一种基于相异度的接收天线选择算法

对于多输入多输出系统天线选择算法而言,穷尽搜索算法能够达到最优的性能,但包含较多矩阵运算,计算复杂度较高。而传统基于相关度和相似度的天线选择算法虽具有较低计算量,但损失了较大的容量性能。针对这一问题,以容量性能为目标,提出了基于相异度的接收天线选择算法,分析了不同相异度下所提选择准则对系统性能的影响。与传统相关度和相似度天线选择算法相比,所提算法有效降低了计算复杂度,改善了系统的容量性能,仿真结果表明:所提算法具有较好的系统性能,适用于实时通信系统。     

60GHz 室内信道下一种快速天线选择算法

传统的基于信道容量最大化准则的天线选择算法虽然使信道容量达到了最大化,但是计算复杂度很高。针对计算复杂度高的问题, 提出了一种基于Doolittle-QR 分解的低复杂度天线选择算法。该算法基于Doolittle-QR 分解,可以快速选择出使系统容量最大化的天线。与传统的天线选择算法相比,该算法的计算复杂度不仅有效地降低了, 而且容量性能相近。在60GHz 室内信道下,仿真实验结果表明, 该算法具有良好的容量性能,优于随机天线选择算法,接近最优天线选择算法。     

二进制猫群算法在大规模MIMO天线选择中的应用

在多输入多输出(MIMO)系统中,天线选择技术平衡了系统的性能和硬件开销,但大规模MI-MO系统收发端天线选择复杂度问题一直没有得到很好的解决.基于信道容量最大化的准则,采用两个二进制编码字符串分别表示发射端和接收端天线被选择的状态,提出将二进制猫群算法(BCSO)应用于多天线选择中,以MIMO系统信道容量公式作为猫群的适应度函数,将收发端天线选择问题转化为猫群的位置寻优过程.建立了基于BCSO的天线选择模型,给出了算法的实现步骤.仿真结果表明所提算法较之于基于矩阵简化的方法、粒子优化算法具有更好的收敛性和较低的计算复杂度,选择后的系统信道容量接近于最优算法,非常适用于联合收发端天线选择的大规模MIMO系统中.     

一种改进的大规模MIMO发射天线选择算法

为了降低天线选择算法在大规模多输入多输出（Multiple-Input Multiple-Output,MIMO）系统下的误码率和复杂度,以用户端接收的总功率为优化目标,提出一种最大化所有用户接收总功率的天线选择算法。该算法将优化目标函数转化为凸函数,并利用凸优化方法求得其有效解。仿真结果表明,所提天线选择算法与传统的最大和容量算法相比,具有较好的系统误码率性能,且运算复杂度低,但系统容量有所降低。     

GMD V-BLAST系统天线选择

基于最大容量和最小差错率准则,研究了在GMD V-BLAST系统中的天线选择问题。选用以信道矩阵非零奇异值的几何均值最大化为目标函数,可以避免容量与差错率性能之间的矛盾。在所有可用天线中进行选择的全搜索算法虽有最佳性能,但复杂度太高。基于贪婪算法,对发射天线采用快速的逐增选择策略,对接收天线采用快速的逐减选择策略,可以显著降低计算的复杂性。计算机仿真结果表明,所采用的快速天线选择算法可以较低的复杂度获得接近全搜索法的容量和分集增益。     

DWT-based joint antenna selection for correlated MIMO channels

This paper proposes a new discrete wavelet transform (DWT)-based joint antenna selection scheme for spatially correlated multiple-input multiple output (MIMO) channels. To reduce the severe performance degradation of the traditional antenna selection schemes in correlated channels, a new scheme which employ joint antenna selection (JAS) at both link ends algorithm and embed DWT operations in the receiver-end RF chains is proposed. Through extensive simulations it is demonstrated that the proposed DWT-based joint antenna selection has significant improvement of the capacity for both i.i.d and correlated MIMO channels, while requiring only a minor hardware overhead and low computational complexity for the DWT operations. Moreover, it is shown that the capacity associated with DWT-based JAS is higher than the system employing DWT-based receive antenna selection (RAS) only. This is achieved in i.i.d. and correlated MIMO channels.     

MIMO系统中改进的二进制粒子群天线选择算法

在多输入多输出系统中,发射端和接收端的多天线配置提高了信道容量和传输可靠性,而天线选择技术能在保持系统优点的同时有效地降低运算复杂度以及硬件成本。为了能在时变的信道条件下快速地选择出一组最优的天线子集,提出了一种基于二进制粒子群算法的改进的天线选择算法。推导出了二进制粒子群联合收发端天线选择的信道容量公式,并将其作为粒子群算法的适应度函数,使天线选择问题转换成二进制编码串的组合优化问题。通过改进模糊函数提高粒子群算法的收敛性,让二进制粒子群尽可能地收敛于全局最优位置。仿真结果表明,改进的算法能在降低运算复杂度的同时提高收敛性,且系统信道容量趋近于最优算法。     

简明的联合收发端天线选择算法

For reducing the computational complexity of the problem of joint transmit and receive antenna selection in Multiple-Input- Multiple-Output (MIMO) systems, we present a concise joint transmit/receive antenna selec-tion algorithm. Using a novel partition of the channel matrix, we drive a concise formula. This formula enables us to augment the chan-nel matrix in such a way that the computational complexity of the greedy Joint Transmit/Receive Antenna Selection (JTRAS) algorithm is reduced by a factor of 4nL, where nL is the number of selected antennas. A de-coupled version of the proposed algorithm is also proposed to further improve the efficien-cy of the JTRAS algorithm, with some capacity degradation as a tradeoff. The computational complexity and the perform-ance of the proposed approaches are evalu-ated mathematically and verified by computer simulations. The results have shown that the proposed joint antenna selection algorithm maintains the capacity perormance of the JTRAS algorithm while its computational complexity is only 1/4nL of that of the JTRAS algorithm. The decoupled version of the proposed algorithm further reduces the computational complexity of the joint antenna selection and has better performance than other decoupling-based algorithms when the selected antenna subset is small as compared to the total number of antennas.     

分布式MIMO系统中的一种可控式天线选择

基于信道容量最大化准则,提出了一种专门针对分布式多入多出（MIMO）系统的可控式天线选择算法。该算法在射频链路数（Lt）固定的前提下分两步执行,第一步进行动态的端口选择,第二步利用穷举搜索法在已选定的端口间选出Lt个最优天线,通过门限值的设定控制天线选择的复杂度和性能的平衡。仿真结果表明,在合适的门限值范围内,该算法具有很好的跟踪特性,具有接近于最优的容量性能,同时大大降低了天线选择复杂度。     

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.     

基于矩阵特征值分解的MIMO天线选择算法

天线子集选择技术是一种有效的低代价且能够获得MIMO好处的技术.文中提出了一种基于矩阵特征值分解的天线选择算法以最大化信道容量,它将大的搜索空间分成若干小区间,在较小的搜索空间中采用穷举法.分析了所提算法及相关算法的计算复杂度.仿真实验表明,该算法能够获得接近穷举法的中断容量性能而且具有较低的计算复杂度.     

广义RASK系统的回溯StOMP检测算法

本文提出了一种基于压缩感知（compressive sensing,CS）理论的低复杂度的检测算法:回溯分段正交匹配追踪（backtracking stagewise orthogonal matching pursuit,BStOMP）次优检测算法,较好地解决了广义接收天线移位键控（generalized receive antenna shift keying,GRASK）系统中最优最大似然（maximum likelihood,ML）检测算法会随着激活接收天线数的增多而计算复杂度大幅度增长的问题。该算法首先依据当前迭代残差和等效信道矩阵的内积选择出大于设定阈值的项作为候选激活天线索引;然后通过最小二乘法对候选激活天线索引进行初步估计;最后引入回溯过程进行筛选,剔除一些多余的索引,提高重构精度。仿真结果表明,在相同条件下,所提算法与传统StOMP检测算法相比,能在增加少量计算复杂度的情况下检测性能得到较大的提升,因此保证了在检测性能与计算复杂度间获得良好的平衡。      

线性离散码系统中基于近似容量分析的发送天线选择算法

为了降低应用线性离散码(LDC)的多输入多输出系统中发送天线选择算法的复杂度,该文利用LDC的线性变换矩阵的酉矩阵参数化性质,推导出了一种考虑LDC矩阵平均影响的近似容量表达式。近似容量表达式不涉及时间扩展的高阶等效信道矩阵的运算,从而基于近似容量的发送天线选择算法可以有效地降低运算复杂度。该文从近似容量出发,给出了最大化近似容量的发送天线选择算法和基于矩阵消元的天线选择算法。近似容量表达式为基于垂直空时分层码(V-BLAST)提出的低复杂度天线选择算法在LDC系统中的直接应用提供了理论支持。仿真结果表明,所提方案具有与最优天线选择算法相近的性能,但具有更低的计算复杂度。基于矩阵消元的天线选择算法与在V-BLAST系统中提出的基于范数和相关性的天线选择算法相比,可以获得更好的分集增益,因而具有更好的误码率性能。     

Fast antenna subset selection in MIMO systems

Multiple antenna wireless communication systems have recently attracted significant attention due to their higher capacity and better immunity to fading as compared to systems that employ a single-sensor transceiver. Increasing the number of transmit and receive antennas enables to improve system performance at the price of higher hardware costs and computational burden. For systems with a large number of antennas, there is a strong motivation to develop techniques with reduced hardware and computational costs. An efficient approach to achieve this goal is the optimal antenna subset selection. In this paper, we propose a fast antenna selection algorithm for wireless multiple-input multiple-output (MIMO) systems. Our algorithm achieves almost the same outage capacity as the optimal selection technique while having lower computational complexity than the existing nearly optimal antenna selection methods. The optimality of the proposed technique is established in several important specific cases. A QR decomposition-based interpretation of our algorithm is provided that sheds a new light on the optimal antenna selection problem.     

一种新型低复杂度双层分组天线选择算法

针对平坦相关瑞利衰落信道环境下的端到端大规模MIMO系统复杂度过高的问题,提出一种基于离散布谷鸟搜索的低复杂度双层分组天线选择算法。该算法首先基于天线信道相关性对大规模天线阵列进行分组处理,进而利用新型双层算法对分组的天线集合进行优化天线选择。其中,新型双层算法的第一层是每小组天线基于离散布谷鸟搜索的内部选择,第二层是对第一层选择的所有天线利用离散布谷鸟搜索进行最终的选择。提出的新型天线选择算法可有效降低大规模MIMO系统复杂度。仿真结果验证了在平坦相关瑞利衰落信道环境下,提出的天线选择算法能够以较低选择复杂度获得接近最优选择方法的容量性能和较优的BER性能。     

CDMA系统智能天线盲自适应波束形成

给出了基于最大化信号对干扰和噪声的功率比(MSINR)准则的盲自适应波束形成在线递推算法,旨在提高DS-CDMA系统中智能天线的性能.将MSINR准则转化为一种新的等价的无约束准则函数,通过最大化此准则函数来在线估计最优波束形成向量.算法直接利用了DS-CDMA系统解扩前和解扩后的观测信号,无须训练序列.通过在线迭代估计,降低了计算波束权值的复杂度.不同通信环境中的仿真结果表明,该算法具有较高的估计精度和很好的抗多址干扰能力.     

Low-Complexity Throughput-Based Antenna Selection Method

Antenna selection is a low-cost low-complexity attractive approach in MIMO systems that capture many advantages of these systems. In this paper, our objective is to select the best antennas that maximize throughput with truncated selective repeat automatic repeat request at data link layer in zero-forcing MIMO receivers. We propose a novel binary particle swarm optimization method with throughput as its fitness function for joint transmit and receive antenna selection. The results of simulations demonstrate that the proposed throughput based antenna selection method has better performance compared to capacity based methods, and PSO algorithm can significantly reduce computational complexity.     

Antenna selection algorithm based on cut-off rate for secure spatial modulation with finite alphabet input

For the security transmission of artificial noise with finite alphabet inputs in secure spatial modulation system,an antenna selection algorithm based on cut-off rate (COR) was proposed.Considering that it was difficult to obtain the closed-form expression of the secrecy rate,the problem of maximizing the secrecy rate into that of maximizing the COR was converted by the algorithm.Meanwhile,in order to reduce the complexity,the COR algorithm was optimized by expanding the matrix norm in columns,which was corresponding to the problem of antenna combination.Finally,the artificial noise vector was mapped in the legitimate channel space to optimize secrecy rate,which only interferes with eavesdropper.The simulation results show that the proposed algorithm based on COR is superior to the leakage-based algorithm and the maximum secrecy rate algorithm,and has lower complexity.     

Improved generalized spatial modulation via antenna selection

When a high spectral efficiency is needed, the cost of Euclidean distance‐based antenna selection for spatial modulation (EDAS‐SM) in terms of hardware, size, and computational complexity is significantly increased because of the large transmit antenna array required. In comparison, generalized spatial modulation (GSM) can match the spectral efficiency of EDAS‐SM, while using significantly fewer transmit antenna elements. However, the error performance of GSM is naturally limited because of the use of a predetermined and fixed set of transmit antenna combinations. By exploiting knowledge of the channel, the optimal set of transmit antenna combinations can be selected by maximizing the minimum Euclidean distance between transmit vectors. In this paper, an adaptive scheme for selection of the optimal set of transmit antenna combinations is proposed to improve the reliability of GSM. The computational overhead of the said scheme is relatively high; hence, a low‐complexity suboptimal scheme for selection of the set of transmit antenna combinations is further proposed. The improved GSM schemes address the spectral efficiency limitation of EDAS‐SM, while demonstrating superior error performance.     

大规模MIMO系统中收发联合阈值天线选择算法

在大规模多输入多输出(massive MIMO)系统中使用天线选择算法可提高能效和系统吞吐量,然而适用于传统MIMO系统的天线选择算法具有高复杂度,很难用于massive MIMO系统。为优化天线选择算法,以算法复杂度和系统容量为优化目标,提出了收发联合阈值天线选择算法。该算法在发射端使用最大范数双向天线选择算法进行天线选择,在接收端使用分组maxvol算法并通过仿真实验结果的预设阈值进行天线选择。仿真实验表明,收发联合阈值天线选择算法在降低复杂度的同时可以提高系统容量,与递增天线选择算法相比,系统容量最多可提高52.2 bit/s/Hz。提出的天线选择算法可以满足不同天线相关度和信噪比的传输环境。