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。     

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。     

Optimal transmit antenna selection using hybrid algorithm for massive MIMO technology

Massive multiple input multiple output (M-MIMO) methods make reference to a useful method for using multipath propagation to communicate and receive multiple data signals at once over a single radio channel. To simultaneously transfer numerous data streams, it makes use of various antennas. The quantity of power used grows as the quantity of antennas rises. As a result, choosing the best transmit antennas, which is a major difficulty in M-MIMO systems, becomes important. In this research, “Hybrid Sea Lion-Whale Algorithm (HS-WA)” is introduced by choosing a best transmit antenna while taking into account several objectives. This method optimizes overall capacity and efficiency. The chosen method combines the “Whale Optimization Algorithm (WOA) and Sea Lion Optimization Algorithm (SLnO)” that determines which antenna should be chosen while also optimizing the antenna quantity. Finally, energy efficiency (EE) and capacity analysis results demonstrate that the provided approach is superior to all other models.     

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。     

面向5G的大规模MIMO天线的波束规划及天线选型探讨

大规模MIMO天线技术作为5G技术的关键,在解决超高容量和多用户连接的问题上极具优势。天线的实际增益效果与多波束的管理和天线的选型紧密相关。基于此,本文先行阐述了大规模MIMO天线的波束形成技术,提出了不同场景下的多套波束规划方案,并进一步地对不同覆盖场景给出天线选型建议,为5G的网络设计与建设提供切实可行的参考依据。     

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.     

Novel transmit antenna selection strategy for massive MIMO downlink channel

In this paper, we deal with the problem of acquiring the channel state information (CSI) at the transmitter in large-scale multiple input multiple output (MIMO) systems, so-called massive MIMO systems. Clearly, obtaining CSI plays a central role to provide high system performance. Even though, in frequency-division duplexed systems, acquiring this information requires a prohibitive amount of feedback, since it increases with the number of transmit antenna. In this work, we design an efficient transmit antenna selection strategy aware of the amount of required CSI for a point-to-multipoint transmission in massive MIMO systems. The proposed strategy provides high sum-rate with limited CSI feedback and limited computational complexity. Innovatively, the antenna selection in our strategy is performed in a decentralized fashion successively at the receiving users. Two schemes are proposed in this work to perform the antenna selection at each user. Next, taking into consideration that the large-scale MIMO transmitter suffers from imperfect knowledge of CSI, we design a new performance criterion. Computer simulations validate that, when the CSI is perfectly known, the proposed strategy is able to achieve high performance in terms of system sum-rate while a significant reduction in both CSI feedback overhead and computational complexity is observed. Moreover, assuming imperfect CSI, the new proposed criterion achieves higher performance when the estimation accuracy is low and at high SNR regime.     

Low-complexity transmit antenna selection algorithm formassive MIMO

Massive multiple input multiple output (MIMO) systems can increase capacity and reliability greatly. However,extremely high hardware costs and computational complexity lead to the demand for reasonable antenna selection.Aiming at the problem that the traditional antenna selection algorithm based on maximizing sum capacity has largecomplexity and worse bit error rate (BER) performance, a two-step selection algorithm is proposed, which selectsa part of the antennas based on the norm-based antenna selection (NBS) firstly, and then selects the antenna basedon maximizing capacity via convex optimization. The simulation results show that the improved algorithm has betterBER performance than the traditional algorithms. At the same time, it reduces computational complexity greatly.     

基于联合用户分组及天线选择的大规模MIMO波束成形

大规模MIMO系统中,当小区用户数与基站天线数较大时,各用户的信道条件不尽相同,提出一种适用于大规模MIMO下行链路的基于联合用户分组及天线选择的迫零波束成形算法。将用户分成两组,选择信道条件较优的一组用户来接收信号,并为每一个发送数据流选择最优的基站天线组合进行通信,以较小的性能损失,换取大规模MIMO 射频电路的成本与功耗的大幅度降低。仿真结果证明,该算法能够较好地实现系统性能与硬件复杂度的折中。     

基于混合精度ADC量化的大规模MIMO系统能效联合优化算法

针对在接收端采用混合精度的数模转换器（analog-to-digitalconverter,ADC）的大规模MIMO系统上行链路,提出一种能效优化算法。在采用最大比合并（maximumratiocombining,MRC）检测算法的基础上,推导得到系统上行链路能效的近似闭式表达式。首先确定高精度ADC的占比率,再以低分辨率ADC的量化精度、用户的发射功率和基站端天线数为优化目标的3个变量进行交替迭代得出最优的能效值;并进一步通过调整高精度ADC的占比率分析其对能效和频效的影响。仿真结果表明,所提算法具有较高的能效值,并且可以通过调整占比率实现能效与频效更好地折中。     

大规模MIMO系统中基于能效最大化的资源联合优化算法

针对信道条件未知的多小区大规模多输入多输出（MIMO）系统,提出一种对导频序列长度、导频符号功率以及数据符号功率进行联合优化的资源分配算法。采用最大比合并（MRC）接收,考虑电功率和导频污染的影响,并对最大传输功率进行约束从而建立起以能效（EE）最大化为目标的非凸函数模型。根据分数规划的性质,首先将分数形式转化成减式形式,进而分解成一系列凸函数之差（DC）的问题,最后采用交替优化算法联合调整 3 个变量从而达到能效最大化的目标。仿真结果表明,随着最大符号传输功率的增加,所提方案仍然能保持良好系统能效性能。     

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.     

A smart multi-user massive MIMO system for next G Wireless communications using evolutionary optimized antenna selection

Recently multiserver queues with setup times have been extensively studied because they have applications in power-saving data centers. A challenging model is the M/M/c/Setup queue where a server is turned off when it is idle and is turned on if there are some waiting jobs. Recently, Gandhi et al. (in: Proceedings of the ACM SIGMETRICS, pp. 153–166, ACM, 2013; Queueing Syst. 77(2):177–209, 2014) obtain the generating function of the number of jobs in the system, as well as the Laplace transform of the response time using the recursive renewal reward approach and the distributional Little’s law (Keilson and Servi in Oper Res Lett 7(5):223– 227, 1988). In this paper, we derive exact solutions for the joint stationary queue length distribution of the same model using two alternative methodologies: generating function approach and matrix analytic method. The generating function approach yields exact closed form expressions for the joint stationary queue length distribution and the conditional decomposition formula. On the other hand, the matrix analytic approach leads to an exact recursive algorithm to calculate the joint stationary distribution and performance measures so as to provide some application insights.     

一种形成赋形锥状波束的双反射面天线

在许多应用场合,需要用到具有锥状赋形波束方向图的天线。以环焦天线为基础,改变设计参数,可以根据需要方便地设计出具有赋形锥状波束的双反射面天线。这种天线与其他形式可形成锥状波束的天线相比具有许多优点。     

基于天线互耦影响的大规模MIMO TDD系统信道互易性校准算法

射频器件的非理想性和信道的时变特性影响大规模多输入多输出（multiple-input multiple-output，MIMO）系统时分双工（time division duplex，TDD）模式的信道互易性，天线阵列的耦合效应影响信道矩阵的相关性.文中提出一种基于互耦影响的大规模MIMO互易性联合校准算法，将天线之间的互耦效应引入信道矩阵，采用OTA（over-the-air）校准算法和基于自回归（auto-regression，AR）模型预测信道矩阵相结合的方案实现同时校准射频非理想性和信道时变特性引起的互易性损失.仿真结果表明，该算法能够提高系统容量，降低误码率，有效地补偿互易性损失.本文算法可以为大规模MIMO系统的多因素联合校准提供参考.     

一种用于MIMO检测中的QR快速分解算法

针对多输入多输出系统中常用的非线性检测算法,如排序QR分解(Sorted QR Decomposition,SQRD)、球型译码（Sphere Decoding,SD）、K-Best或QRM（QR Decomposition and M algorithm）等,提出了一种具有最优检测顺序的QR快速分解方法,作为检测前的预处理操作。该算法首先对信道矩阵进行第一次QR分解,根据所得上三角矩阵R可确定最优的检测顺序,并按该顺序对R进行列重排。然后对R进行第二次QR分解,即得具有最优检测顺序的QR分解结果。与现有的基于R对角元素的模值排序的QR分解算法相比,本算法可保证检测顺序最优从而性能最优。仿真结果表明天线配置为4*4和6*6时,在误码率10^-3处可节约信噪比分别为:1dB和2dB;与现有的基于信干噪比排序的QR分解算法相比,本算法与其性能一致的基础上可节约25%的复乘法次数和33%的复加法次数。      

Variable-phase-shift-based RF-baseband codesign for MIMO antenna selection

We introduce a novel soft antenna selection approach for multiple antenna systems through a joint design of both RF (radio frequency) and baseband signal processing. When only a limited number of frequency converters are available, conventional antenna selection schemes show severe performance degradation in most fading channels. To alleviate those degradations, we propose to adopt a transformation of the signals in the RF domain that requires only simple, variable phase shifters and combiners to reduce the number of RF chains. The constrained optimum design of these shifters, adapting to the channel state, is given in analytical form, which requires no search or iterations. The resulting system shows a significant performance advantage for both correlated and uncorrelated channels. The technique works for both transmitter and receiver design, which leads to the joint transceiver antenna selection. When only a single information stream is transmitted through the channel, the new design can achieve the same SNR gain as the full-complexity system while requiring, at most, two RF chains. With multiple information streams transmitted, it is demonstrated by computer experiments that the capacity performance is close to optimum.     

Efficient capacity-based antenna selection for MIMO systems

The achieved capacity of the multiple-input-multiple-output wireless channel is typically dependent on the array configurations at the transmitter and receiver. Maximizing system capacity or throughput therefore requires that the arrays adapt to changing channel conditions, which may be accomplished by selecting an appropriate subset of available antenna elements for connection to the electronic transmit and receive modules. This work presents algorithms, derived using relatively straightforward information theoretic considerations, for efficiently and effectively selecting the antenna elements. Computational examples using a realistic channel model for indoor environments illustrate the performance of the techniques.     

适用于大规模MIMO系统的低复杂度RZF-SOR预编码算法

在大规模多输入多输出(MIMO)系统中,为了降低传统预编码算法的复杂度,在原有正则化迫零(RZF)预编码算法的基础上,提出用超松驰迭代(SOR)法代替矩阵求逆的高复杂度运算,得到一种改进算法RZF-SOR,并应用随机矩阵原理得出其最优相关参数的近似表达式和取值的必要条件.实验仿真表明,提出的RZF-SOR预编码算法与RZF预编码相比有效地降低了一个数量级的复杂度,在很小的迭代次数下达到接近于RZF预编码的误码率性能,并且优于基于Neumann级数预编码算法的误码率性能.     

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。