能量有效的认知多小区协同波束赋形算法

针对认知多小区多用户下行传输链路,提出了一种基于能量效率最大化准则的协同波束赋形优化方法。该方法采用迫零消除小区内用户间干扰,在保证用户最小速率需求及认知干扰约束的同时,实现了能量效率和频谱效率的同步改善。为了分布式求解优化问题,通过约束泄露干扰并利用半定松弛,将其转换为凸问题,在此基础上,采用部分对偶分解方法将多小区联合优化问题分解为一组单小区优化问题,从而实现了分布式求解。仿真结果表明,该方法不仅实现了能量效率和频谱效率的有效折中,而且达到了集中式算法的性能。     

一种基于多小区OFDMA系统用户公平性约束的分布式资源分配算法

针对多小区OFDMA系统下行链路,研究了用户公平性约束下的资源分配问题,提出了一种多基站协作的迭代优化的分布式资源分配算法。每个小区根据干扰状况及用户公平性,迭代地进行子载波和功率的资源优化;而每次迭代中,根据用户公平性准则分配子载波,并将非凸的小区功率优化问题转化为其下界的凸问题,通过一个分布式算法来求解。通过仿真验证了算法的有效性;仿真结果表明,与传统网络的固定功率分配的情形相比,所提算法保证了用户之间的公平性并显著提高了系统吞吐量。     

多载波超密集网络的抗干扰分布式节能功率分配

该文研究多载波超密集网络(UDN)上行链路能效最优功率分配方案,基于非合作博弈论提出一种抗干扰分布式功率分配方案,使每个小区独立优化能效的同时抑制邻小区干扰。由于最大传输功率和QoS约束下的能效函数具有不易解决的非凸特性,且小小区间存在严重干扰。针对以上挑战,该文在最佳响应过程中设计了一种高精度低复杂度的阶梯注水算法,基于该算法利用干扰信道增益提出了一种多用户抗干扰功率分配算法。仿真结果和数值分析表明该算法运算复杂度低,且能在保证系统频谱效率的同时大幅度提升系统能效。     

大规模MIMO系统中基于溢出概率的鲁棒协作波束设计

大规模多输入多输出(Massive MIMO)技术通过在基站端配置大规模天线能有效提升5G蜂窝系统容量。考虑信道估计误差对系统性能的影响,该文在多小区大规模MIMO系统中形成了用户信干噪比的非溢出概率约束下最小化系统功率的优化问题。针对非凸概率约束中下行波束难于求解的问题,该文根据矩阵迹的性质将优化问题中的非凸约束缩放,进而提出上下行对偶算法求解波束矢量。为进一步减少多小区系统中信令开销,基于大系统分析,提出了仅采用大尺度信息的分布式算法。仿真结果表明,所提的分布式算法与对偶算法相比,在保证用户信干噪比的概率约束时,降低了大规模MIMO系统中传输瞬时信道状态信息的开销,同时具有良好的鲁棒性。     

认知无线电系统的顽健资源分配算法

摘 要：针对多用户下垫式认知无线电网络中参数不确定性问题,提出了一种顽健分布式功率控制算法。在干扰温度门限和次用户信干噪比（SINR）的约束下,考虑信道不确定性,实现认知系统功率消耗最小化。基于欧几里得球形不确定性描述,利用拉格朗日对偶分解理论给出了顽健功率控制问题的解。仿真结果表明,该顽健功率分配算法能同时满足主用户和次用户的QoS需求,与非顽健算法和传统SOCP算法对比可提升系统性能。     

多载波超密集网络的抗干扰分布式节能功率分配

该文研究多载波超密集网络(UDN)上行链路能效最优功率分配方案,基于非合作博弈论提出一种抗干扰分布式功率分配方案,使每个小区独立优化能效的同时抑制邻小区干扰.由于最大传输功率和QoS约束下的能效函数具有不易解决的非凸特性,且小小区间存在严重干扰.针对以上挑战,该文在最佳响应过程中设计了一种高精度低复杂度的阶梯注水算法,基于该算法利用干扰信道增益提出了一种多用户抗干扰功率分配算法.仿真结果和数值分析表明该算法运算复杂度低,且能在保证系统频谱效率的同时大幅度提升系统能效.     

基于分布式天线的全双工中继系统最大化和速率波束成形设计

基于分布式天线的全双工中继系统结合了全双工中继两跳同时同频传输的能力和分布式天线高效覆盖的特性,为提升小区边缘和严重阴影衰落区域的频谱效率提供了一种有效途径。在自干扰抵消非理想的多用户场景下,利用分布式多天线波束成形可实现对系统中自干扰和多用户干扰的联合抑制。为此,该文首先建立了在各分布式天线节点独立发射功率约束下最大化多用户端到端和速率的最优化系统模型,进而提出一种双层迭代算法,解决原问题的非凸性求解难题。仿真结果验证了算法的有效性,表明在多用户分布式天线全双工中继系统中,所提波束成形设计能够有效抑制自干扰和多用户干扰,显著提高系统频谱效率。     

基于 GSVD 的分布式 MIMO 雷达测向算法

针对分布式多输入多输出（multi-input multi-output, MIMO）雷达测向中存在的数据信息提取不充分、运算量偏大等问题，开展了基于广义奇异值分解（generalized singular value decomposition, GSVD）的测向算法研究，以提高低信噪比条件下的角度估计性能。首先，建立了分布式阵列MIMO雷达回波信号的统一化表征模型；其次，将分布式MIMO雷达系统接收阵列数据的多线程GSVD问题转换为一个联合优化问题，运用交替最小二乘（alternating least squares, ALS）技术实现阵列信号流行矩阵的拟合，并引入子空间类算法实现目标角度联合估计；最后，对优化问题增加l1范数约束，避免了每次迭代中进行的奇异值分解运算，降低了算法运算量。仿真实验从角度联合估计、均方误差、运算时间等方面验证了所提算法的有效性。     

LTE-Advanced异构网中载波选择与波束成形联合算法

为解决LTE-Advanced异构网内宏小区及微小区间的相互干扰问题,提出了载波选择与波束成形联合算法.在高密度、多低功率微基站节点的异构网中,通过关闭部分基站的部分载波,并在开启载波的基站天线上进行波束成形,使接收端受到的干扰大幅度降低.另外,基于效用函数的约束,对载波、波束分别计算,使原本二维联合优化问题变成两个一维优化问题,降低了算法复杂度.仿真结果表明该方案增加了异构网中边缘用户及整个系统的吞吐量.     

基于时域eICIC的小区覆盖扩展策略能效优化

为了应对未来5G网络流量爆炸式增长,研究学者提出在宏基站覆盖范围内密集部署小基站,并利用小区覆盖扩展(Cell Range Expansion,CRE)策略实现宏基站与小基站的负载均衡。但是,CRE区域用户会严重受到宏基站的同频干扰,为解决此问题引入几乎空白子帧(Almost Blank Subframe,ABS)策略,提出了一种联合偏置值及ABS比例的迭代优化算法。针对具有偏置值与ABS比例约束的问题,分别对偏置值及ABS比例进行搜索寻找最优解,再通过迭代联合算法进行更新迭代,以实现系统能效的最大化。对提出的迭代联合优化算法进行了仿真分析,进一步验证了所提方案具有较好的能效。     

A novel Hα control strategy for design of a robust dynamic routing algorithm in traffic networks

In this paper novel centralized and decentralized routing control strategies based on minimization of the worst-case queuing length are proposed. The centralized routing problem is formulated as an H_infin optimal control problem to achieve a robust routing performance in presence of multiple and unknown fast time-varying network delays. Unlike similar previous work in the literature the delays in the queuing model are assumed to be unknown and time-varying. A Linear Matrix Inequality (LMI) constraint is obtained to design a delay-dependent H_infin controller. The physical constraints that are present in the network are then expressed as LMI feasibility conditions. Our proposed centralized routing scheme is then reformulated in a decentralized frame work. This modification yields an algorithm that obtains the "fastest route", increases the robustness against multiple unknown time-varying delays, and enhances the scalability of the algorithm to large scale traffic networks. Simulation results are presented to illustrate and demonstrate the effectiveness and capabilities of our proposed novel dynamic routing strategies.     

无线传感器网络下线性支持向量机分布式协同训练方法研究

针对无线传感器网络中分散在各节点上的训练数据传输到数据融合中心集中训练支持向量机(Support Vector Machine, SVM)时存在的高通信代价和高能量消耗问题,该文研究了仅依靠相邻节点间的相互协作,在网内分布式协同训练线性SVM的方法。首先,在各节点分类器决策变量与集中式分类器决策变量相一致的约束下,对集中式SVM训练问题进行等价分解,然后利用增广拉格朗日乘子法,对分解后的SVM问题进行求解和推导,进而提出基于全局平均一致性的线性SVM分布式训练算法(Average Consensus based Distributed Supported Vector Machine, AC-DSVM);为了降低AC-DSVM算法中全局平均一致性的通信开销,利用相邻节点间的局部平均一致性近似全局平均一致性,提出基于一次全局平均一致性的线性SVM分布式训练算法(Once Average Consensus based Distributed Supported Vector Machine, 1-AC-DSVM)。仿真实验结果表明,与已有算法相比,AC-DSVM算法的迭代次数和数据传输量略高,但其能够完全收敛到集中式训练结果;1-AC-DSVM算法具有较好的收敛性,而且在收敛速度和数据传输量上也表现出显著优势。     

视觉传感器网络中基于RANSAC的顽健定位算法

视觉传感器网络由于节点故障或环境变化将导致节点对目标的观测数据出现错误,而基于最小二乘的多视觉信息融合定位方法将因此造成较大的定位误差。针对此问题提出一种基于集中式RANSAC的顽健定位算法,将错误数据进行筛选剔除,从而提高定位精度,进一步针对集中式 RANSAC 将会导致单个节点的计算复杂度过高而导致网络节点能耗不平衡问题,提出基于分布式 RANSAC 的顽健定位算法,从而将大量的迭代计算平均分布在各个节点中并行处理,在保证定位过程顽健性的同时保证了网络的计算能耗平衡性。最后通过实验对no-RANSAC、cen-RANSAC 和 dis-RANSAC算法的定位性能进行了比较,验证了该算法能够依照预定的概率获得良好的定位结果,并对算法的时间复杂度进行了分析。     

多小区广播干扰信道的加权均方误差最小化收发机设计算法

未来的第五代无线网络中,由于干扰问题的存在,阻碍了网络性能的提升,尤其是对于小区边缘的用户,其通信质量的需求不能得到满足。本文研究了基于加权均方误差优化的多小区多用户广播干扰信道的收发机算法设计。针对所需求解的优化问题,首先利用分层预编码的方法提出了一种广义干扰对齐的方案,然后提出了一种最小化加权均方误差的交替优化算法。所提算法的收敛性可以由单调有界理论保证。仿真结果表明:与传统的方案相比,所提方案可以有效的消除干扰,并且有效的提升系统的和速率性能。      

Energy efficiency optimization algorithm for heterogeneous NOMA network based on imperfect CSI

In order to improve the suppression capability of parametric perturbation and energy efficiency (EE) of heterogeneous networks (HetNets),a robust resource allocation algorithm was proposed to maximize system EE for reducing cross-tier interference power in non-orthogonal multiple access (NOMA) based HetNets.Firstly,the resource optimization problem was formulated as a mixed integer and nonlinear programming one under the constraints of the interference power of macrocell users,maximum transmit power of small cell base station (BS),resource block assignment and the quality of service (QoS) requirement of each small cell user.Then,based on ellipsoid bounded channel uncertainty models,the original problem was converted into the equivalent convex optimization problem by using the convex relaxation method,Dinkelbach method and the successive convex approximation (SCA) method.The analytical solutions were obtained by using the Lagrangian dual approach.Simulation results verifiy that the proposed algorithm had better EE and robustness by comparing it with the existing algorithm under perfect channel state information.     

Joint subcarrier and power allocation for downlink cellular OFDMA systems by intercell interference limitation

This paper addresses resource allocation for sum throughput maximization in a sectorized two-cell downlink orthogonal frequency-division multiple-access (OFDMA) system impaired by multicell interference. It is well known that the optimization problem for this scenario is NP-hard and combinational, which is here converted to a novel sum throughput maximization problem in cellular OFDMA systems based on the intercell interference limitation. Then, three subclasses of this new problem are solved. By the first subclass, on the assumption that subcarrier allocation parameters are fixed, an algorithm for optimal power allocation is obtained. However, the optimal resource allocation requires an exhaustive search, including the optimal power allocation which cannot be implemented in practice due to its high complexity. By the second subclass, the problem is reduced to a single cell case where the intercell interference in each subcarrier is limited to a certain threshold. Based on the solution of the single cell problem, a distributed resource allocation scheme with the aim of small information exchange between the coordinated base stations is proposed. By the third subclass, the centralized resource allocation for two adjacent cells as a general problem is solved. Here, the algorithm allocates simultaneously the subcarriers and the power of the considered two cells while the resource allocation parameters of both cells are coupled mutually. The present study shows that distributed and centralized resource allocation algorithms have much less complexity than the algorithm used in the exhaustive search and can be used in practice as efficient multicell resource allocation algorithms. Simulation results illustrate the performance improvements of the proposed schemes in comparison to the schemes with no intercell interference consideration.     

Gossip-Based Load Balance Strategy in Big Data Systems with Hierarchical Processors

In big data systems, data are assigned to different processors by the system manager, which has a large amount of work to perform, such as achieving load balances and allocating data to the system processors in a centralized way. To alleviate its load, we claim that load balancing can be conducted in a decentralized way, and thus, the system manager need not be in charge of this task anymore. Two decentralized approaches are proposed for load balancing schemes, namely, a utilization scheme based on a load balance algorithm (UBLB) and a number of layers scheme based on a load balance algorithm (NLBLB). In the UBLB scheme, considering the hierarchy of the processor’s processing abilities, a gossip-based algorithm is proposed to achieve load balance using the jobs’ utilizations as load balance indicators in addition to the number of jobs. The reason for this action is that the processor’s process abilities are different from one another. Thus, the utilization indicator is more reasonable. In the NLBLB scheme, the processors are classified into different layers according to their processing abilities. In each layer, a sub-load balance is conducted, which means that the UBLB is achieved in a sub-region. The efficiencies of the two proposed schemes are validated by simulation, which proves their positive effect.     

A distributed implementation of mobility load balancing with power control and cell reselection in 4G network

We consider a self‐organizing network (SON) capability of mobility load balancing in a 4G network, which determines the transmission power level for individual base stations and cell reselection for individual mobile stations such that the network‐wide load is minimized while satisfying the minimum signal‐to‐noise and interference ratio (SINR) requirement of individual users. Both centralized and distributed schemes are proposed. The centralized scheme is based on the greedy algorithm, serving as a performance bound to the distributed scheme. The distributed scheme is to solve the system‐wide optimization problem in the flat network model, i.e. no central control node. Furthermore, it requires relatively low inter‐cell exchange information among neighboring cells over an inter‐cell channel, e.g. X2 interface in the LTE network. The proposed design objective is to minimize the number of mobile users that do not satisfy the specified average throughput, while distributing the user traffic load as uniformly as possible among the neighboring cells. Our simulation results for a uniform user distribution demonstrate that the proposed scheme can achieve up to almost 80% of a load balancing gain that has been achieved by a greedy algorithm in the centralized optimization. Copyright © 2014 John Wiley & Sons, Ltd.     

Robust power and subcarrier allocation algorithm for cognitive network based on interference efficiency maximization

For the cognitive OFDMA uplink communication system,a robust power and subcarrier allocation algorithm based on maximum interference efficiency was proposed.Firstly,considering primary user interference constraint,secondary user transmit power constraint,subcarrier allocation constraint and secondary user minimum rate constraint,a robust resource optimization model based on outage probability was established.Then,by using Bernstein approximation and Dinkelbach’s method,the original non-convex problem based on outage probability was transformed into an equivalent convex optimization one,and the analytical solution was obtained by Lagrangian dual function method.Meanwhile,the computational complexity and robust sensitivity of the algorithm were analyzed.The simulation results show that the proposed algorithm has better interference efficiency and robustness.