LTE多天线系统资源分配算法与性能分析

在给出LTE（Long Term Evolution）下行多天线传输系统模型的基础上,通过理论分析得出了各多天线模式下用户接收信号信噪比（SINR）的计算公式,提出了含预编码的多用户MIMO（MU—MIMO—Pre）的调度算法。通过系统级仿真得到了不同多天线模式（包括其对应调度算法）以及不同站距下的吞吐量结果和用户公平性曲线,并对各种多天线模式的优缺点进行分析和比较。     

MIMO-OFDM系统的自适应 多用户分集及性能分析

 本文建立基于随机波束成形和自适应调制的MIMO-OFDM系统的自适应多用户分集系统模型,提出随机波束成形QR接收机的自适应多用户分集资源分配算法及其简化算法,推导在最大化系统吞吐量条件下系统的误码性能.仿真结果表明该算法在用户数很小时使系统性能收敛到特征波束成形系统的性能;当用户数增多时,采用天线分配算法的多用户分集系统能获得比特征波束成形系统更好的性能;将自适应编码调制技术运用到多用户分集系统来提高系统的实际吞吐量和误码性能.     

多天线无线数据通信系统中多用户分集的研究

研究当接收天线不少于发送天线时多输入多输出(MIMO)系统的多用户分集能力。首先从理论上分析了发送天线个数等于1和2时最大似然接收和迫零接收系统的平均吞吐量和调度增益,以及仿真分析了发送天线个数大于2时系统性能。理论分析和仿真表明：在多用户的MIMO系统中,接收的平均信噪比、用户个数、收发天线个数、接收机的结构等对于多用户分集有很大的影响。当发送天线个数为1时,接收天线较少(1,2,3)和平均信噪比为．10dB时调度增益很大,但调度增益随着天线个数和发送功率增大急剧下降。和最大似然接收相比,迫零接收具有更大的多用户分集增益,因此迫零接收机的吞吐量可以很容易超过最大似然接收机。     

采用天线选择的多用户MIMO-OFDM系统容量研究

对于采用线性预编码进行空分复用的多用户MIMO-OFDM系统的下行链路,分析了使用户间干扰完全消除的预编码矩阵设计实现过程,提出了用户端联合天线选择算法,以增大发送的数据流数,提高系统和容量,在此基础上提出了逐次递增确定用户天线的选择算法.仿真结果表明,本文所提的天线选择算法使系统和容量远高于未经天线选择时的和容量,并且次优的逐次选择算法在计算量可接受的情况下与最优联合选择算法相比获得的容量增益相差不大.     

多用户MIMO系统在有限反馈下结合预编码与调度算法的研究

捅要：研究了多用户多输入多输出（MIMO）系统在有限反馈下的一种结合单位预编码与用户调度算法的方案。在该系统中每一用户都具有多个接收天线,该方案具有较大的多用户分集增益和较小的计算复杂度,从而能够减少系统中的用户间干扰。每个用户独立地决定自己的天线合并矢量,并将最优波束矢量以及对应的估计信干噪比通过有限反馈信道反馈给基站,此时基站获取了相应的用户信道信息和用户间干扰信息。基于接收到的反馈信息,基站从预定义的码本中选取和速率最大的最优子集进行系统预编码,然后依照预编码矩阵调度欧氏距离最小的用户且用户个数不超过发送天线的数目。仿真结果显示该方案有效地改善了系统速率,特别是在用户数目较多或者信道环境较好的情况下。     

多天线系统中的多用户分集方案的性能分析及比较

在多天线多用户无线通信系统中,可以利用用户之间彼此独立的随机衰落信道的特点,结合随机波束成型技术,或者空间分集技术,或者天线选择技术,获得多用户分集增益。然而不同多用户分集方案的系统性能不尽相同。该文分析了在多输入单输出(MISO)信道中目前几种典型的多用户分集方案的性能,给出系统吞吐率的定量表达式,并综合比较了不同用户数和不同天线数对各种多用户分集方案性能的影响,为实际多天线多用户系统设计提供了有益的参考。     

存在估计误差的有限反馈MIMO性能分析

考虑的是利用有限反馈技术的下行多用户多天线系统。接收端通过信道估计获得信道状态信息,采用随机矢量量化信道信息并反馈码子的序号,发送端利用部分信道信息,采用波束成型的预编码技术实现多用户数据传输。考虑接收端信道估计存在误差,并对由此带来的系统性能影响进行分析,给出吞吐率损失的上界,以及使有限反馈系统保持固定空分复用增益,信道估计误差方差的限制条件和反馈比特数随发送功率的变化关系。     

多用户MIMO-OFDM系统中的一种新的用户调度算法

本文针对多用户环境下的多天线正交频分复用下行系统,提出了一种新的基于迫零波束成型的用户调度算法。迫零波束成型是在发送端消除用户间的干扰生成多个独立的空间子信道,来实现空间复用。由于在多天线正交频分复用下行系统中最优的用户选择算法需要遍历所有可能的用户集合,算法复杂度非常大,本文提出了一种简化算法。所提算法在保证每个用户的速率要求的情况下,通过贪婪算法来选择每个子载波上的用户集合,以最小化系统的发送功率。仿真结果表明在满足用户的速率要求下能达到很好的系统性能。     

基于满意度准则采用混合波束成型预编码的调度方法

为了满足未来无线宽带网络中多种服务质量(QoS)要求业务并存的需求,该文提出一种多天线下行系统中结合混合波束成型预编码技术基于满意度的调度方法。该调度方法联合考虑用户的QoS要求及其信道质量,灵活地使用随机波束成型和迫零波束成型预编码技术,并通过惩罚机制避免特定用户占用超出其需求的带宽,更有效地为用户分配令其满意的资源,使各种QoS要求的用户获得满意的服务。仿真结果证明,与现有的方法相比,该文提出的方法在保证用户服务质量的同时,能更好地利用系统资源。     

基于人工噪声的多用户MIMO系统加密算法

当多用户MIMO系统中的用户数多于或等于发射端天线数时,现有的基于人工噪声的物理层加密算法会导致合法用户无法正常接收。为提高此时的多用户MIMO系统的安全性,该文提出一种新的基于人工噪声的多用户MIMO系统加密算法。首先,发送端对多个合法用户进行联合处理,建立多用户联合信道状态矩阵;然后,将联合信道状态矩阵进行奇异值分解,并根据最小的奇异值进行预编码,以消除人工噪声对合法用户的影响;最后,提出一种优化功率分配的方案。仿真结果表明,该算法将多用户MIMO系统的保密容量平均增加了0.1 bit/(s?Hz),从而提高多用户MIMO系统的安全性。     

Joint Optimum Linear Precoding and Power Control Strategies for Downlink MC-CDMA Systems

In this paper we derive two iterative joint precoding and power allocation methods for downlink MC-CDMA systems with multiple transmit antennas at the base station. The first method optimally solves the so called assigned target SINRs problem, i.e. determines the precoding coefficients and the minimum sum-power allocation under a constraint on the SINR of each user. The second method instead solves the maximum sum-rate problem, i.e. maximizes the system throughput under a constraint on the overall transmit power. The main result of this paper is that the proposed joint precoding and power control solutions provide an advantage with respect to suboptimal solutions for a fully loaded system, i.e. when the number of users is equal to the spreading factor times the number of transmit antennas. For lower loads, performance of the various schemes becomes closer.     

Distributed closed-loop spatial multiplexing for uplink multiuser systems

Focusing on the uplink, where mobile users (each with a single transmit antenna) communicate with a base station with multiple antennas, we treat multiple users as antennas to enable spatial multiplexing across users. Introducing distributed closed-loop spatial multiplexing with threshold-based user selection, we propose two uplink channel-assigning strategies with limited feedback. We prove that the proposed system also outperforms the standard greedy scheme with respect to the degree of fairness, measured by the variance of the time averaged throughput. For uplink multi-antenna systems, we show that the proposed scheduling is a better choice than the greedy scheme in terms of the average BER, feedback complexity, and fairness. The numerical results corroborate our findings.     

空间相关莱斯衰落信道下基于部分信道信息的多用户预编码与调度算法

在空间相关的莱斯衰落信道模型下,针对多用户MIMO(Multiple-Input Multiple-Output)系统潜在的多用户分集增益和空间分集增益,该文提出了一种基于部分信道信息的多用户预编码与调度算法。结合部分瞬时信道信息和统计信息,利用约束最大似然估计对各用户信道矢量进行估计,然后利用估计的各用户信道调度多个用户进行预编码。仿真结果表明,该方案以较少的反馈开销,获得了较大的性能增益。     

Low Complexity Iterative-QR (IQR) Precoder Design Based on Structure Blocks for Massive MIMO System

Massive multiple-input multiple-output (MIMO) technology is a promising technique having a high spectral efficiency for future wireless systems. Counterintuitively, the practical issues of transmitted signals are being attenuated by fading, propagation limitations, and implement non-linear precoding are solved by enlarging system dimensions. However, the computational complexity of precoding grows with the system dimensions. The existence block diagonalization (BD) precoding, which completely pre-cancels the multiuser interference is very complicated to implement with the use of a large number of BS antennas, since it considers full multiplexing order. Motivated by the high performance of the BD and generalized for the case when the users have multiple antennas, we propose a structure blocks based on iterative QR decompositions (IQRDs) to compute the precoding scheme. The proposed BLIQR-based precoder designed partitioned the channel matrix into capable square-wise blocks matrix and the IQRDs are applied to the blocks channel matrix. The channel matrix is partitioned such that it can fulfill the multiplexing order for the use in Massive MIMO. The computational complexity of the proposed design is effectively reduced and the sum-rate performance is improved, especially in large number of BS antennas. The performance of the proposed scheme achieves a good trade-off between throughput and computational complexity.     

Low Complexity User Scheduling,Ordering and Transmit Covariance Matrix Optimization Algorithms for Successive Zero-Forcing Precoding

In this paper we consider user scheduling, ordering and transmit covariance matrix optimization problems under successive zero-forcing (SZF) precoding for multiuser multiple-input multiple-output downlink. We propose a heuristic user scheduling metric and an intermediate user grouping technique to develop a low complexity greedy scheduling algorithm. A suboptimal user ordering technique is also proposed for transmit covariance matrix optimization under SZF. Proposed algorithm is of low complexity, but performs closely to the highly complex exhaustive search algorithm. For transmit covariance optimization under SZF, a dirty paper coding based algorithm has been previously proposed, which is computationally very complex. In this paper, we propose a suboptimal but much simplified algorithm, which employs an iterative procedure similar to a known multiple access channel (MAC) covariance optimization algorithm, but does not involve multiple levels of covariance matrix transformations. With the proposed suboptimal user ordering the exhaustive search through all possible user orders is avoided during transmit covariance matrix optimization resulting in a significant complexity reduction, and without a significant performance penalty. Simulation results show that the proposed algorithm performs very close to the known algorithm in the low SNR region.     

MIMO Broadcast Scheduling with Limited Feedback

We consider multiuser scheduling with limited feedback of partial channel state information in MIMO broadcast channels. By using spatial multiplexing at the base station (BS) and antenna selection for each user, we propose a multiuser scheduling method that allocates independent information streams from all M transmit antennas to the M most favorable users with the highest signal-to-interference-plus-noise ratio (SINR). A close approximation of the achievable sum-rate throughput for the proposed method is obtained and shown to match the simulation results very well. Moreover, two reduced feedback scheduling approaches are proposed. In the first approach, which we shall refer to as selected feedback scheduling, the users are selected based on their SINR compared to a predesigned threshold. Only those selected users are allowed to feed back limited information to the BS. The resultant feedback load and achievable throughput are derived. It will then be demonstrated that with a proper choice of the threshold, the feedback load can be greatly reduced with a negligible performance loss. The second reduced feedback scheduling approach employs quantization for each user, in which only few bits of quantized SINR are fed back to the BS. Performance analysis will show that even with only 1-bit quantization, the proposed quantized feedback scheduling approach can exploit the multiuser diversity at the expense of slight decrease of throughput.     

Subcarrier Wise Scheduling Methods for Multi-antenna and Multi-carrier Systems

In this paper, multiuser scheduling algorithms are evaluated for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) networks. These scheduling schemes allocate M [number of transmit antennas at base station (BS)] number of independent data streams from BS to the M most favourable users experiencing the highest signal-to-interference-plus-noise-ratio (SINR). Here, SINR is used to convey the channel state information (CSI) to the BS. We have investigated the system throughput and feedback overhead attained by these scheduling schemes for different scenarios as: (a) the maximum CSI is sent to the BS by every user and (b) the maximum CSI sent to the BS corresponding to every BS antenna. The overall feedback overhead incurred by MIMO-OFDM system increases linearly with number of users, number of subcarriers and number of transmit antennas. Hence, to reduce the feedback overhead, a scheme is proposed where users with SINR values greater than or equal to a predefined threshold value are only allowed to feedback the channel state information to BS. The relation between system throughput and various thresholds is also studied. The achievable system throughput results are validated by comparing the probability density function of achieved SINR values by different scheduling schemes.

     

Low Complexity Suboptimal User Selection Algorithm for Multiuser MIMO Systems with Block Diagonalization

In downlink multiuser multiple-input multiple-output (MU-MIMO) systems, block diagonalization (BD) is a well-kown precoding technique that eliminates interuser interference. The number of simultaneously supportable users with BD is limited by the number of base station transmit antennas and the number of user receive antennas. The brute-force search for the optimal user set, however, is computationally prohibitive. Therefore, we propose a low complexity and suboptimal user selection algorithm based on block diagonalization for MU-MIMO systems. We introduce a strong tight upper bound of sum capacity as selection metric. Furthermore, we employ a substitution operation to improve system performance. The computational complexity analysis and simulation results show that the proposed algorithm achieves comparable throughput with low complexity compared to the existing algorithms.     

使用对偶分解的MU-CoMP-JT联合资源分配

在MU-Co MP-JT(Multi-User Coordinated Multiple-Points Joint Transmission)联合资源分配问题中,传统的迫零预编码矩阵会使得每根天线发送功率互不相同,当Co MP节点发射功率仅满足总功率约束时性能损失不明显,而当Co MP节点分布在不同的地理位置时将受到单节点功率约束,这势必会降低系统功率利用率。为了进一步提升系统吞吐量,基于对偶分解理论提出了一种联合预编码优化的资源分配算法。该算法以最大化用户权重速率为目标,将原优化问题分解成若干个优化的子问题,不同子问题对应不同接收天线数的联合优化问题。当子信道的发送天线数大于接收天线数时,通过多次迭代计算得到预编码矩阵,并且预编码矩阵会随着拉格朗日因子的变化而变化。仿真结果表明所提联合预编码优化的联合资源分配算法能够明显提升系统吞吐量,且提高天线功率利用效率。     

Low complexity MIMO scheduling with channel decomposition using capacity upperbound

In multiuser MIMO systems, the base station schedules transmissions to a group of users simultaneously. Since the data transmitted to each user are different, in order to avoid the inter-user interference, a transmit preprocessing technique which decomposes the multiuser MIMO downlink channel into multiple parallel independent single-user MIMO channels can be used. When the number of users is larger than the maximum that the system can support simultaneously, the base station selects a subset of users who have the best instantaneous channel quality to maximize the system throughput. Since the exhaustive search for the optimal user set is computationally prohibitive, a low complexity scheduling algorithm which aims to maximize the capacity upper bound is proposed. Simulation results show that the proposed scheduling algorithm achieves comparable total throughput as the optimal algorithm with much lower complexity.