSIC (DR-MUSIC) algorithm is proposed for DOA and range estimation with low computational complexity, and given a closed-form expression of polarization estimation. Next, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signals with high estimation accuracy. In addition, the analysis of the computational complexity and simulations of the proposed method are provided, showing that the proposed method yields a better performance than DR-MUSIC in massive MIMO systems.     

An IDFT approach for coprime array direction-of-arrival estimation

In massive multiple-input multiple-output (MIMO) systems, efficiently estimating both the direction-of-arrival (DOA) and the source power with an increased number of degrees-of-freedoms (DOFs) is important but challenging. Aiming at this, we introduce the framework of coprime array signal processing into massive MIMO system and propose an efficient inverse discrete Fourier transform (IDFT)-based DOA estimation algorithm in this paper. By implementing IDFT on the second-order virtual array signals characterized by the equivalent spatial frequency, it is proved that the resulting spatial response enables to effectively estimate both DOA and source power with an increased number of DOFs. Meanwhile, the window method and the zero-padding technique are sequentially considered to alleviate the spectral leakage phenomenon and improve the DOA estimation accuracy. Compared with the existing coprime array DOA estimation algorithms, the implementation of IDFT indicates a remarkably reduced computational complexity as well as the hardware overhead. Simulation results show the effectiveness of the proposed algorithm.     

Joint direction of arrival estimation and array calibration for coprime MIMO radar

Compared to large-scale MIMO radar, coprime MIMO radar can achieve approximate estimation performance with reduced antenna number. In this paper, joint direction of arrival (DOA) estimation and array calibration for coprime multiple-input multiple-output (MIMO) radar is considered, and an iterative method for the estimations of DOA and array gain-phase errors is proposed. Based on the received data structure of coprime MIMO radar, trilinear decomposition is firstly adopted to obtain the estimations of transmit and receive direction matrices, which are perturbated by the gain-phase errors. Through equation transformation, the un-perturbated direction matrices and gain-phase errors can be iteratively updated based on Least squares (LS). Finally, the unique DOA estimation is determined from the intersection of transmit and receive direction matrices. The proposed algorithm achieves better DOA estimation and array calibration performance than other methods including estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm, multiple signal classification (MUSIC)-like algorithm and joint angle and array gain-phase error estimation (JAAGE) method, and it performs close to the method with ideal arrays. Multiple simulation results verify the algorithmic effectiveness of the proposed method.     

双基地MIMO雷达中基于传播算子的DOD和DOA估计算法

多输入多输出(Multiple-input multiple-output,MIMO)雷达利用多个天线发送和接收信号,具有超过传统相控阵的潜在优势.本文提出一种双基地MIMO雷达中基于传播算子的离开角(Direction of departure,DOD)和到达角(Direction of arrival,DOA)估计...     

基于贝叶斯压缩感知的毫米波MIMO信道估计

针对毫米波大规模多输入多输出(MIMO)通信系统中存在的硬件成本高、能耗大等问题,混合模拟-数字的收发机架构是一个很有前景的解决方案,然而系统的信道估计问题却成为一个挑战。在考虑正交频分复用和频率选择性衰落信道模型的前提下,提出了一种使用贝叶斯压缩感知理论来估计信道的方法。贝叶斯压缩感知算法可以在稀疏信道先验知识不完备的情况下,实现更高精度的信道估计。仿真结果验证了所提方法的有效性,与正交匹配追踪算法相比,在信噪比为30dB时,归一化均方误差降低了约25dB。     

基于低秩矩阵完备的大规模MIMO系统信道估计研究*

随着大规模MIMO系统中天线数的增长,获取信道状态信息(channel state information at the transmitter, CSIT)所需的下行信道训练开销和上行反馈开销变得非常巨大。针对信道估计开销过大的问题,提出了一种新的CSIT估计方案和基于低秩矩阵完备的信道估计算法。在本方案中,基站发送训练信号给各个用户之后,用户直接将其观测信号反馈给基站,并在基站端进行统一的CSIT估计。然后,利用大规模MIMO信道矩阵的特点将信道估计问题转化为低秩矩阵完备问题,从而利用软阈值算法恢复出所有用户的信道状态信息。仿真结果表明,该算法可以获得精确的信道状态信息并有效地估计减少了信道估计开销和复杂度。     

Achievable throughput enhancement based on modified carrier interferometry for MIMO/OFDM

In MIMO systems, channel estimation is important to distinguish the transmitted signals from multiple transmit antennas. When MIMO systems are introduced in cellular systems, we have to measure the received power from all the connectable base station (BS), as well as to distinguish all the channel state information (CSI) for the combination of transmitter and receiver antenna elements. One of the most typical channel estimation schemes for MIMO in a cellular system is to employ a code division multiplexing (CDM) scheme in which a unique spreading code is assigned to distinguish both BS and MS antenna elements. However, by increasing the number of transmit antenna elements, large spreading codes and pilot symbols are required to estimate an accurate CSI. To reduce this problem, in this paper, we propose a high time resolution carrier interferometry (HTRCI) for MIMO/OFDM to achieve an accurate CSI without increasing the number of pilot symbols.     

Block rank sparsity-aware DOA estimation with large-scale arrays in the presence of unknown mutual coupling

With the development of massive multiple-input mutiple-output (MIMO) technique, high-resolution direction-of-arrival (DOA) estimation has attracted great attention. A novel sparse signal reconstruction method based on the inherent block rank sparsity of the sub-matrix is proposed for high resolution DOA estimation with large-scale arrays under the condition of unknown mutual coupling. In the proposed method, by taking advantage of the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM), a novel block representation model is firstly formulated by parameterizing the steering vector. Then, exploiting the inherent block sparsity characteristics of the sub-matrix, a reweighted nuclear norm minimization algorithm is proposed to reconstruct the sparse matrix, in which the weighted matrix is designed by using the spectrum of MUSIC-Like algorithm. Finally, the DOAs are achieved by searching the non-zeros blocks of the recovered matrix. The proposed method not only makes full use of the block rank sparsity characteristics of the sub-matrix and weighted matrix for enhancing the sparse solution, but also avoids the array aperture loss. Thus, the proposed method has superior estimation performance than the state-of-the-art algorithms under the condition of unknown mutual coupling. Especially, in the case of large-scale antennas, the advantage of the proposed method is more obvious. Some computer simulation results are performed to verify the advantage of our proposed method.     

大规模MIMO加权正交匹配追踪信道估计算法

针对大规模多输入多输出系统基站天线数目众多,移动用户很难实时精确完成信道估计等问题。提出了一种加权的正交匹配追踪算法。该算法在每次迭代过程中,计算得到的估计信号值由当前残差信号估计值和迭代之前估计值两部分组合而成;分别对当前残差信号估计值和迭代之前估计值设置不同的权值,以提高信号在低信噪比情况下的估值精度;通过调整不同迭代次数权值大小,可以提升信号在不同信噪比情况下的计算精度。仿真结果表明,在不同的信噪比情况下,该算法都可以获得比标准正交匹配追踪算法更高的估计精度。     

基于原子范数最小化的二维稀疏阵列波达角估计算法

基于二维稀疏平面阵列的波达角(Direction-of-arrival,DOA)估计问题在第五代移动通信大规模多输入多输出阵列的应用中日益重要。无网格稀疏重构技术促进了DOA估计问题的发展,原子范数理论则使得DOA估计的超分辨率得到进一步的提高。文中研究了多个方向的频谱稀疏信号入射到二维稀疏阵列时的DOA估计问题。为了准确、成对地识别出所有入射信号的仰角和方向角,提出了一种基于多个测量矢量(Multiple Measurement Vectors,MMV)的二维原子范数算法,并用半正定规划进行求解。所提算法将二维DOA估计问题中的压缩感知理论从单个测量矢量拓展到多个测量矢量,从而有效利用MMV的联合稀疏性。数值仿真结果表明,随着MMV矢量的增长,可识别的信源个数增加,稀疏阵列中物理传感器所占比例降低到30%,DOA估计误差也显著降低,并且在信噪比增大时,所提算法能够取得很好的收敛效果。     

Investigation on Key Technologies in Large-Scale MIMO

Large-scale MIMO (multiple-input multiple-output) systems with numerous low-power antennas can provide better performance in terms of spectrum efficiency, power saving and link reliability than conventional MIMO. For large-scale MIMO, there are several technical issues that need to be practically addressed (e.g., pilot pattern design and low-power transmission design) and theoretically addressed (e.g., capacity bound, channel estimation, and power allocation strategies). In this paper, we analyze the sum rate upper bound of large-scale MIMO, investigate its key technologies including channel estimation, downlink precoding, and uplink detection. We also present some perspectives concerning new channel modeling approaches, advanced user scheduling algorithms, etc.     

TDD reciprocity calibration for multi-user massive MIMO systems with iterative coordinate descent多用户大规模MIMO时分双工系统迭代坐标下降互易性校准算法

For massive multiple-input multiple-output (MIMO) antenna systems, time division duplexing (TDD) is preferred since the downlink precoding matrix can be obtained through the uplink channel estimation, thanks to the channel reciprocity. However, the mismatches of the transceiver radio frequency (RF) circuits at both sides of the link make the whole communication channel non-symmetric. This paper extends the total least square (TLS) method to the case of self-calibration, where only the antennas of the access points (APs) are involved to exchange the calibration signals with each other and the feedback from the user equipments (UEs) is not required. Then, the proof of the equivalence between the TLS method and the least square (LS) method is presented. Furthermore, to avoid the eigenvalue decomposition required by these two methods to obtain the calibration coefficients, a novel algorithm named as iterative coordinate descent (ICD) method is proposed. Theoretical analysis and simulation results show that the ICD method significantly reduces the complexity and achieves almost the same performance of the LS method.     

单基地MIMO雷达中相干目标的波达角和多普勒频率快速联合估计算法

针对单基地MIMO中相干目标的波达角（Direction-of-arrival,DOA）和多普勒频率联合估计问题,提出了一种降维-前向平滑-传播算子算法（Reduced dimension-forward spatial smoothing-propagator method,RD-FSS-PM）。该算法首先通过对接收信号进行降维变换以降低复杂度,继而利用前向平滑技术（Forward spatial smoothing,FSS）实现解相干,最后通过传播算子算法（Propagator method,PM）实现了对相干目标的波达角和多普勒频率联合估计,且无需额外配对。与传统的FSS-PM算法相比,所提算法波达角估计性能提升,多普勒频率估计性能接近而复杂度大大降低。本文同时分析了算法的理论均方误差（Mean squared error,MSE）和单基地MIMO雷达中波达角和多普勒频率联合估计问题的克拉美罗界（Cramer-Rao bound,CRB）。最后提供了详尽的仿真实验以验证算法的性能。     

MIMO阵列中基于PM和降维变换的高效DOA估计算法

在这篇论文中,我们讨论MIMO阵列中的高效DOA估计方法。由于PM算法不需要对互相关矩阵进行特征值分解,也不需要对接收的数据进行奇异值分解,因此它的计算量可以显著的变小。因此MIMO阵列中基于PM算法和降维变换的DOA估计方法被提出了,而且这种提出的算法比PM算法有更低的计算复杂度。这种算法在没有频谱搜索的情况下效果很好。此外,它比PM算法的角度估计性能稍好。DOA估计中的估计误差的方差和克拉美罗界也可以导出。仿真结果验证了提出的算法的有效性。     

A sparse direction-of-arrival estimation algorithm for MIMO radar in the presence of gain-phase errors

In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation. Useful cumulants are designed and extracted to estimate the gain and the phase errors in the transmit array and the receive array, thus a reliable error matrix is obtained. Then the proposed algorithm reduces the gain-phase error matrix to a low dimensional one. Finally, with the updated gain-phase error matrix, the FOC-based reweighted sparse representation framework is introduced to achieve accurate DOA estimation. Thanks to the fourth-order cumulants based gain-phase error matrix estimation, and the reweighted sparse representation framework, the proposed algorithm performs well for both white and colored Gaussian noises, and provides higher angular resolution and better angle estimation performance than reduced-dimension MUSIC (RD-MUSIC), adaptive sparse representation (adaptive-SR) and ESPRIT-based algorithms. Simulation results verify the effectiveness and advantages of the proposed method.     

An overview of transmission theory and techniques of large-scale antenna systems for 5G wireless communications

To meet the future demand for huge traffic volume of wireless data service, the research on the fifth generation (5G) mobile communication systems has been undertaken in recent years. It is expected that the spectral and energy efficiencies in 5G mobile communication systems should be ten-fold higher than the ones in the fourth generation (4G) mobile communication systems. Therefore, it is important to further exploit the potential of spatial multiplexing of multiple antennas. In the last twenty years, multiple-input multiple-output (MIMO) antenna techniques have been considered as the key techniques to increase the capacity of wireless communication systems. When a large-scale antenna array (which is also called massive MIMO) is equipped in a base-station, or a large number of distributed antennas (which is also called large-scale distributed MIMO) are deployed, the spectral and energy efficiencies can be further improved by using spatial domain multiple access. This paper provides an overview of massive MIMO and large-scale distributed MIMO systems, including spectral efficiency analysis, channel state information (CSI) acquisition, wireless transmission technology, and resource allocation.     

Multiuser detection algorithm based on belief propagation in multiple-input multiple-output systems

In MIMO (multiple-input, multiple-output) systems, signals from differenttransmitting antennas interfere at each receiving antenna and multiuser detection (MUD)algorithms may be adopted to improve the system performance. This paper proposes anovel multiuser detection algorithm in MIMO systems based on the idea of "beliefpropagation" which has achieved great accomplishment in decoding of low-densityparity-check codes. The proposed algorithm has a low computation complexityproportional to the square of transmitting/receiving antenna number. Simulation resultsshow that under low signal-to-noise ratio (SNR) circumstances, the proposed algorithmoutperforms the traditional linear minimum mean square error (MMSE) detector while itencounters a "floor' of bit error rate under high SNR circumstances. So the proposedalgorithm is applicable to MIMO systems with channel coding and decoding. Although inthis paper the proposed algorithm is derived in MIMO systems, obviously it can be appliedto ordinary code-division m     

大规模MIMO系统研究进展

随着无线通信技术的快速发展和智能手机的迅速普及,人们对数据传输速率提出了更高的需求。为进一步提高数据传输速率,通过增加基站天线数目构建大规模MIMO系统,是一种高效而相对便捷的方式。大规模MIMO系统能深度发掘空间维的自由度,使得基站能够利用同一时频资源服务于多个用户。本文探讨了大规模MIMO系统的导频污染问题及解决方案、适用于大规模MIMO系统的信道模型以及低复杂度的传输技术与实现方法三项关键技术。与现有MIMO系统相比,大规模MIMO系统能显著提高频谱效率、能量效率和系统的鲁棒性能。作为第五代移动通信（5G）最具潜力的研究内容之一,大规模MIMO无线通信技术已引起国内外的广泛关注,但相关研究工作尚处在起步阶段,还有大量的技术难点需要进一步突破。     

Constrained ALS-based tensor blind receivers for multi-user MIMO systems

Tensor factorizations has shown to be an efficient approach for symbols and/or channel estimation in multi-input multi-output (MIMO) systems, where the factor matrices of tensor that correspond to symbols, channel, code/diversity of signals, are often estimated by using alternating least squares (ALS) algorithm. Although the performance of tensor approaches strongly depend on the initializations of the factor matrices. However, due to the absence of a priori on channels, these initializations are done randomly in traditional ALS algorithm. This generally implies a slow convergence. Further, ALS does not take into account the potential orthogonal structure in the factor matrices, which can be exploited to improve the accuracy of factor matrices recovery. To address these insures, this paper proposes constrained ALS tensor blind receivers for multi-user MIMO systems. We show that the multi-user MIMO signals can be expressed as a third-order tensor model, where the matrices of users symbols, direction-of-arrival (DOA) and delay can be viewed as three factor matrices of the tensor model. Two constrained ALS blind algorithms that take into account the potential orthogonal and Vandermonde structures in the factor matrices, are proposed to learn the tensor model, where the users symbols, DOA and delay are joint estimated as three factor matrices. Besides provide the estimations for the factor matrices, the orthogonal and Vandermonde structures also give a better uniqueness results for the use of tensor model. Interestingly, these structures are the nature properties of the factor matrices in our system. This results in an efficient blind approach that has better performance and lower complexity compare with the traditional ALS.     

A nested-PARAFAC based approach for target localization in bistatic MIMO radar systems

Bistatic MIMO radar systems gather several advantages such as increased resilience to electronic countermeasures, unknown receiver location, higher identifiability of targets and direct application of several high resolution adaptive techniques. In this paper, we propose a tensor-based method for joint direction of departure (DoD) and direction of arrival (DoA) estimation in bistatic MIMO radar systems. By assuming that the transmit array is divided into two maximally overlapping subarrays, we initially model the cross-covariance matrix of the matched filters outputs as a Nested-PARAFAC decomposition of a fourth-order covariance tensor. Then, exploiting the structure of this decomposition, we first propose a two stage algorithm for joint DoD and DoA estimation of multiple targets based on double alternating least squares (DALS). In addition, for scenarios in which the number of receive antennas exceeds the number of targets, we propose a closed-form solution to the second stage of the proposed method based on the least squares Khatri-Rao factorization (LS-KRF) concept. Simulation results show that the proposed method offers a highly-accurate localization of multiple targets in real-world scenarios where the antenna elements at the transmit and receive arrays have positioning errors as well as less complexity compared to competing state-of-the-art tensor-based solutions.