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.     

Sum Capacity of Multiuser MIMO Broadcast Channels with Block Diagonalization

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with dirty paper coding (DPC). However, algorithms that approach the DPC sum capacity do not appear viable in the forseeable future, which motivates lower complexity interference suppression techniques. Block diagonalization (BD) is a linear preceding technique for downlink multiuser MIMO systems. With perfect channel knowledge at the transmitter, BD can eliminate other users' interference at each receiver. In this paper, we study the sum capacity of BD with and without receive antenna selection. We analytically compare BD without receive antenna selection to DPC for a set of given channels. It is shown that (1) if the user channels are orthogonal to each other, then BD achieves the same sum capacity as DPC; (2) if the user channels lie in the same subspace, then the gain of DPC over BD can be upper bounded by the minimum of the number of transmit and receive antennas. These observations also hold for BD with receive antenna selection. Further, we study the ergodic sum capacity of BD with and without receive antenna selection in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is proposed for easy estimation of the gap between the sum capacity of DPC and BD without receive antenna selection.     

Successive User Scheduling Schemes for a Multiuser MIMO System Employing Generalized Channel Inversion

Generalized channel inversion (GCI) is a precoding technique for multiuser multiple-input multiple-output system. While producing each user’s precoding matrix, GCI takes into account noise and thus it is more robust compared with alternative techniques such as block diagonalization technique in terms of sum rate capacity and frame error rate. In this paper, two suboptimal multiuser scheduling schemes for GCI are proposed that by scheduling a subset of mobile users nearly maximize the sum rate capacity. They employ an iterative approach involving a number of search steps. At each step, unselected mobile users are evaluated one by one, and only one of them is chosen according to given criteria. It is shown via computer simulations that the proposed schemes are capable of achieving a large portion of the sum rate capacity that is offered by an exhaustive search. The performance of the proposed multiuser scheduling schemes is evaluated when the antenna mutual coupling effects are taken into account at the mobile users’ sides. Numerical results reveal that the presence of antenna mutual coupling can result in an increased sum rate capacity when the array inter-element spacing is in the range of 0.3–0.4 wavelength.     

User Selection Algorithms for MU‐MIMO Systems with Coordinated Beamforming

In this paper, we propose two novel user selection algorithms for multiuser multiple‐input and multiple‐output downlink wireless systems, in which both a base station (BS) and mobile stations (MSs) are equipped with multiple antennas. Linear transmit beamforming at the BS and receive combining at the MSs are used to avoid interference between users and find a better sum‐rate capacity performance. An optimal technique for selecting users would entail an exhaustive search, which in practice becomes computationally complex for a realistic number of users. Suboptimal algorithms with low complexity are proposed for a coordinated beamforming scheme. Simulation results show that the performance of the proposed algorithms is better than that provided by previous algorithms and is very close to an optimal approach with reduced complexity.     

空间相关信道下的交替多用户调度算法

针对多用户多输入多输出（MIMO）系统,研究了空间相关信道下的多用户调度问题。通过推导用户信道向量夹角的统计特征,分析了多用户调度算法对所调度用户间正交性的影响。分析结果表明在空间相关信道下现有基于串行搜索的调度方法在统计意义上降低了被调度用户之间的正交性,从而导致基于串行搜索的调度算法与最优的穷举搜索之间存在较大的性能差距。为了提高相关信道下多用户MIMO系统的性能,提出了一种基于交替搜索的多用户调度算法及其低复杂度实现方法。仿真结果表明,所提出的用户调度算法能够有效地弥补基于串行搜索的调度方法的性能损失,在空间独立信道和空间相关信道下以较低的计算复杂度获得接近穷举搜索的最优性能。      

回传链路容量受限条件下分布式大规模MIMO系统的联合用户调度及天线选择(英文)

Massive MIMO systems offer a high spatial resolution that can drastically increase the spectral and/or energy efficiency by employing a large number of antennas at the base station(BS).In a distributed massive MIMO system,the capacity of fiber backhaul that links base station and remote radio heads is usually limited,which becomes a bottleneck for realizing the potential performance gain of both downlink and uplink.To solve this problem,we propose a joint antenna selection and user scheduling which is able to achieve a large portion of the potential gain provided by the massive MIMO array with only limited backhaul capacity.Three sub-optimal iterative algorithms with the objective of sumrate maximization are proposed for the joint optimization of antenna selection and user scheduling,either based on greedy fashion or Frobenius-norm criteria.Convergence and complexity analysis are presented for the algorithms.The provided Monte Carlo simulations show that,one of our algorithms achieves a good tradeoff between complexity and performance and thus is especially fit for massive MIMO systems.     

EDA-Based Scheduling of Users in the MIMO Multiple Access Channel

In this paper, we address a user scheduling (selection) problem in the uplink multiuser multiple input multiple output (MIMO) wireless communication system. For this problem, the computational complexity of exhaustive search grows exponentially with the number of users. We present an iterative, low-complexity, sub-optimal algorithm for this problem. We apply an Estimation of Distribution Algorithm (EDA) for the user scheduling problem. An EDA is an evolutionary algorithm and updates its chosen population at each iteration on the basis of the probability distribution learned from the population of superior candidate solutions chosen at the previous iterations. The proposed EDA has a low computational complexity and can find a nearly optimal solution in real time for the user scheduling problem. Beyond applying the general EDA to user scheduling, we also present specific improvements that reduce computation for obtaining an acceptable solution. These improvements include the idea of generating an initial population by cyclically shifting a candidate solution. The simulation results show that our proposed algorithm performs better than other scheduling algorithms with comparable complexity.     

Scheduling Algorithms and Throughput Maximization for the Downlink of Packet-Data Cellular Systems with Multiple Antennas at the Base Station

The capacity-achieving coding scheme for the multiple-input multiple-output (MIMO) broadcast channel is dirty-paper coding. With this type of transmission scheme the optimal number of active users that receive data and the optimal power allocation strategy are highly dependent on the structure of the channel matrix and on the total transmit power available. In the context of packet-data access with adaptive transmission where mobile users are equipped with a single receive antenna and the base station has multiple transmit antennas, we study the optimal number of active users and the optimal power allocation. In the particular case of two transmit antennas, we prove that the optimal number of active users can be a non-monotonic function of the total transmit power. Thus not only the number of users that should optimally be served simultaneously depends on the user channel vectors but also on the power available at the base station transmitter. The expected complexity of optimal scheduling algorithms is thus very high. Yet we then prove that at most as many users as the number of transmit antennas are allocated a large amount of power asymptotically in the high-power region in order to achieve the sum-capacity. Simulations confirm that constraining the number of active users to be no more than the number of transmit antennas incurs only a marginal loss in spectral efficiency. Based on these observations, we propose low-complexity scheduling algorithms with sub-optimal transmission schemes that can approach the sum-capacity of the MIMO broadcast channel by taking advantage of multiuser diversity. The suitability of known antenna selection algorithms is also demonstrated. We consider the cases of complete and partial channel knowledge at the transmitter. We provide simulation results to illustrate our conclusions.     

多用户MISO系统下行链路的自适应用户选择算法

针对多用户多输入单输出(MISO)系统中的用户选择问题,该文基于多用户系统的容量公式提出一种低复杂度的自适应用户选择算法,使选择的用户数随当前的信道状态自适应变化以最大化所有用户的和速率。仿真结果表明,该算法具有接近最优的性能。在此基础上结合比例公平调度对算法进行改进以保证系统中用户服务的公平性。     

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.     

Opportunistic Feedback for Multiuser MIMO Systems With Linear Receivers

A novel multiuser scheduling and feedback strategy for the multiple-input multiple-output (MIMO) downlink is proposed in this paper. It achieves multiuser diversity gain without substantial feedback requirements. The proposed strategy uses per-antenna scheduling at the base station, which maps each transmit antenna at the base station (equivalently, a spatial channel) to a user. Each user has a number of receive antennas that is greater than or equal to the number of transmit antennas at the base station. Zero-forcing receivers are deployed by each user to decode the transmitted data streams. In this system, the base station requires users' channel quality on each spatial channel for scheduling. An opportunistic feedback protocol is proposed to reduce the feedback requirements. The proposed protocol uses a contention channel that consists of a fixed number of feedback minislots to convey channel state information. Feedback control parameters including the channel quality threshold and the random access feedback probability are jointly adjusted to maximize the average throughput performance of this system. Multiple receive antennas at the base station are used on the feedback channel to allow decoding multiple feedback messages sent simultaneously by different users. This further reduces the bandwidth of the feedback channel. Iterative search algorithms are proposed to solve the optimization for selection of these parameters under both scenarios that the cumulative distribution functions of users are known or unknown to the base station     

认知无线电网络基于F范数的频谱共享

针对多用户多输入多输出认知无线电网络的频谱共享问题,提出一种在保证授权用户服务质量要求的前提下,以认知网络容量最大化为目标的基于F范数的频谱共享方法.该方法利用信道矩阵的F范数选择认知用户以获得认知网络的多用户分集增益,并采用两次选择的方式降低算法的复杂度,通过将认知用户的发射信号投射到干扰信道的零空间来避免认知用户对...     

Throughput and Channel Access Statistics of Generalized Selection Multiuser Scheduling

To provide a near-optimal low-complexity solution to parallel multiuser scheduling in code-division multiple-access (CDMA), we propose generalized selection multiuser diversity (GSMuD) schemes with multi-code channel assignment and analyze their performance. The proposed GSMuD (Lc, L) schemes rank a total of L users awaiting transmissions by their signal-tonoise ratios (SNRs) and select the Lc (1 ? Lc ? L) users with the largest absolute (or normalized) SNRs for parallel channel access, which achieve near-optimal sum rate with a low scheduling complexity. The sum and individual channel throughput rates, second order statistics, fairness, and channel access statistics of the proposed GSMuD schemes are derived, taking into account different types of generalized fading channels. Compared to the round robin (RR) scheduling without SNR ranking, the GSMuD with normalized SNR ranking achieves a substantially higher sum rate while maintaining fairness. GSMuD also significantly improves the channel access performance and the degree of fairness than selective multiuser diversity (SMuD), which selects one best user only at each time slot.     

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.     

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.     

Robustness of Weighting Receive Antenna Selection Algorithm

The conventional antenna selection schemes suffer from severe performance degradations in most fading channels. This paper proposes a new receive antenna selection algorithm based on the theory of convex optimization that improve the system performance over Rayleigh fading multiple-input multiple-output (MIMO) channels. With this method, each Radio Frequency chain is not allocated to a single antenna element, but instead to the complex-weighted and combined response of a group array of elements. In this paper, we firstly get optimal solution under no constraints. Then, suboptimal algorithms are introduced based on minimised the squared error and convex optimization technique. The Monte-Carlo simulations show that the algorithm proposed can provide the performance very close to that of the optimal selection based on exhaustive search.     

User scheduling and power allocation for nonfull buffer traffic in NOMA downlink systems

Nonorthogonal multiple access (NOMA) is one of the key technologies for 5G, where the system capacity can be increased by allowing simultaneous transmission of multiple users at the same radio resource. The most of the proportional fairness (PF)–based resource allocation studies for NOMA systems assumes full buffer traffic model, while the traffic in real‐life scenarios is generally nonfull buffer. In this paper, we propose User Demand–Based Proportional Fairness (UDB‐PF) and Proportional User Satisfaction Fairness (PUSF) algorithms for user scheduling and power allocation in NOMA downlink systems when traffic demands of the users are limited and time‐varying. UDB‐PF extends the PF‐based scheduling by allocating optimum power levels towards satisfying the traffic demand constraints of user pair in each resource block. The objective of PUSF is to maximize the network‐wide user satisfaction by allocating sufficient frequency and power resources according to traffic demands of the users. In both cases, user groups are selected first to simultaneously transmit their signals at the same frequency resource, while the optimal transmission power level is assigned to each user to optimize the underlying objective function. In addition, the genetic algorithm (GA) approach is employed for user group selection to reduce the computational complexity. When the user traffic rate requirements change rapidly over time, UDB‐PF yields better sum rate (throughput) while PUSF provides better network‐wide user satisfaction results compared with the PF‐based user scheduling. We also observed that the GA‐based user group selection significantly reduced the computational load while achieving the comparable results of the exhaustive search.     

Simulation Models for Investigation of Multiuser Scheduling in MIMO Broadcast Channels

Spatial correlation is a result of insufficient antenna spacing among multiple antenna elements, while temporal correlation is caused by Doppler spread. This paper compares the effect of spatial and temporal correlation in order to investigate the performance of multiuser scheduling algorithms in multiple‐input multiple‐output (MIMO) broadcast channels. This comparison includes the effect on the ergodic capacity, on fairness among users, and on the sum‐rate capacity of a multiuser scheduling algorithm utilizing statistical channel state information in spatio‐temporally correlated MIMO broadcast channels. Numerical results demonstrate that temporal correlation is more meaningful than spatial correlation in view of the multiuser scheduling algorithm in MIMO broadcast channels. Indeed, the multiuser scheduling algorithm can reduce the effect of the Doppler spread if it exploits the information of temporal correlation appropriately. However, the effect of spatial correlation can be minimized if the antenna spacing is sufficient in rich scattering MIMO channels regardless of the multiuser scheduling algorithm used.     

下行多用户MIMO-OFDMA/SDMA系统动态资源分配

该文对下行多用户MIMO-OFDMA/SDMA系统动态资源分配算法进行了研究,在满足各种约束条件的前提下,以最大化系统吞吐量为目标建立了相应的优化模型。由于最优解难以获得,将整个优化过程分两步完成,第1步定义了一个用于度量配置多根天线的用户空间兼容性的指标,并根据该指标提出了相应的调度算法;第2步提出了两种次优的资源分配算法。仿真结果表明,所提算法优于传统的随机调度算法,与功率复用策略结合时,所提算法的性能接近于基于用户选择的最优分配算法的性能。     

上行多用户MIMO系统中的用户与天线联合选择方案

在多用户MIMO系统中,基站所能同时进行通信的用户数受到基站和用户端天线数的限制,随着用户数的增加,系统的性能反而会降低,因此,用户选择技术就成为一种改善系统性能的有效技术.在本文中,针对上行多用户MIMO系统提出了一种低复杂度的用户选择算法.为获得更大的系统性能,在用户选择的基础上,进一步提出了一种用户与天线联合选择算法.这两种算法在极大地简化计算复杂度的条件下,提供了与最优算法几乎相同的性能.