Weighted Gauss-Seidel Precoder for Downlink Massive MIMO Systems

In this paper, a novel precoding scheme based on the Gauss-Seidel (GS) method is proposed for downlink massive multiple-input multiple-output (MIMO) systems. The GS method iteratively approximates the matrix inversion and reduces the overall complexity of the precoding process. In addition, the GS method shows a fast convergence rate to the Zero-forcing (ZF) method that requires an exact invertible matrix. However, to satisfy demanded error performance and converge to the error performance of the ZF method in the practical condition such as spatially correlated channels, more iterations are necessary for the GS method and increase the overall complexity. For efficient approximation with fewer iterations, this paper proposes a weighted GS (WGS) method to improve the approximation accuracy of the GS method. The optimal weights that accelerate the convergence rate by improved accuracy are computed by the least square (LS) method. After the computation of weights, the different weights are applied for each iteration of the GS method. In addition, an efficient method of weight computation is proposed to reduce the complexity of the LS method. The simulation results show that bit error rate (BER) performance for the proposed scheme with fewer iterations is better than the GS method in spatially correlated channels.     

单用户毫米波大规模MIMO系统的混合预编码方案设计

对MIMO通信系统的数字预编码方法进行了深入分析,指出单用户毫米波大规模MIMO系统和传统全数字预编码方案所用射频(RF)链路数量过大致使系统实现成本大和能量消耗较高,并针对这种情况,提出了一种在系统收发两端分别采用混合预编码器和混合合成器的混合预编码方案。该方案首先通过迭代算法设计模拟预编码矩阵,并且根据信道矩阵与模拟预编码矩阵作用生成的等效信道矩阵设计数字预编码矩阵,然后根据混合预编码器设计混合合成器,从而使系统频谱效率最大化。该方案与传统全数字预编码及现有混合预编码方案的仿真比较结果表明,该方案有效降低了系统实现成本和能量消耗,且性能优于现有的混合预编码方案,与传统全数字预编码方案相比,性能非常接近。     

An Enhanced Jacobi Precoder for Downlink Massive MIMO Systems

Linear precoding methods such as zero-forcing (ZF) are near optimal for downlink massive multi-user multiple input multiple output (MIMO) systems due to their asymptotic channel property. However, as the number of users increases, the computational complexity of obtaining the inverse matrix of the gram matrix increases. For solving the computational complexity problem, this paper proposes an improved Jacobi (JC)-based precoder to improve error performance of the conventional JC in the downlink massive MIMO systems. The conventional JC was studied for solving the high computational complexity of the ZF algorithm and was able to achieve parallel implementation. However, the conventional JC has poor error performance when the number of users increases, which means that the diagonal dominance component of the gram matrix is reduced. In this paper, the preconditioning method is proposed to improve the error performance. Before executing the JC, the condition number of the linear equation and spectrum radius of the iteration matrix are reduced by multiplying the preconditioning matrix of the linear equation. To further reduce the condition number of the linear equation, this paper proposes a polynomial expansion precondition matrix that supplements diagonal components. The results show that the proposed method provides better performance than other iterative methods and has similar performance to the ZF.     

大规模MIMO系统的全导频RZF预编码方法

研究了大规模多输入多输出(MIMO)系统的预编码。针对小区间干扰和导频污染会对预编码的有效性产生较大影响的问题,提出了一种改进的全导频正则化迫零(RZF)预编码方法。该方法通过将有限的正交导频信号分配给小区用户来对预编码矩阵中的信道向量进行优化,得到优化后的全导频正则化预编码矩阵,并依此推导出新的包含导频污染函数和用户干扰函数的和速率表达式。另外,综合考虑基站天线数、用户数以及导频重用因子,优化其配置关系,从而用较少的用户信息和更高的导频重用因子来抑制更多的小区间干扰,有效地提升系统性能。实验结果表明,这种改进的RZF预编码算法能够较好地提高系统容量,在完全已知信道状态信息(CSI)和未完全已知CSI两种情况下的频谱效率及和速率均优于传统的RZF方法。     

RZF预编码与MMSE-SIC检测联合的大规模MIMO系统预编码设计算法

研究了大规模多输入多输出(MIMO)系统的预编码设计算法。针对大规模MIMO系统通常利用增加用户天线数来提高系统频谱效率的方法会导致用户间干扰增大,从而对系统性能产生负面影响的问题,提出了一种将正则化迫零(RZF)预编码与最小均方误差-串行干扰消除(MMSE-SIC)检测相结合的改进算法。该算法通过在基站端采用RZF预编码对信号进行预处理以平衡用户间干扰和噪声干扰的影响,继而在接收端运用检测性能优异的MMSE-SIC算法来进一步减轻信号中的干扰,从而达到提升系统容量的目的。实验结果表明,这种将RZF预编码与MMSE-SIC检测相结合的改进算法,在用户间干扰较大时具有较好的适用性,且在完全已知和未完全已知信道状态信息情况下的频谱效率均优于传统RZF算法。     

毫米波大规模MIMO系统波束选择方案的设计

针对毫米波大规模MIMO系统采用全数字预编码时,所需射频链路数量过多而导致能量消耗高的问题,提出了一种基于透镜的波束选择方案。该方案首先通过分析用户受干扰的可能性,将所有的用户分为干扰用户组和非干扰用户组,然后对于非干扰用户,直接利用最大功率准则进行波束选取,而对于干扰用户,则通过低复杂度增量算法选择合适的波束使系统和速率最大化。仿真结果表明,在有效减少系统所需射频链路数量和降低计算复杂度的基础上,该方案的系统和速率能够达到接近全数字预编码方案的水平,并且能够获得更高的能量效率。     

Improved Hybrid Beamforming for mmWave Multi-User Massive MIMO

Massive multiple input multiple output (MIMO) has become essential for the increase of capacity as the millimeter-wave (mmWave) communication is considered. Also, hybrid beamforming systems have been studied since full-digital beamforming is impractical due to high cost and power consumption of the radio frequency (RF) chains. This paper proposes a hybrid beamforming scheme to improve the spectral efficiency for multi-user MIMO (MU-MIMO) systems. In a frequency selective fading environment, hybrid beamforming schemes suffer from performance degradation since the analog precoder performs the same precoding for all subcarriers. To mitigate performance degradation, this paper uses the average channel covariance matrix for all subcarriers and considers an iterative algorithm to design analog precoder using approximation techniques. The analog precoder is iteratively updated for each column until it converges. The proposed scheme can reduce errors in the approximating process of the overall spectral efficiency. This scheme can be applied to fully-connected and partially-connected structures. The simulation results show that spectral efficiency performance for the proposed scheme is better than the conventional schemes. The proposed scheme can achieve similar performance with full-digital beamforming by using a sufficiently large number of RF chains. Also, this paper shows that the proposed scheme outperforms other schemes in the frequency selective fading environment. This performance improvement can be achieved in both structures.     

有限反馈SDMA系统中信道矩阵的逐列量化算法

针对多天线广播信道(MIMO BC)块对角化(BD)预编码空分多址接入(SDMA)系统对角化预编码时,直接量化信道矩阵的有限反馈方法复杂度过高的问题,利用系统中用户配置多个天线的特点,提出了基于天线合并逐列量化(PCQ)信道矩阵的有限反馈方法.该方法对接收信号做多次天线合并,将传统算法中直接量化信道矩阵转变为多次量化等效信道矢量,仿真结果表明,在总的反馈比特数相同的情况下,该方法以较少的和容量损失换来了复杂度的大幅降低.     

Optimization of Channel Estimation Using ELMx-based in Massive MIMO

In communication channel estimation, the Least Square (LS) technique has long been a widely accepted and commonly used principle. This is because the simple calculation method is compared with other channel estimation methods. The Minimum Mean Squares Error (MMSE), which is developed later, is devised as the next step because the goal is to reduce the error rate in the communication system from the conventional LS technique which still has a higher error rate. These channel estimations are very important to modern communication systems, especially massive MIMO. Evaluating the massive MIMO channel is one of the most researched and debated topics today. This is essential in technology to overcome traditional performance barriers. The better the channel estimation, the more accurate it is. This paper investigated machine learning (ML) for channel estimation. ML channel estimations based on the Extreme Learning Machine (ELMx) group are also implemented. These estimations, known as the ELMx group, include Regularized Extreme Learning Machine (RELM) and Outlier Robust Extreme Learning Machine (ORELM). Then, it was compared with LS and MMSE. The simulation results reveal that the ELMx group outperforms LS and MMSE in channel capacity and bit error rate. Additionally, this paper has proven complexity for verified computational times. The RELM method is less time consuming and has low complexity which is suitable for future use in large MIMO systems.     

莱斯信道下大规模MIMO系统混合模数转换器的随机向量量化方案

针对大规模MIMO系统用配置单精度模数转换器(ADC)来降低系统能耗会导致系统损失部分性能的问题,提出了一种莱斯信道下大规模MIMO系统的采用混合ADC的随机向量量化(RVQ)改进方案。该方案在频分双工模式下,首先在基站端采用高分辨率ADC和低分辨率ADC混合的接收方案处理信号,使接收的导频信号和有用信号具有较高的转换精度;系统对导频信号进行信道估计后,再对信道状态信息(CSI)进行RVQ处理,以此减小系统的反馈开销;最后运用最小均方误差(MMSE)信号检测算法减轻由量化误差引起的的用户间干扰,从而达到降低能耗并减小系统性能损失的目的。实验结果表明,这种改进的RVQ方案能在降低系统能耗的基础上有效减小系统容量损失,并使其和速率接近传统的高分辨率ADC接收方案。     

Interpolation-based precoding with limited feedback for MIMO-OFDM systems

The channel state information (CSI) at the transmitter can significantly improve the performance of multiple-antenna systems. However, providing full knowledge of CSI at the transmitter may not be affordable in many practical cases. Thus, exploiting the partial channel knowledge to improve system performance seems to be attractive. An interpolation based limited feedback precoding scheme (ILFP) for MIMO-OFDM systems is proposed. In this scheme, both the transmitter and the receiver store the codebook of precoding matrices constructed offline using two-variable joint vector quantisation. Considering the correlation between OFDM subcarriers, they are divided into subcarrier clusters. At the receiver, precoding is carried out on the clusters, and then the precoding information is conveyed to the transmitter by limited bits of feedback. At the transmitter, the precoding matrices for each subcarrier are obtained by interpolation according to the feedback. Simulation results show that the proposed scheme outperforms the existing schemes.     

Adaptive two-stage equalisation and FEXT cancellation architecture for 10GBASE-T system

For 10GBASE-T systems, variation in a multiple-input-multiple-output (MIMO) channel degrades the decision-point signal-to-noise ratio (DP-SNR) owing to imperfect pre-equalisation in the Tomlinson-Harashima precoding (THP) at the transmitter sides and catastrophic error propagation in far-end crosstalk (FEXT) cancellation at the receiver sides. Moreover, by using fixed THP coefficients during data transmission, as specified in the 10GBASE-T standard, and the non-linearity of THP pose challenges in the design of adaptive receivers. The authors propose an adaptive two-stage equalisation and FEXT cancellation (TS-EFC) architecture without updating the THP coefficients to combat channel variation at both the transmitter and receiver sides. In the first stage, we propose a new non-decision-directed FEXT canceller at the transmitter side using a joint training architecture to avoid error propagation. In the second stage, we devise an adaptive MIMO equaliser together with a novel pre-processing unit at the receiver side to combat channel variation. The pre-processing unit can eliminate the non-linearity issue by estimating both effective data sequences and precoded channel inputs. In addition, we develop a block least mean square algorithm that exploits the properties of two-dimensional modulated symbols for updating coefficients of the adaptive MIMO equaliser. Simulation results show that our TS-EFC architecture is robust against channel variation and significantly improves the DP-SNR. It eliminates the error propagation and also achieves faster convergence rates during the adaptation process.     

Optimal training design for linearly time-varying MIMO/OFDM channels modelled by a complex exponential basis expansion

Time- and frequency-selective fading of propagating channels degrades the performance of multiple-input multiple-output/orthogonal frequency-division multiplexing (MIMO/OFDM) systems extensively by introducing double convolutions in both time-domain and frequency- domain. The author addresses the problem of linearly time-varying (LTV) channel estimation of MIMO/OFDM systems. First, the time-varying coefficients of the LTV channel are modelled by complex exponential basis expansions. Secondly, the LTV channel is estimated and the optimal pilot symbols are derived following the minimum mean square error criterion. It is shown that the optimal pilot strategy is to group consecutive pilot tones together as a pilot cluster and to distribute uniformly all pilot clusters in frequency-domain. In addition, the LTV channel estimation is further improved by imposing a window function on received signals. Finally, through simulations, it is shown that the new channel estimator can provide a considerable performance improvement in estimating MIMO-LTV channels, especially for the rapidly time-varying channel of a large Doppler frequency.     

Precoding in downlink multi-carrier code division multiple access systems using expectation maximisation algorithm

The presence of multiple access interference and hardware complexity of the mobile terminal are two major burdens for multi-carrier code division multiple access schemes. Both burdens can be overcome in downlink by precoding the transmitted signal using the knowledge of the channel state information. In multiuser precoding techniques, the transmitter is optimised to combat channel impairments through the use of new spreading sequences that are obtained by solving an optimisation problem based on some criterion. Among these optimisation problems, the problem based on maximum likelihood is hard and complex to solve. In the proposed precoding scheme, the well-suited expectation maximisation algorithm is used to solve this problem. The proposed precoding technique is simulated and its performance is analysed and compared with some other precoding and detecting techniques. The results show that the proposed scheme considerably outperforms the previous precoding schemes.     

基于最大化信干噪比的广播信道干扰消除算法

研究了广播信道分布式干扰消除算法的性能,提出了一种基于动态功率分配的多天线广播干扰信道对齐算法.该算法利用多用户多天线干扰信道相关矩阵的迹来计算分配功率,并采用最大化码流信干噪比的方法来得到预编码矩阵和接收滤波器矩阵.数值仿真表明,该动态功率分配算法与等功率分配算法相比,尽管低负荷下干扰对齐后的平均信干噪比增益不明显,但中、高负荷下其干扰对齐后的平均信干噪比大幅提高,特别是高负荷下可以解决等功率方法存在的信干噪比瓶颈效应.由于低负荷的应用需求并不强烈,因而该算法是一种实用的广播信道干扰消除方法.     

MIMO水声通信中的空时分组扩展编码方案探讨

采用多输入多输出（MIMO）结构和空时编码可以使水声通信系统获得分集增益,改善系统性能。针对水声信道中的频率选择性衰落,研究了一种用于MIMO水声通信系统的空时分组扩展编码方案（STBSC）,利用正交扩频编码克服了水声信道中多径干扰对空时分组编码（STBC）信号正交性的破坏。给出了方案的系统模型和实现框图,采用MIMO水声模型对方案的性能进行了仿真。仿真结果表明,STBSC方案在多径水声信道中可以得到完全发射分集。最后介绍了方案的水库试验,试验结果表明了方案的有效性。     

相关MIMO信道下一种特征波束成型方案的性能研究

提出了相关多输入多输出信道下的一种空时分组码、空分复用与波束成型相结合的新方案．该方案利用了发射分集和空分复用所带来的分集和复用增益，并基于接收机反馈的信道相关矩阵设计了一种波束成型器．仿真结果表明，这种方案的性能较传统的方案在高信噪比时能获得较大的改善．     

基于空时块编码和正交脉冲的MIMO系统性能研究

为了有效利用脉冲无线电(IR)的短距离、高数据速率以及多输入多输出(MIMO)通信系统的更大容量,提出了一种基于空时块编码(STBC)和正交脉冲的MIMO超宽带(UWB)通信系统。具体实现是通过对标准单输入单输出(SISO)脉冲无线电系统的直接序列超宽带作为多址(MA)技术的IR-MA信号模型以及信道模型、收发器结构和检测方法的分析,提出了一种把空时块编码和正交脉冲相结合的新方法,并采用FS-rake相干接收(CR)和非相干接收(NCR)技术及IEEE UWB信道模型对其获得的空间分集和编码性能进行了研究,从而得到可以相对较好地描述高信噪比时的性能变化趋势的理论上限值。仿真结果表明,这种STBC-IR方案相比于传统的单链路-脉冲无线电(SL-IR)方案,可获得更好的空间分集和编码增益,可用于增加传输距离和减少rake接收机的复杂性,而且在UWB通信中,STBC-IR方案还能很好地消除定时抖动的影响。     

Cross-entropy optimisation of multiple-input multiple-output capacity by transmit antenna selection

Radio channel capacity can be increased dramatically using a multiple-input multiple-output (MIMO) scheme, but at the expense of hardware complexity. An efficient approach for complexity reduction is antenna subset selection at the transmitter and/or receiver. A novel transmit antenna selection algorithm is presented using the cross-entropy optimisation method to maximise channel capacity. In contrast with the existing work, the proposed algorithm guarantees a result to within 99% of the true optimum (i.e. the maximal capacity with selected transmit antennas) with substantially low complexity. The simulation results indicate that the proposed algorithm is independent of the relationship between the selected transmit array size and receive array size. The proposed scheme has the potential to make practical MIMO systems with high performance simpler to implement.     

Power allocation in SVD- and GMD-assisted MIMO systems

Singular-value decomposition (SVD)-based multiple-input multiple-output (MIMO) systems have attracted a lot of attention in the wireless community where the whole MIMO channel is decomposed into a number of unequally weighted independent single-input single-output (SISO) channels. The unequal weighting of the SISO channels has led to intensive research on bit- and power allocation even in MIMO channel situations with poor scattering conditions identified as the antenna correlation effect. In this situation, the unequal weighting of the SISO channels becomes even much stronger. In comparison to the SVD-assisted MIMO transmission, geometric mean decomposition (GMD)-based MIMO systems are able to compensate for the drawback of weighted SISO channels when using SVD at the cost of remaining interferences which can be easily removed by using dirty paper precoding. Together with different QAM constellation sizes per layer, bit loading and power allocation can be helpful to balance the bit-error probabilities in the activated number of MIMO layers. The novel contribution of this paper is that optimal and suboptimal power allocation solutions are investigated under the assumption of unequal SISO channels as well as different QAM constellation sizes per MIMO layer. Our results show that GMD-based MIMO transmission has the potential to significantly simplify the process of bit and power loading and outperforms the SVD-based MIMO transmission as long as the same QAM-constellation size is used on all equally-weighted SISO channels.