多用户准正交空时分组码MIMO系统的下行链路预编码

该文针对采用准正交空时分组码的多用户多输入多输出(QSTBC-MU-MIMO)系统,给出一种基于信漏噪声比(SLNR)准则的下行链路干扰抑制预编码方法。通过迭代优化,该文方法可以改善原始SLNR方法中由干扰用户增加所引起的系统性能下降问题,增强了算法对系统用户数的鲁棒性。由于在优化目标中考虑了系统噪声,该方法可通过恰当选取复比例因子来实现优化问题的简化求解。仿真结果验证了该文方法的有效性。     

最大化SLNR及软频率复用的比例公平调度算法

针对 LTE-Advanced 系统中小区间干扰及用户公平性问题,提出了最大化信漏噪比(SLNR)及软频率复用的比例公平调度算法(SLNR–SFR–PF)。该算法通过基于最大化 SLNR的分布式预编码和软频率复用的方法抑制小区间干扰,并结合比例公平调度算法,提高用户的公平性。仿真结果表明,与 SLNR–PF 算法相比,SLNR–SFR–PF 算法使系统容量提高了约7.9%,同时,小区边缘的平均用户数据速率提高了11.5%。     

基于Khatri-Rao积的3D MU-MIMO预编码方法

为了解决下行三维(3D)多用户(Multi-User,MU)多输入多输出(MIMO)系统中的共信道干扰问题,提出了一种基于Khatri-Rao积的3D预编码方法。该方法首先产生离线码本,然后各用户分别选择与水平维和垂直维信道最匹配的的码字并将其反馈给基站端,最后,根据提出的KhatriRao积基站端构造3D预编码矩阵,对不同用户数据进行处理。理论分析和仿真结果表明,该方法可有效地匹配真实信道状态信息(CSI),而且可有效地抑制共信道干扰,提升系统误码性能,对系统的性能优化有一定的参考价值。     

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。     

单中继多用户MIMO系统中继预编码新方案

针对单中继多用户多输入多输出(MIMO)通信系统中用户间干扰问题,提出了一种可以有效抑制用户间干扰的中继接收和预编码方案。该方案在中继端对信号进行迫零(ZF)接收,利用最大化信漏噪比(SLNR)准则设计中继端预编码向量,并针对难以获得最优解的情况,利用迭代解出次优解。仿真结果表明,与中继端ZF预编码相比,SLNR预编码更好地抑制了用户间干扰,相同和速率性能下,基站发送端功率比ZF预编码减少4 dB,具有实用价值。     

多波束移动卫星系统的自适应开环预编码方法

对于卫星移动通信系统,由于卫星与地面终端之间的相对运动以及星地间传输延迟,传统的基于理想信道信息的预编码方法是不适用的。针对这一问题,提出了一种基于开环信道估计的预编码方法。卫星端利用开环获取的部分信道信息实现多波束联合预编码,并导出了系统传输速率的闭合解析表达式。此外,为了克服强干扰环境下多波束预编码系统性能恶化问题,提出了一种自适应预编码传输方法。卫星发射机依据开环获得的慢时变用户位置信道信息和信道统计量信息,自适应地选择预编码方法或传统频率复用方法,实现最优的系统性能。理论分析和仿真结果表明,与传统的干扰抑制方法相比,所提方法能实现更优的系统性能,同时也克服了传统预编码方法的局限性。     

基于SLNR预编码的感知无线电技术

感知无线电技术允许多个感知用户之间公平协调共享频谱,由于基站在同一频率同一时隙下可以和几个移动终端同时通信,这将引起用户间的共信道干扰（CCI）。本文引入了预编码的思想,多个感知用户感知并共享频谱时在发射端加入预编码矩阵,基于信漏噪比（SLNR）的预编码同时消除了共信道干扰（CCI）和噪声,提高了感知系统性能。仿真结果说明采用了基于SLNR的预编码后系统性能得到了明显改善,且发射天线数越多,系统性能越好。     

多小区MIMO系统中基于泄漏的多用户下行链路预编码新算法

针对多小区MIMO系统的多用户下行链路预编码设计是未来移动通信系统研究的重要内容之一。本文在蜂窝多小区MIMO通信环境下,基于信漏噪声比(Signal-to-Leakage-Noise Ratio,SLNR)最大准则,提出了改进的多用户下行链路线性预编码算法。通过在优化目标中考虑接收端白化滤波器的影响,该预编码算法实现了对用户间干扰和小区间干扰(OCI)的同步抑制消除;在求解预编码矩阵优化问题时,本文基于QR分解提出了一种新的低复杂度求解算法,该算法能以较低的计算复杂度实现系统性能的有效提升。仿真实验结果表明,本文的改进SLNR线性预编码算法能有效抑制小区间干扰(OCI)的影响,使多小区MIMO系统获得更高的和速率(Sum Rate)容量。      

基于SLNR与矢量扰动的毫米波大规模MIMO双层混合预编码算法

在多用户毫米波大规模多入多出（Multiple Input Multiple Output,MIMO）系统中，针对现有基于信漏噪比（Signal-to-Leakage-plus-Noise Ratio,SLNR）准则的混合预编码算法未考虑用户信道质量不同和信道矩阵具有病态性的问题，本文提出了一种联合SLNR与矢量扰动（Vector Perturbation,VP）的双层混合预编码算法.该算法分两层设计预编码矩阵.第一层，考虑用户信道质量不同，提出了基于SLNR准则的匹配加权算法.该算法充分考虑信道系数、预编码矢量和组合矢量的影响设计信道加权因子，利用加权因子对SLNR算法进行改进，从而设计更优的混合预编码器，消除用户间干扰.第二层，根据第一层获取到的等效基带信道，利用迫零（Zero Force,ZF）消除用户天线间干扰.考虑到信道矩阵具有病态性，采用VP算法进一步处理.通过在发射信号上添加扰动矢量减小发射端功率增强的影响.仿真结果表明，相较于同类混合预编码算法，所提算法可以获取更好的误码率（Bit Error Rate,BER）性能和频谱效率.     

抑制小区间干扰的改进信漏噪比预编码方法

针对下行多用户MIMO系统在多小区环境中系统性能下降的问题,在传统信漏噪比(SLNR)预编码基础上,提出在接收端采用白化滤波器抑制干扰,发射端考虑邻小区干扰加噪声协方差矩阵的预编码方法,消除用户间干扰的同时能很好地抑制小区间干扰,改善系统性能.仿真结果表明,改进算法在最大化新定义的等效信漏噪比的过程中,由于引入的等效信道考虑了小区间干扰,相对于传统算法,可明显提高系统容量.     

基于SLNR准则的MU-MIMO下行链路的预编码与用户调度

在多用户MIMO（MU-MIMO）系统的下行链路中,为了降低用户端的处理复杂度,发射端预编码的设计对多用户MIMO系统的性能非常重要,基于信漏噪比（SLNR）准则的预编码技术由于同时考虑了共信道干扰（CCI）和噪声,提高了系统的性能,但基站同时服务的用户有限,基于信漏噪比最大的用户调度能够合理地选择用户,提高了多用户分级增益,使系统获得更高的系统容量和系统性能。仿真结果表明,基于SLNR准则的多用户预编码在系统容量和误码率方面要优于单一考虑CCI的迫零预编码（ZF）和单一考虑噪声的最大化每个用户接收到信号的信噪比预编码（MRT）。在采用SLNR预编码的条件下,信漏噪比最大的用户调度系统性比轮询调度和最大信道增益调度好,并且随着待选用户数的增多,不会给系统的调度策略带来很大的影响。     

Spectral efficiency evaluation of full‐duplex mode of communications based on SLNR approach

Full‐duplex (FD) mode of communication with efficient transmission scheme is a promising approach for 5G wireless systems by improving the spectral efficiency. This can be attained by making use of various precoding approaches. We propose a new co‐channel interference (CCI)‐aware improvement to signal‐to‐leakage‐and‐noise ratio (SLNR) technique and a suppression filter at the receiver to whiten the interference for the downlink channel. As well, for the uplink (UL) communication, we propose a self‐interference (SI)‐aware enhancement to SLNR scheme and designing a precoder using self‐interference plus noise covariance matrix. The total spectral efficiency is obtained from the sum‐rates of both downlink and uplink communication systems. Simulation results verify that the spectral efficiency (SE) of FD using the proposed scheme performs well relative to the half‐duplex system for all Rician factor and for small powers at the base station (BS) and UL communication channel users. Moreover, as the number of users grows, which entails that as the number of receiving antennas greater than the number of antennas at the BS the SLNR scheme still works, nonetheless, zero‐forcing (ZF) and block‐diagonalization (BD) precoding schemes failed. This is due to the fact that designing a precoder based on SLNR scheme supports multiple numbers of antennas at the base station and users compared with ZF and BD by compromising the interference and noise. However, for the cases of ZF and BD approaches failed due to both schemes require the number of transmit antennas at the BS to be larger than the sum of the receiving antennas at all users.     

Efficient Beamforming by SLNR Maximization for Dual-Hop Relay Assisted MIMO Network

Relay beamforming is a key signal processing technique to mitigate interference in single and multiuser multiple input and multiple output (MIMO) wireless communication networks. In MIMO relay networks interference cancellation is an essential task of the study to get optimal network capacity. In this paper, the problem of interference resulted from leakage signal from desired signal for intended antenna, which causing interference to other antennas of the same user with multiple antennas is tackled. The criterion of signal to leakage plus noise ratio (SLNR) maximization is considered to design a vector by vector relay downlink precoding based on Fukunaga Koontz transform. This can control the interference among multiple antennas and maximize SLNR. However, matched filter beamforming is utilized at the relay receiving side that can increase output signal to noise ratio at the relay node. The channel state information of both the channel between source to relay and relay to destination is assumed at the relay node. The effectiveness of the proposed technique is studied and compared with conventional relay precoding techniques. Simulation is carried out in MATLAB environment using ideal channel conditions. This study demonstrates that, the proposed scheme for relay assisted MIMO networks can improve overall system performance in terms of ergodic capacity.     

大规模MIMO时分双工系统的鲁棒预编码设计

对于采用大规模MIMO技术的时分双工系统,天线互易误差会破坏上下行信道互易特性,大幅降低预编码算法下行传输性能。由于实际系统难以完全消除天线互易误差,该文以最大化各用户平均信泄噪比为目标,根据天线互易误差的统计特性,设计了对该误差具有鲁棒性的线性预编码算法。同时为了进一步降低用户接收端的等效噪声功率,该文还将该线性鲁棒预编码算法扩展为基于矢量扰动的非线性鲁棒预编码算法,并通过减格辅助技术降低其扰动矢量求解复杂度,使其更适用于大规模MIMO系统应用。计算机仿真结果表明在存在基站天线互易误差条件下,该文所提出的线性与非线性鲁棒性预编码算法的性能均优于传统预编码算法的性能。     

基于半正定松弛的MIMO系统下行链路预编码方案

多用户多输入多输出系统下行链路中,信漏噪比最大化的预编码方法的发射功率控制方式并不能有效保证用户可达的信漏噪比,该文提出追求信漏噪比约束下最小化发射总功率的预编码器设计方案。利用半正定松弛的方法对目标问题可以进行有效地求解,并且在设计目标中增加功率约束条件,能有效降低基站的发射总功率。仿真结果显示相比于信漏噪比最大化方法,该文提出的方案在满足较大的信漏噪比门限时具有更好的系统误码性能和更低的发射总功率,并且随着信噪比的增加,其发射总功率不断降低。     

Power allocation scheme based on sum capacity maximization for signal‐to‐leakage‐and‐noise ratio precoded multiuser multiple‐input single‐output downlink

This paper proposes a power allocation scheme to maximize the sum capacity of all users for signal‐to‐leakage‐and‐noise ratio (SLNR) precoded multiuser multiple‐input single‐output downlink. The designed scheme tries to explore the effect of the power allocation for the SLNR precoded multiuser multiple‐input single‐output system on sum capacity performance. This power allocation problem can be formulated as an optimization problem. With high signal‐to‐interference‐plus‐noise ratio assumption, it can be converted into a convex optimization problem through the geometric programming and hence can be solved efficiently. Because the assumption of high signal‐to‐interference‐plus‐noise ratio cannot be always satisfied in practice, we design a globally optimal solution algorithm based on a combination of branch and bound framework and convex relaxation techniques. Theoretically, the proposed scheme can provide optimal power allocation in sum capacity maximization. Then, we further propose a judgement‐decision algorithm to achieve a trade‐off between the optimality and computational complexity. The simulation results also show that, with the proposed scheme, the sum capacity of all the users can be improved compared with three existing power allocation schemes. Meanwhile, some meaningful conclusions about the effect of the further power allocation based on the SLNR precoding have been also acquired. The performance improvement of the maximum sum capacity power allocation scheme relates to the transmit antenna number and embodies different variation trends in allusion to the different equipped transmit antenna number as the signal‐to‐noise ratio (SNR) changes.Copyright © 2013 John Wiley & Sons, Ltd.