Quantized Hybrid Precoding Design for Millimeter-Wave Large-Scale MIMO Systems

Millimeter wave(mmWave) and large-scale multiple input multiple output(MIMO) are two emerging technologies in fifth-generation wireless communication systems. The power consumption and hardware cost of radio frequency(RF) chains increase exponentially with the bit resolution of analog-to-digital converters(ADCs) and digital-to-analog converters(DACs). One promising solution is to employ few RF chains with low-bit ADCs and DACs. In this paper, we consider mmWave large-scale MIMO systems with low bits DACs and ADCs. Leveraging on the Bussgang theorem and the additive quantization noise model(AQNM), a closed-form expression of the achievable rate is derived to show the effect of the ADCs? and DACs? resolution. Moreover, an orthogonal matching pursuit(OMP) based hybrid precoding algorithm is proposed to increase the achievable rate. Our results show that the impact of DACs is more pronounced than the impact of ADCs. Furthermore, 5-bit ADCs and DACs are sufficient at the transceiver to operate without a significant performance loss.

Fairness-Oriented Hybrid Precoding for Massive MIMO Maritime Downlink Systems with Large-Scale CSIT

Different from conventional cellular networks, a maritime communication base station(BS) has to cover a much wider area due to the limitation of available BS sites. Accordingly the performance of users far away from the BS is poor in general. This renders the fairness among users a challenging issue for maritime communications. In this paper, we consider a practical massive MIMO maritime BS with hybrid digital and analog precoding. Only the large-scale channel state information at the transmitter(CSIT) is considered so as to reduce the implementation complexity and overhead of the system. On this basis, we address the problem of fairness-oriented precoding design. A max-min optimization problem is formulated and solved in an iterative way. Simulation results demonstrate that the proposed scheme performs much better than conventional hybrid precoding algorithms in terms of minimum achievable rate of all the users, for the typical three-ray maritime channel model.

Antenna selection based on large-scale fading for distributed MIMO systems

An antenna selection algorithm based on large-scale fading between the transmitter and receiver is proposed for the uplink receive antenna selection in distributed multiple-input multiple-output(D-MIMO) systems. By utilizing the radio access units(RAU) selection based on large-scale fading,the proposed algorithm decreases enormously the computational complexity. Based on the characteristics of distributed systems,an improved particle swarm optimization(PSO) has been proposed for the antenna selection after the RAU selection. In order to apply the improved PSO algorithm better in antenna selection,a general form of channel capacity was transformed into a binary expression by analyzing the formula of channel capacity. The proposed algorithm can make full use of the advantages of D-MIMO systems,and achieve near-optimal performance in terms of channel capacity with low computational complexity.

Decentralized Stabilization of Large-scale Uncertain Systems with State-Delays——LMIs Approach

This paper deals with the problem of decentralized robustcontrol for a class of interconnected uncertain systemswith state delays.The parameter uncertainties are un-known but norm-bounded.A new sufficient condition isobtained for each subsystem and overall system to be sta-bilizable via linear memoryless state feedback robust de-centralized controllers.The results depend on the size of the delays and are given in terms of linear matrix ine-qualities,so they are less conservative than those of delay-independent.Moreover,matching condition is not a necessary condition.Finally,an example is presented to illustrative the effectiveness of the proposed method.

Utilization Survey of Livestock Manure Resources in Large-scale Farms of Yangzhou

Based on surveying the conditions of large -scale farms and commercial manure in the each county of Yangzhou city, the situations and problems for utilization of livestock manure resources were grasped. After an analysis of the potential value of livestock manure, the suggestion and strategy for utilization of livestock manure resources were proposed based on the actual conditions in Yangzhou city.

ZTE HSPA+ MIMO Solution Ready for Large Scale Commercialization

ZTE Corporation announced that it has completed the Inter-Operability Test (IOT)of its 3GPP R7-basedHSPA+ MIMO solution,conducted in conjunction with mainstream terminal chip platform vendors,in July 2009.

多用户大规模LDPC-SM-MIMO联合分层消息传递检测

对于多用户大规模空间调制多输入多输出(Spatial Modulation Multiple Input Multiple Output,SM-MIMO)系统,消息传递检测(Message Passing Detection,MPD)是常用的检测算法,但其复杂度较高。针对该问题,引入基于分层消息传递机制的分层MPD(Layered MPD,LMPD)算法以加快算法收敛速度,降低算法复杂度。进一步,将低密度奇偶校验码(Low-density Parity-check,LDPC)与SM-MIMO系统相结合,提出联合LMPD-BP(Joint LMPD Belief Propagation,JLMPD-BP)算法,LMPD算法可利用BP算法反馈的概率信息,提升系统检测性能。理论分析与仿真结果表明:与传统MPD算法相比,LMPD算法在不损失误码率性能的前提下可加快算法收敛速度,当信噪比(Signal-to-Noise Ratio,SNR)为4 dB时,MPD算法需3次迭代才能达到收敛,而LMPD算法仅需2次迭代即可收敛;同时,通过与LDPC码结合,JLMPD-BP算法极大地降低了系统误码率,如在迭代次数为(2,2,2)、SNR=2 dB时,与迭代次数为(4,4,0)的LMPD-BP算法相比,JLMPD-BP算法的误码率由10-2降至5×10-3。

U6G超大规模MIMO技术

为了满足不断攀升的数据传输速率需求,扩大频带宽度、增加天线数量是直接有效手段。在6G应用场景与关键能力的驱动下,6 425~7 125 MHz频段(U6G)超大规模多输入多输出(MIMO)技术成为满足6G需求的潜在使能技术之一。基于U6G超大规模MIMO系统的天线形态及信道特征,针对该技术面临的成本、开销、复杂度三重挑战,提出高效能U6G超大规模MIMO无线传输的总体设计目标,并展望其未来研究趋势。

超大规模MIMO-OTFS系统上行辅助的下行信道估计方法

针对超大规模多输入多输出(MIMO,multiple-input multiple-output)正交时频空间(OTFS,orthogonal time frequency space)系统,提出了一种适用于高速移动场景的低复杂度下行信道估计方法。不同于现有研究,该方法考虑了超大规模MIMO-OTFS系统显著的空间非平稳特性,基于可视路径区域提出了一种低复杂度的增强型稀疏正交匹配追踪算法,利用频分双工(FDD,frequency-division duplex)模式中上下行信道的映射关系实现上行辅助的下行信道估计。仿真结果表明,所提上行辅助的下行信道估计方法能够充分考虑信道非平稳特性,在降低计算复杂度的同时显著提高信道估计性能,并且在高速移动物联网场景下表现良好。

基于大规模MIMO-OFDM系统的5G通信信道估计算法研究

信道估计对宽带无线移动通信系统的具体实现至关重要。本文介绍了大规模MIMO-OFDM系统的通信原理,指出5G无线信道存在的局限性,再根据信道衰落、多径效应等问题的成因,提出基于压缩感知理论的信道估计算法,并以MATLAB为仿真平台,设置了载波数量、稀疏度、载波频率等仿真参数,在不同的信噪比下开展了算法性能检验。结果显示,稀疏度自适应匹配追踪算法的性能表现最佳。

Formation conditions and exploration direction of large natural gas reservoirs in the oil-prone Bohai Bay Basin, East China

The Bohai Bay Basin is a typical oil-prone basin, in which natural gas geological reserves have a small proportion. In this basin, the gas source rock is largely medium-deep lake mudstone with oil-prone type Ⅱ2-Ⅱ1 kerogens, and natural gas preservation conditions are poor due to active late tectonic movements. The formation conditions of large natural gas fields in the Bohai Bay Basin have been elusive. Based on the exploration results of Bohai Bay Basin and comparison with large gas fields in China and abroad, the formation conditions of conventional large-scale natural gas reservoirs in the Bohai Bay Basin were examined from accumulation dynamics, structure and sedimentation. The results show that the formation conditions of conventional large natural gas reservoirs in Bohai Bay Basin mainly include one core element and two key elements. The core factor is the strong sealing of Paleogene "quilt-like" overpressure mudstone. The two key factors include the rapid maturation and high-intensity gas generation of source rock in the late stage and large scale reservoir. On this basis, large-scale nature gas accumulation models in the Bohai Bay Basin have been worked out, including regional overpressure mudstone enriching model, local overpressure mudstone depleting model, sand-rich sedimentary subsag depleting model and late strongly-developed fault depleting model. It is found that Bozhong sag, northern Liaozhong sag and Banqiao sag have favorable conditions for the formation of large-scale natural gas reservoirs, and are worth exploring. The study results have important guidance for exploration of large scale natural gas reservoirs in the Bohai Bay Basin.

基于特征融合的大规模MIMO系统CSI反馈

信道状态信息(Channel State Information,CSI)反馈是大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的一个关键问题。大规模MIMO系统中基站天线数量巨大,CSI反馈出现了反馈开销大、反馈精度低等问题。为了降低反馈开销,提高反馈精度,采用深度学习方法,提出了一种基于特征融合的CSI反馈网络(Feature Fusion Net,FFNet)。利用基于注意力机制的特征融合在编码器中融合不同尺度的CSI特征,并在解码器中使用多通道多分辨率卷积网络以及通道重排,从而高精度地重建压缩后的CSI。仿真结果表明,与几种经典的深度学习CSI反馈方法相比,在室内和室外信道条件下,均具有更高的反馈精度。

基于阵列域大尺度衰落模型的1-bit大规模MIMO系统性能的分析

该文研究在阵列域大尺度衰落模型下,基站端天线仅仅配备了1-bit精度的模数转换器时大规模MIMO系统的性能。首先给出了系统采用最大比合并接收机时,用户上行可达速率的闭式表达式。然后分析了1-bit系统的功率效率性能,并将该1-bit系统与传统的具有无穷精度模数转换器的系统性能相比较。通过计算机仿真验证了该文的分析结果。该文指出基站端可以安装更多的天线来弥补1-bit精度的模数转换器所造成的性能损失。

大规模MIMO-OFDM系统峰均值比降低方案改进

针对低复杂度大规模MIMO-OFDM系统峰均值比降低方案给出一种改进。先裁剪OFDM信号的峰值,利用曲线拟合计算出削峰信号,再将其在预留的子载波上发送,由此降低预留天线上OFDM信号的峰均值比。在Matlab中随机产生10个用户的发送符号,预留12个子载波,采用改进的低复杂度方案对发送符号进行处理,得到信号的互补累计分布函数(CCDF)。仿真结果表明,在CCDF概率等于1%时,改进方案比原低复杂度方案的PAPR降低性能可提高约2.5dB。

基于边动态调度的多用户大规模SM-MIMO消息传递检测

消息传递检测(Message Passing Detection, MPD)算法是一种基于置信度传播的检测算法,非常适用于多用户大规模空间调制多输入多输出(Spatial Modulation Multiple Input Multiple Output, SM-MIMO)系统,但传统MPD算法复杂度仍然较高。针对该问题,研究基于边动态调度的MPD算法,根据观测节点与变量节点可靠性,提出基于观测节点可靠性MPD(the Reliability of Observation Nodes MPD, TRON-MPD)和基于双节点可靠性MPD(the Reliability of Observation and Variable Nodes MPD, TROVN-MPD)两种边动态调度方案,在迭代检测过程中,根据边的可靠性,实现边信息的动态更新,以进一步降低算法复杂度。理论分析与仿真结果表明:与传统MPD算法相比,TRON-MPD与TROVN-MPD方案均可实现以损失较小误码率性能为代价大幅降低算法复杂度;同时,通过适当设定边动态调度方案的阈值,可在系统误码率与算法复杂度之间取得较好折中。

基于天线选择技术的大规模下行MU-MIMO系统能效分析

为了提高大规模多用户多输入多输出(MU-MIMO)下行系统的能效,提出了一种基于发送天线选择技术的能效优化机制。首先建立了同时考虑发送功率与电路功耗的新的系统功耗模型,并基于该模型,分析了基站配置天线数目与所有接收终端用户数目对系统总功耗及能效的影响。然后通过理论推导得到了系统能效最优时的最优天线选择数目,并与使用全部天线时的系统能效进行比较。仿真结果表明,所提出的发送天线选择机制通过优化激活部分基站天线能够明显提高系统能效。在用户数为10、发送功率分别为40 W和10 W时,与使用全部天线相比,天线选择技术能够分别使得系统能效获得大约12%和78%的性能增益。

基于全息RIS的太赫兹MIMO信道估计研究

基于全息通信研究的大背景,提出了可重构智能表面(RIS)的全息版本。推导出了全息RIS的光束模式,并通过分析表明,理想全息RIS的光束模式可以近似于具有更实用硬件架构的超密集RIS的光束模式;提出了一种闭环信道估计(CE)方案,是以有效估计表征全息RIS辅助的THz大规模MIMO系统的宽带信道;为了减少导频开销,利用THzMIMO信道在角域和延迟域中的双重稀疏性,引入了一种基于压缩感知的CE算法。仿真结果证明,全息RIS优于非全息RIS,提出的CE方案有效。

超大规模MIMO系统中稀疏度自适应的极化域信道估计

针对超大规模多输入多输出(Multiple-Input Multiple-Output, MIMO)系统中的极化域信道估计调整,提出了一种基于压缩感知的自适应极化域稀疏度同步正交匹配追踪(Adaptive Polar-domain Simultaneous Orthogonal Matching Pursuit, AP-SOMP)算法。AP-SOMP算法利用信道相关度设计合理的判决准则来估计极化域信道稀疏度,从而能在极化域信道稀疏度未知的情况下完成信道估计。该算法有效地克服了传统算法对信道稀疏度的依赖,具有更强的实用性。仿真结果表明,AP-SOMP算法在归一化均方误差性能表现上优于传统的基于网格的极化域同步正交匹配追踪(Polar-domain Simultaneous Orthogonal Matching Pursuit, P-SOMP)算法,且算法复杂度并未明显增加。

超大规模MIMO系统中基于交叠可视区域的功率分配方法

该文解决了超大规模多输入多输出(MIMO)系统中不同用户的可视区域(VR)存在相互交叠时的下行功率分配问题。考虑单个基站服务多个单天线用户的超大规模MIMO通信场景,由于基站配备的阵列较大,各个用户受障碍物遮挡仅能与基站部分天线进行通信,这部分天线即为各用户的可视区域。该文考虑不同用户的可视区域分布两两交叠,并依此划分子阵,并在各子阵上进行规则化迫零预编码以降低复杂度。接着基于大维随机矩阵理论,推导了系统下行遍历和速率的确定性近似表达式。然后,通过最大化该表达式,给出了基于统计信道状态信息的最优用户功率分配方法的闭式解。最后,仿真结果表明,和速率近似表达式的精度很高,所提功率分配方法能有效提高系统性能。