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。     

大规模MIMO相关信道下的联合天线分组和天线选择

在大规模多输入多输出系统中,针对密集部署的大型天线阵列之间的强相关性会抑制天线选择增益效果的问题。在系统下行链路场景下建立空间相关信道模型,提出了基于天线分组的天线选择算法。根据瞬时信道相关矩阵将天线阵列划分为若干组,保证各组内天线之间相关性较强。在完成天线分组的基础上,基于信道矩阵列范数准则在各组发射天线与接收天线之间构成的子信道矩阵中选择天线,进而构造有效发射天线与接收天线之间的信道矩阵。仿真分析了所提天线选择算法对系统遍历和速率的影响,结果表明,在基站天线数为32、接收天线数为2、选择天线数为2、天线相关因子为0.9的假设下,当信噪比为10 dB时,与基于相邻天线分组的天线选择算法相比,所提算法使系统和速率约提高了27.5%,且所提算法若要与最优天线选择算法达到相同的和速率,仅需将其信噪比提升1～2 dB即可。     

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。     

相关衰落MIMO信道下的联合天线子集选择

针对相关衰落MIMO信道下的联合天线子集选择,提出了2种选择算法。首先,在已知精确信道知识的情况下,选择算法选择以使接收数据流的SNR最大化和瞬时误差概率即误符号率最小化的天线子集,从而得到平均SNR增益改善的解析表达式;其次,当信道迅速变化时,在已知信道统计知识的情况下,选择算法选择以使全部可能信道实现的平均误差概率最小化的天线子集,从而得到编码增益,在极端相关信道条件下,也可以得到分集增益;基于2种选择算法的性能分析和仿真结果验证了本文提出算法的有效性。     

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。     

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.     

Least squares-based receive antenna selection for MIMO spatial multiplexing systems with linear receivers

In this letter, a novel receive antenna selection technique is proposed for multiple input multiple output (MIMO) spatial multiplexing systems with linear receivers in the presence of unknown interference. This antenna selection technique is directly implemented based on training sample sequence under the least squares (LS) criterion. It avoids the channel estimation and retain the diversity benefit by antenna selection in the presence of unknown multiple access interference (MAI). In addition, practical implementation with manageable complexity is made possible by extending the fast backward greedy algorithm (BGA) into the proposed antenna selection algorithm.     

接近最优的编码MIMO系统的发送天线子集选择算法

多天线无线系统可提供更大的信道容量和更好的抗衰落能力,发送端利用反馈的部分信道状态信息进行发送天线子集选择能够进一步提高信道容量。该文提出了一种MIMO系统的快速的、动态的天线子集选择算法,其提供的信道容量高于已有的静态算法,且接近于最优天线选择算法,而无需计算所有可能的天线子集组合的信道容量,因而具有更低的复杂度。将本文算法与比特交织编码调制(BICM)技术相结合,对各天线速率进行适配,提出了空时自适应比特交织编码调制(ST-ABICM)方案。仿真结果证实了该方案性能的优越性。     

Selecting array configurations for MIMO systems: an evolutionary computation approach

This paper presents an antenna selection method for multiple-input multiple-output wireless systems. By exploitation of the channel transfer matrix, the antenna selection criterion is the maximization of the instantaneous capacity achieved using a specific number of transmitting and receiving antenna array elements. For each environment, the proposed method applies a genetic algorithm which seeks the most advantageous subset of antenna elements. The results are based on measured and simulated channels and show that the proposed method selects array configurations that yield superior performance compared to the arrays usually employed. Furthermore, comparative analysis results are presented, with respect to a state-of-the-art algorithm.     

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.     

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.     

Joint antenna selection based on MIMO systems

Antenna selection can not only increases channel capacity in multiple input multiple output (MIMO) system, but also effectively decreases the radio frequency circuit cost. In order to achieve the antenna subset which can enlarge the system capacity rapidly, an improved methods obtaining better channel capacity was proposed, which is based on the norm and relevance of sending and receiving antenna joint selection algorithm. Simulation results indicate that the algorithm decreases a lot of computation time without any loss of system capacity.     

多天线OFDM系统中的自适应子信道分配和天线选择

针对多用户MIMO-OFDM系统,提出自适应子信道分配算法.首先将相邻的子载波分块成若干子信道,在分配子信道的时候优先考虑平均信道容量小的用户,并且将相对利用度大的子信道分配给该用户,从而在频谱利用率和公平性上具有较好的性能.为了降低发送端的硬件复杂度以及非线性因素的影响,将自适应信道分配和发送天线选择相结合,提出联合自适应子信道分配和天线选择算法.仿真结果表明,本文提出的联合算法具有较高的频谱效率和较低的中断概率.     

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。     

新的V-BLAST系统次优天线选择

该文提出新的基于ZF SIC检测的V-BLAST系统次优天线选择准则:最小化信道矩阵伪逆的最大行范数。基于贪婪选择思想,发射天线选择采用使得该范数增加最小的递增选择策略,接收天线选择采用使得该范数减少最大的递减选择策略。仿真表明所提出的新准则明显优于已有的最大第1检测层后处理信噪比准则,且相应的快速选择算法可以获得最优的基于最大最小准则的全搜索选择的大部分分集增益,而复杂度很低。     

Energy efficiency of massive MIMO wireless communication systems with antenna selection

Massive multiple-input multiple-output (MIMO) requires a large number (tens or hundreds) of base station antennas serving for much smaller number of terminals, with large gains in energy efficiency and spectral efficiency compared with traditional MIMO technology. Large scale antennas mean large scale radio frequency (RF) chains. Considering the plenty of power consumption and high cost of RF chains, antenna selection is necessary for Massive MIMO wireless communication systems in both transmitting end and receiving end. An energy efficient antenna selection algorithm based on convex optimization was proposed for Massive MIMO wireless communication systems. On the condition that the channel capacity of the cell is larger than a certain threshold, the number of transmit antenna, the subset of transmit antenna and servable mobile terminals (MTs) were jointly optimized to maximize energy efficiency. The joint optimization problem was proved in detail. The proposed algorithm is verified by analysis and numerical simulations. Good performance gain of energy efficiency is obtained comparing with no antenna selection.     

基于天线子集选择算法的DSTBC优化

为进一步提高空时码性能,在信道模型为瑞丽和莱斯慢衰落信道的情况下,文中提出了一种优化差分空时分组码的＂天线子集选择算法＂——假设发射信号为特定的Alamouti正交空时分组码,通过最大化信道参数矩阵F范数的方法,优化了MIMO系统的天线集。仿真结果表明：此方法可降低码元的比特错误率,并且可获得相当客观的分集增益和能量增益。此外,还讨论了该优化方法的适用范围。     

MIMO antenna subset selection with space-time coding

This paper treats multiple-input multiple-output (MIMO) antenna subset selection employing space-time coding. We consider two cases differentiated based on the type of channel knowledge used in the selection process. We address both the selection algorithms and the performance analysis. We first consider the case when the antenna subsets are selected based on exact channel knowledge (ECK). Our results assume the transmission of orthogonal space-time block codes (with emphasis on the Alamouti (see IEEE J. Select. Areas Commun., vol.16, p.1451-68, Oct. 1998) code). Next, we treat the case of antenna subset selection when statistical channel knowledge (SCK) is employed by the selection algorithm. This analysis is applicable to general space-time coding schemes. When ECK is available, we show that the selection algorithm chooses the antenna set that maximizes the channel Frobenius norm leading to both coding and diversity gain. When SCK is available, the selection algorithm chooses the antenna set that maximizes the determinant of the covariance of the vectorized channel leading mostly to a coding gain. In case of ECK-based selection, we provide analytical expressions for average SNR and outage probability improvement. For the case when SCK-based selection is used, we derive expressions for coding gain. We also present extensive simulation studies, validating our results.     

非相干天线选择算法

基于广义似然比检验(GLRT)与天线选择相结合,提出了平瑞利衰落信道的非相干天线选择(NON-AS)算法,天线选择和信号检测无需信道状态信息.NON-AS算法适用于多输入多输出(MIMO)系统的接收端,接收天线选择是基于每个天线瞬时接收到信号矢量的F-2范数.与相干检测的天线选择相比,NON-AS算法不需要估计信道,大大降低了系统复杂性.成对错误概率分析和仿真结果表明:在高信噪比情况下,选择有最大范数的接收天线,系统能实现和使用全部接收天线相同的分集增益.     

分布式MIMO系统发送天线选择与预编码联合设计

为了抑制多用户分布式多入多出(MIMO)系统中的同道干扰(CCI),使系统同时服务于更多用户,提出一种发送天线选择与预编码的联合设计方法。该方法立足于分布式MIMO系统基站端天线较多的特点,将下行发送天线选择与信漏噪比(SLNR)预编码相结合,通过为用户选择不同天线,从根本上减少CCI;在为每个用户选择天线时,先以信道子矩阵的迹为依据进行端口选择,再采用逐减的方法选择使SLNR损失值最小的天线,以保证每个用户对其他用户的干扰尽量小,从而达到进一步抑制CCI的目的。复杂度分析和仿真结果表明,该方法在具有较低复杂度同时,其容量性能仍可逼近最优算法;较之单纯的SLNR预编码,在相同的容量性能约束下,其能够有效增加系统同时服务的用户数。