一种适用于SC-FDMA多小区系统的协作调度和功率控制算法

该文针对长期演进(LTE)上行单载波频分多址(SC-FDMA)多小区系统的性能受限于小区间干扰的问题,提出一种综合考虑协作调度和功率控制的方案。该方法分步执行小区间的协作调度和功率控制,首先调度各小区中的用户,在此基础上优化用户的发射功率。调度时首先估计小区间的干扰信息并分配频率资源块给每个小区内的用户,在优化用户的发射功率时,同时考虑由于用户功率改变所造成的目标小区和其他干扰小区性能的变化。进一步提出一种低复杂度功率控制方案,在优化目标小区用户的功率时,只考虑受目标小区干扰影响最大的几个小区性能的变化,其他干扰小区性能的变化则通过引入补偿因子来估计。计算机仿真验证了该文所提方法在系统吞吐量和小区边缘吞吐量方面的性能优势显著。     

多小区Massive MIMO系统中低复杂度MMSE线性检测算法研究

文章针对多小区Massive MIMO上行链路系统中MMSE线性检测中涉及大矩阵求逆具有高复杂度的问题,提出了一种低复杂度的MMSE检测算法。首先考虑已知目标小区的信道状态信息而其他小区信道状态信息未知条件下,通过求解干扰项与噪声之和的均值与方差,将多小区信道模型转化为单小区信道模型,再利用MMSE算法进行检测。为了降低求逆矩阵的复杂度,将大矩阵分解为对角矩阵和空心矩阵之和,再利用诺依洛曼级数近似将其展开,并优化展开项因子来增加算法收敛速度。仿真结果表明,所提出的改进算法在性能损失很少的情况下复杂度从O(K~3)降低到O(K~2),其中K为本小区中的用户数。     

基于非合作博弈论的多小区OFDMA系统动态资源分配算法研究

该文采用非合作博弈论的方法研究了多小区OFDMA系统中的动态资源分配问题,首先将各基站的发射功率平均分配给各子载波,然后由所有小区在每个子载波上独立地进行资源分配博弈,给出了用户调度与功率分配联合博弈框架。为了进一步简化,将用户调度和资源分配分开完成,通过将信道增益引入到定价函数中,提出了一种新的定价机制,建立了用户确定时的非合作功率分配博弈模型,分析了其纳什均衡的存在性和唯一性,并设计了具体的博弈算法。仿真结果表明,所提算法在保证吞吐量性能的同时,进一步提升了系统的公平性。     

用于智能天线系统的多小区空时联合处理算法

TD-SCDMA系统同频组网时,邻区用户同样会产生多址接入干扰,而传统的联合检测无法消除.为了抑制邻区干扰,对上行链路接收端提出了一种将联合检测扩展到多个小区并与智能天线相结合的多小区空时联合处理算法.该算法根据对邻区用户信道估计的结果选择其中的强干扰用户,并将邻区强干扰用户和本小区用户的复合信道冲激响应同时纳入空时联合处理的系统矩阵.仿真结果表明,在室内-室外和高速车载信道模型下,采用多小区空时联合处理算法的接收机解调性能有3～4.8 dB的提升.此外,讨论了邻区相关信息的获取、邻区强干扰用户的选择和运算复杂度等具体实现时需考虑的问题.多小区空时联合处理算法可有效消除邻区强干扰用户的干扰,从而大幅提高了同频组网时系统的性能.     

多点传输系统的联合调度方案

在下行蜂窝网络中,多点传输计划因可以大幅度提高扇区平均吞吐量和小区边缘用户速率而越来越受到关注。传统的独立调度算法中参与多点传输的扇区分别调度各自的用户,这种独立调度算法限制了系统性能的进一步提高。文章提出了一种针对SF-DCHSDPA系统新的联合调度方案,基站间通过交换信息选择适合的传输方案。可更好地调度用户。通过系统级仿真结果可以看出,新提出的联合调度算法可以获得更好的扇区平均吞吐量和边缘用户体验。     

一种多小区OFDMA系统中有效公平的资源分配方法

在多小区OFDMA系统中,一个主要的研究方向就是如何通过控制来自邻小区的同道干扰来优化系统性能。采用了联盟形成以及合作博弈中纳什讨价还价解(NBS)的方法,使小区内用户随机地结合成合作型双人联盟并对子载波的使用进权行讨价还价,从而换取有利于自己的子载波。所提出的算法不仅复杂度低而且能产生一个公平的解,另外还证明了这种机制下的帕累托最优性。仿真结果充分表明了所提算法的有效性与公平性。     

一种抑制小区间干扰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,提出了基于多小区联合预编码和静态功率控制的比例公平(MCPPC-PF)调度算法。通过干扰空间迫零和静态控制发射功率的方法抑制小区间干扰,并结合比例公平(PF)调度算法,提高用户的公平性。仿真结果表明,与传统算法相比,MCPPC-PF算法提升系统容量的同时还提高了用户的公平性;与基于多小区联合预编码和静态功率控制的最大化吞吐量调度算法相比,MCPPC-PF算法在系统容量损失了4.6%的情况下,边缘用户容量提高了约45%。     

基于多小区容量最大化的分布式功率分配和调度算法

无线数据网络中传输功率和调度算法的联合最优化可以提供可观的系统容量增益,然而,这个问题被认为是在现实中很难解决的。本文中,我们分析一个多小区全复用网络的下行链路,以使它的整个网络容量达到最大化。我们提出了一种分布式功率分配和调度算法,可以为任何有限数量的用户提供很大的容量增益。这种分布式小区协调算法,实际上是动态频谱复用的一种形式,复用的数量随着信道条件的变化而变化。     

无线网络多小区接入方案的容量最大化算法

利用所谓的多小区接入（multi—cell access）,我们提出了一种新的方案来提高无线网络的容量。多小区接入实际上是传统的多接入问题在多用户网络中的重现。然而,在多小区接入方案多个小区需要竞争多个接入,而且,这种方案的一个应用实例已经提出来了,在这个实例中,小区取得了一种信任度,这种信任度是小区所调度用户的信道增益的一个功能,基于信任度量,小区用户可以允许按照一定比例进行传输。我们将分析这种方案下的网络容量,并提出一种最优算法,这种算法就是接入概率分布为二进制分布。     

窃听信道下认知多小区中的协同波束赋形算法

小区之间优化变量耦合使多小区联合优化问题为非线性非凸问题,通过约束泄露干扰提出了基于半正定松弛的中心式协同波束成形算法,在此基础上,采用部分对偶分解方法将中心式联合优化问题分解为一组单小区优化问题,提出了一种分布式迭代求解算法。结果表明,所提算法在保证系统安全速率需求和认知干扰约束的条件下实现了系统功耗的降低,此外,分布式算法不仅能降低实现复杂度,而且能达到中心式算法的性能。     

中继增强的无线蜂窝多小区系统的联合调度与功率控制算法

针对采用全局频率复用的中继增强的无线蜂窝多小区系统,该文考虑多种通信模式并存的混合场景,提出了一种干扰感知的联合资源分配策略。以最大化系统总吞吐量为目标,同时考虑小区间干扰对中继节点与移动站点的影响,以及基站与中继节点各自的发射功率约束。为了降低计算复杂度,针对用户与中继节点配对问题提出了一种基于小区间干扰的调度算法;针对功率控制问题分别提出了一种基于符号规划的最优功率分配算法和一种次优的最小能耗功率分配算法。仿真结果表明,该文所提算法逼近最优资源分配,在系统吞吐量与能量效率等性能方面具有显著优势。     

毫米波通信低复杂度波束选择和用户调度算法

针对毫米波混合波束成形系统中用户调度方案复杂度过高的问题,提出两种低复杂度的波束选择和用户调度联合优化算法。混合波束成形架构使得用户调度问题面临着新的挑战,变成了模拟波束选择和用户调度的联合优化问题。考虑发送端无法获得完美信道状态信息的实用场景,采用基于固定码本的波束训练方案获取等效信道状态信息,引入调用指示函数将联合优化问题建模成非凸组合优化规划,分别以粒子群优化和贪婪算法为核心,提出两种低复杂度的次优解决方法。仿真结果表明,相较穷举搜索,所提算法能在性能和复杂度之间取得很好的折中。      

On multicell cooperative transmission in backhaul-constrained cellular systems

Recent work has shown that multicell cooperative signal processing in cellular networks can significantly increase system capacity and fairness. For example, multicell joint transmission and joint detection can be performed to combat intercell interference, often mentioned in the context of distributed antenna systems. Most publications in this field assume that an infinite amount of information can be exchanged between the cooperating base stations, neglecting the main downside of such systems, namely, the need for an additional network backhaul. In recent publications, we have thus proposed an optimization framework and algorithm that applies multicell signal processing to only a carefully selected subset of users for cellular systems with a strongly constrained backhaul. In this paper, we consider the cellular downlink and provide a comprehensive summary and extension of our previous and current work. We compare the performance obtained through centralized or decentralized optimization approaches, or through optimal or suboptimal calculation of precoding matrices, and identify reasonable performance–complexity trade-offs. It is shown that even low-complexity optimization approaches for cellular systems with a strongly constrained backhaul can yield major performance improvements over conventional systems.     

Distributed scheduling based on polling policy with maximal spatial reuse in multi-hop WMNs

The scheduling algorithm based on the three-way handshaking scheme in IEEE 802.16d-2004 standard has some serious problems because of the complexity of the algorithm and low scheduling efficiency.To enhance the scheduling efficiency and improve the performance of multi-hop wireless mesh networks (WMNs), one distributed scheduling algorithm that can maximize the spatial and time reuse with an interference-based network model is proposed.Compared to the graph-based network model, the proposed network model can achieve a better throughput performance with maximal spatial reuse.Furthermore, this proposed scheduling algorithm also keeps fairly scheduling to all links, with a priority-based polling policy.Both the theoretical analysis and simulation results show that this proposed distributed scheduling algorithm is simple and efficient.     

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.     

Cross-Layer Radio Resource Allocation for Multicarrier Air Interfaces in Multicell Multiuser Environments

Packet scheduling over shared channels is one of the most attractive issues for researchers dealing with radio resource allocation in wireless networks as modern systems' different traffic types, with different application requirements, need to coexist over the air interface. Recently, attention has been attracted to multicarrier techniques and the application of cross-layer approaches to the design of wireless systems. In this paper, a radio access network using a multicarrier air interface is considered in a multicell multiuser context. We propose a new cross-layer scheduling algorithm that manages channel, physical layer, and application-related information; we compare its performance with a previously published cross-layer strategy and with simpler well-known channel-aware or channel-unaware techniques and then discuss its optimization. We investigate the performance in terms of perceived user quality and fairness in the presence of mixed realistic traffic composed of H.264 video streaming with tight bounds on the delay jitter and file transfer protocol (FTP) data. To support video traffic, application-suited buffer-management techniques are also considered in conjunction with scheduling, and link adaptation is implemented at the physical layer to better exploit channel fluctuations. The role of scheduling and resource-allocation functionalities are discussed. It is shown that the cross-layer strategy proposed guarantees the same performance obtained by the previously published algorithm while reducing complexity. Moreover, under heavily loaded conditions, the cross-layer scheduling strategy provides a significant gain with respect to simple channel-aware or channel-unaware techniques.     

Robust beamforming for multicell downlink TDD massive MIMO system based on QoS

In view of multicell downlink time division multiplexing (TDD) massive multiple-input multiple-output (MIMO) systems which had imperfect channel state information (CSI),the beamforming problem that minimized the total transmit power and signal leakage power based on quality of service (QoS) was studied.First,the objective problem was approximated as a standard convex optimization problem.Then,by using the duality of uplink and downlink,an inner and outer layer iterative algorithm was proposed.Numerical results show that,comparing with other typical downlink multicell massive MIMO beamforming algorithms,the proposed algorithm has obvious advantages in terms of complexity and energy efficiency.     

Cross‐Layer Resource Allocation in Multi‐interface Multi‐channel Wireless Multi‐hop Networks

In this paper, an analytical framework is proposed for the optimization of network performance through joint congestion control, channel allocation, rate allocation, power control, scheduling, and routing with the consideration of fairness in multi‐channel wireless multi‐hop networks. More specifically, the framework models the network by a generalized network utility maximization (NUM) problem under an elastic link data rate and power constraints. Using the dual decomposition technique, the NUM problem is decomposed into four subproblems — flow control; next‐hop routing; rate allocation and scheduling; power control; and channel allocation — and finally solved by a low‐complexity distributed method. Simulation results show that the proposed distributed algorithm significantly improves the network throughput and energy efficiency compared with previous algorithms.     

Adaptation, Coordination, and Distributed Resource Allocation in Interference-Limited Wireless Networks

A sensible design of wireless networks involves striking a good balance between an aggressive reuse of the spectral resource throughout the network and managing the resulting co-channel interference. Traditionally, this problem has been tackled using a ldquodivide and conquerrdquo approach. The latter consists in deploying the network with a static or semidynamic pattern of resource reutilization. The chosen reuse factor, while sacrificing a substantial amount of efficiency, brings the interference to a tolerable level. The resource can then be managed in each cell so as to optimize the per cell capacity using an advanced air interface design. In this paper, we focus our attention on the overall network capacity as a measure of system performance. We consider the problem of resource allocation and adaptive transmission in multicell scenarios. As a key instance, the problem of joint scheduling and power control simultaneously in multiple transmit-receive links, which employ capacity-achieving adaptive codes, is studied. In principle, the solution of such an optimization hinges on tough issues such as the computational complexity and the requirement for heavy receiver-to-transmitter feedback and, for cellular networks, cell-to-cell channel state information (CSI) signaling. We give asymptotic properties pertaining to rate-maximizing power control and scheduling in multicell networks. We then present some promising leads for substantial complexity and signaling reduction via the use of newly developed distributed and game theoretic techniques.     

Adaptive pilot decontamination in multi-cell massive MIMO networks

This paper considers the contaminated channel estimation in multicell very large multiuser multiple-input multiple-output networks. To provide desirable spectral efficiency of these systems, we propose two adaptive algorithms which their implementations do not require orthogonal pilot sequences. The first algorithm (i.e., normalized least mean square) has low complexity and also gives an acceptable spectral efficiency in channels with low attenuation. The second algorithm (i.e., recursive least square) is more robust against pilot contamination and provides the desirable spectral efficiency in very large number of antennas at the base stations. In simulations, the performance of the proposed algorithms is evaluated and also compared with the performance of the conventional minimum mean square error estimation.