A novel proactive soft load balancing framework for ultra dense network

A major challenge for the future wireless network is to design the self-organizing architecture.The reactive self-organizing model of traditional networks needs to be transformed into an active self-organizing network.Due to the user mobility and the coverage of small cells,the network load often becomes unbalanced,resulting in poor network performance.Mobility management has become an important issue to ensure seamless communication when users move between cells,and proactive mobility management is one of the important functions of the active Self-Organizing Network(SON).This paper proposes a proactive mobility management framework for active SON,which transforms the original reactive load balancing into a forward-aware and proactive load balancing.The proposed framework firstly uses the BART model to predict the users’temporal and spatial mobility based on a weekly cycle and then formulate the MLB optimization problem based on the soft load.Two solutions are proposed to solve the above MLB problem.The simulation results show that the proposed method can better optimize the network performance and realize intelligent mobile management for the future network.

Joint load balancing and interference coordination in multi-antenna heterogeneous networks

In heterogeneous networks（Het Nets）, it is desirable to offload users from macro cells to small cells to achieve load balancing. However, the offloaded users suffer a strong inter-tier interference. To guarantee the performance of the offloaded users, the interference from macro cells should be carefully managed. In this paper, we jointly optimize load balancing and interference coordination in multi-antenna Het Nets. Different from previous works, instead of almost blank subframes（ABS） on which the macro cells waste time resource, the macro cells suppress the interference to the offloaded users by zero-forcing beamforming（ZFBF） on interference nulling subframes（INS）. Considering user association cannot be conduct frequently, we derive the long-term throughput of users over Rayleigh fading channels while previous works focused on instantaneous rate. From the perspective of the spectrum efficiency and user fairness, we formulate a long-term network-wide utility maximization problem. By decomposing the problem into two subproblems, we propose an efficient joint load balancing and interference coordination strategy. Simulation results show that our proposal can achieve good system performance gains over counterparts in term of the network utility, cell edge throughput and average throughput.

Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

Heterogeneous Networks(HetNets)and cell densification represent promising solutions for the surging data traffic demand in wireless networks.In dense HetNets,user traffic is steered toward the Low-Power Node(LPN)when possible to enhance the user throughput and system capacity by increasing the area spectral efficiency.However,because of the transmit power differences in different tiers of HetNets and irregular service demand,a load imbalance typically exists among different serving nodes.To offload more traffic to LPNs and coordinate the Inter-Cell Interference(ICI),Third-Generation Partnership Project(3GPP)has facilitated the development of the Cell Range Expansion(CRE),enhanced Inter-Cell Interference Coordination(eICIC)and Further enhanced ICIC(FeICIC).In this paper,we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe(LPS)approach.Our solution allows the separation of User Association(UA)functions at the User Equipment(UE)and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength(max-RSS)based UA scheme,where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the system.The proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service regions.Results show that our method effectively solves the offloading and interference coordination problems in dense HetNets.

LTE-Advanced异构网络中负载均衡研究进展及展望

新一代移动通信网络不断演进为高密集、无定型的异构网络,业务需求与资源供给的匹配问题,即负载均衡问题是异构网络(heterogeneous network,HetNet)资源管理的挑战问题之一。阐述了负载均衡的一般性问题,根据负载均衡算法的主要特征给出了综合分类,按照分类对近年来提出的主要负载均衡策略进行了细致和深入的对比分析;同时也对比分析了几种减少负载均衡过程中信令开销的方案。在总结现有研究成果的基础上,进一步分析负载均衡技术的最新发展趋势,为下一步的研究提出了新的问题和思路。

Load balancing strategy of heterogeneous wireless networks based on multi-hop routing algorithm of ad hoc network

Because of different system capacities of base station （BS） or access point （AP） and ununiformity of traffic distribution in different cells, quantities of new call users may be blocked in overloaded cell in communication hot spots. Whereas in some neighboring under-loaded cells, bandwidth may be superfluous because there are only few users to request services. In order to raise resource utilization of the whole heterogeneous networks, several novel load balancing strategies are proposed, which combine the call ad- mission control policy and multi-hop routing protocol of ad-hoc network for load balancing. These loadbalancing strategies firstly make a decision whether to admit a new call or not by considering some parameters like load index and route cost, etc., and then transfer the denied users into neighboring under-loaded cell with surplus channel according to optimum multi-hop routing algorithm. Simulation results show that the proposed load balancing strategies can distribute traffics to the whole heterogeneous wireless netorks, improve the load balance index efficiently, and avoid the call block phenomenon almost absolutely.

Using Extension Theory and Fuzzy Analytic Hierarchy Process on Heterogeneous Wireless Network Selection Algorithm

In order to solve the problem that determining decision factors weights is of subjectivity in heterogeneous wireless network selection algorithm, a network selection algorithm based on extension theory and fuzzy analytic hierarchy process （FAHP） is proposed in this paper. In addition, user and operator codetermine the optimal network using the proposed algorithm, which can give consideration to user and operator benefits. The fuzzy judgment matrix is coustructed by membership degree of decision factors which is calculated according to extension theory. The comprehensive weight of each decision factor is obtained using FAHP. Finally, the optimal network is selected through total property value ranldng of each candidate network under user preference and operator preference. The simulation results show that the proposed algorithm can select the optimal network efficiently and accurately, satisfy user preference, and implement load balance between networks.

Balancing D2D Communication Relayed Traffic Offloading in Multi-Tier HetNets

In Heterogeneous Networks (HetNets), Integrating Device-to-Device communication (D2D) techniques presents as a promising solution for improving system performance by offloading traffic from heavily loaded macro cell (MC) to small cells (SCs). For instance, D2D can be used to offload traffic from heavily-loaded cells to light-loaded small cells. However, offloading new users may result in an unfair load distribution among small cells and consequently may affect the quality of service of some users. To achieve better performance and reduce blocking probability load balancing among small cells should be considered when we offload traffic from macro to small cells. In this paper, we consider a centralized offloaded relay selection scheme, in which a cellular provider can decide whether users can assist each other to relay their traffic to small cells. We propose a joint user-relay selection with dynamic load balancing scheme based on D2D communications using the Kuhn-Munkres (K-M) method. The offloading process considers the load from MC to SCs and among SCs. Compared to previous works, our simulation results show that the proposed scheme increases the number of admitted users in the system, and achieves a higher load balancing fairness index among small cells. Also, our scheme achieves a higher rate fairness index among users by adjusting the signal to interference plus noise ratio (SINR) threshold.

战术网络中基于资源负载均衡和服务优先级的业务调度策略

针对战术网络环境的多样化业务和网络异构性、高动态特性导致的网络负载易失衡的问题,提出了一种基于资源负载均衡和服务优先级的业务调度策略。通过所承载业务类型的优先级感知,基于通信网络当时可用资源等约束,将业务按需调度到战术通信网络的通信资源池,达到通信网络的自动负载均衡和资源可靠合理利用,提高业务服务质量,并保证高优先级业务可靠传输。通过仿真,验证了该策略算法可大大提高网络吞吐量和资源利用效率。

Load-balance cell association for improving network capacity in heterogeneous cellular networks

Heterogeneous cellular networks improve the spectrum efficiency and coverage of wireless communication networks by deploying low power base station （BS） overlapping the conventional macro cell. But due to the disparity between the transmit powers of the macro BS and the low power BS, cell association strategy developed for the conventional homogeneous networks may lead to a highly unbalanced traffic loading with most of the traffic concentrated on the macro BS. In this paper, we propose a load-balance cell association scheme for heterogeneous cellular network aiming to maximize the network capacity. By relaxing the association constraints, we can get the upper bound of optimal solution and convert the primal problem into a convex optimization problem. Furthermore we propose a Lagrange multipliers based distributed algorithm by using Lagrange dual theory to solve the convex optimization, which converges to an optimal solution with a theoretical performance guarantee. With the proposed algorithm, mobile terminals （MTs） need to jointly consider their traffic type, received signal-to-interference-noise-ratios （SINRs） from BSs, and the load of BSs when they choose server BS. Simulation results show that the load balance between macro and pico BS is achieved and network capacity is improved significantly by our proposed cell association algorithm.

基于多跳分簇算法的异构融合通信网络节点负载均衡方法

针对网络节点负载不均衡、节点能量消耗较大、网络吞吐量较低的问题,提出基于多跳分簇算法的异构融合通信网络节点负载均衡方法。依据异构融合通信网络实时(RT)和非实时(NRT)业务的服务质量满意度,应用效用函数结合三角模融合算子,将重载网络通信业务转移至轻载网络,实现异构融合通信网络业务负载均衡;运用多跳分簇路由算法,依据节点位置和其所剩能量选取簇头,通过减少网络边缘的簇头数量,节省网络能量消耗,通过簇头节点剩余能量和链路权值大小选取下一路由,构建簇间路由通信机制,实现异构融合通信网络节点负载均衡。在实验验证方面,在5.5×15^(3)个周期时,存货节点为0,吞吐量最高可达到13.5×15^(4)bit,出色地完成异构融合通信网络节点负载均衡,合理选取簇头节点,减少节点能量的消耗,提升节点生存周期和网络吞吐量。

物联网开放环境多源异构传感数据低能耗采集

物联网开放环境存在大规模异构数据,增加了传感数据采集的能耗,且容易导致采集误差。为此,提出了物联网开放环境多源异构传感数据低能耗采集。利用数据采集时间、负载均衡度和数据采集过程中的能量损耗为评价函数。并在此基础上构建数据采集分配模型,之后布置相关传感网络节点,收集数据信息。构建传感器节点优化部署模型,优化网络信道分配,降低数据采集能耗,以此达到低能耗数据采集的目的。实验验证表明,使用该方法对数据实施采集,当丢包率达到15%时,数据采集误差为0.03 m。平均能量消耗为5.24 J,平均节点存活数量为141个,平均采集数据量为2.18×10~3 bit,该方法优化了数据采集误差和网络信道的分配性能,有效降低了传感数据的采集能耗。

Self-Organization Approaches for Optimization in Cognitive Radio Networks

Cognitive radio(CR) is regarded as a promising technology for providing a high spectral efficiency to mobile users by using heterogeneous wireless network architectures and dynamic spectrum access techniques.However,cognitive radio networks(CRNs)may also impose some challenges due to the ever increasing complexity of network architecture,the increasing complexity with configuration and management of large-scale networks,fluctuating nature of the available spectrum,diverse Quality-of-Service(QoS)requirements of various applications,and the intensifying difficulties of centralized control,etc.Spectrum management functions with self-organization features can be used to address these challenges and realize this new network paradigm.In this paper,fundamentals of CR,including spectrum sensing,spectrum management,spectrum mobility and spectrum sharing,have been surveyed,with their paradigms of self-organization being emphasized.Variant aspects of selforganization paradigms in CRNs,including critical functionalities of Media Access Control(MAC)- and network-layer operations,are surveyed and compared.Furthermore,new directions and open problems in CRNs are also identified in this survey.

A Fuzzy Neural Network Based Dynamic Data Allocation Model on Heterogeneous Multi-GPUs for Large-scale Computations

The parallel computation capabilities of modern graphics processing units （GPUs） have attracted increasing attention from researchers and engineers who have been conducting high computational throughput studies. However, current single GPU based engineering solutions are often struggling to fulfill their real-time requirements. Thus, the multi-GPU-based approach has become a popular and cost-effective choice for tackling the demands. In those cases, the computational load balancing over multiple GPU ＂nodes＂ is often the key and bottleneck that affect the quality and performance of the real=time system. The existing load balancing approaches are mainly based on the assumption that all GPU nodes in the same computer framework are of equal computational performance, which is often not the case due to cluster design and other legacy issues. This paper presents a novel dynamic load balancing （DLB） model for rapid data division and allocation on heterogeneous GPU nodes based on an innovative fuzzy neural network （FNN）. In this research, a 5-state parameter feedback mechanism defining the overall cluster and node performance is proposed. The corresponding FNN-based DLB model will be capable of monitoring and predicting individual node performance under different workload scenarios. A real=time adaptive scheduler has been devised to reorganize the data inputs to each node when necessary to maintain their runtime computational performance. The devised model has been implemented on two dimensional （2D） discrete wavelet transform （DWT） applications for evaluation. Experiment results show that this DLB model enables a high computational throughput while ensuring real=time and precision requirements from complex computational tasks.

面向低碳园区供需平衡的混杂负荷系统聚合和控制模型构建

随着“双碳”目标的持续推进,园区系统的低碳转型受到了广泛关注。低碳园区具备高比例的清洁能源,其间歇性和波动性问题给园区的安全稳定运行带来了挑战,传统增加储能设备的手段成本高且灵活性低。为此,该文提出了利用园区内丰富的可调节负荷资源,经过有效的聚合管控充分挖掘负荷调节潜力,提供功率转移容量以实现园区自身供需平衡。以园区内各种可调负荷组成的聚合系统为控制对象,针对负荷物理参数的异质混杂特性,提出包括设备嵌入控制、异质聚合控制、系统辨识控制以及混杂协同控制的分层控制算法,由设备嵌入控制自底而上形成稳定可控的异质集群,从混杂协同控制自上而下控制大量混杂负荷输出稳定收敛响应。在系统辨识控制层,构建基于径向基函数神经网络的系统参数辨识模型,辨识异质集群的聚合参数,从而构建稳定可控的异质聚合模型,能够适用于不同负荷种类的异质集群,具有通用性。在混杂协同控制层,构建异质集群协同管控模型,随机选取容量合适的集群参与清洁能源消纳。最后,通过提供清洁能源出力跟踪服务验证了方法的有效性。仿真结果表明,在该文的异质聚合模型下负荷聚合数量提高了11.2%,表明了该文的异质聚合方法能够有效提升负荷的聚合数量。此外,该文混杂负荷分层控制算法的跟踪误差在0.02%以内,验证了该文算法的准确性。

基于动态CRE偏置选择的干扰抑制策略

随着无线通信用户数量的指数性增长,宏小区用户数量超负荷,边缘用户受邻近微小区的干扰逐步加剧,导致整个小区的通信服务质量降低。为解决这些问题,针对双层无线异构网络场景,结合粒子群优化算法和小区范围扩展技术动态地调整微小区服务区域,寻找出小区范围扩展最优偏置值,缓解宏小区高热点区域负载压力并提高系统用户平均传输速率。仿真结果表明,相比静态小区范围扩展偏置值算法,利用动态小区范围扩展偏置选择的宏小区用户数量下降了近10%,即卸载了部分宏用户使其接入微小区,达到为宏小区减负的效果;微小区边缘用户的信干噪比相应提高了约2 dB,系统用户平均传输速率提高,整个小区的通信服务质量有较大提升。

基于模糊神经网络的异构无线网络接入选择算法

针对现有异构无线网络基于模糊逻辑及神经网络的接入选择方法未能合理考虑网络负载状况的问题,提出一种基于RBF(径向基函数)模糊神经网络的接入选择方法。该方法以可接入网络的接入阻塞率相等为模糊神经网络参数强化学习的目标,对网络负载程度具有很好的动态适应性,实现了智能化的接入判决。仿真结果表明,该方法能有效均衡异构无线网络间的负载,保障实时与非实时业务的QoS,并且相对于负载均衡算法(MLB算法)降低了网络的接入阻塞率。

异构无线分组网络中的接入选择机制

传统多接入选择(Multi-Radio Access Selection)方法只针对话音业务,在设计中仅仅考虑已接入用户数及系统容量,由于未将"分组级"信息考虑在内而不适用于无线分组网络。提出了一种新的异构无线分组网络中的接入选择算法,分组业务在多接入选择时考虑了实时、非实时的不同QoS要求和链路自适应机制(链路层重传及自适应调制编码);用户接入后,在保证其QoS的基础上,同时达到不同网络间负载均衡的目的。基于OPNET Modeler的动态仿真对算法性能进行了仿真评估,并与传统的最小用户数和最小路径损耗多接入选择方法在保证业务QoS和负载均衡性能方面进行了比较。

异构蜂窝网络中一种基于匈牙利算法的用户关联方法

在异构蜂窝网络中使用传统的小区选择方法会导致宏基站和小基站的负载失衡,而与小基站关联的用户面临服务质量(Qo S)的降低的问题。针对该问题,提出了一种基于效用函数最大化的用户与基站关联方法。该方法将用户与基站的关联过程建模为双目标优化问题并且线性化为系数可调的效用函数最大化问题,以实现基站负载均衡和用户Qo S之间的折中。通过设计权值系数,将该效用函数最大化问题转化为基于二部图的最大匹配,并用匈牙利算法求得最优解。仿真结果表明,该方法实现了异构蜂窝网络中宏基站与小基站之间的负载均衡,并且通过系数调节,达到了基站负载均衡和用户Qo S之间的折中。

数据中心负载均衡方法研究综述

随着云计算的发展,数据中心网络成为近年来学术界和工业界关注的研究热点.现代数据中心网络通常采用胖树等多根树拓扑结构,存在多条可用路径来提供高对分带宽.由于等价多路径路由等传统的负载均衡方法无法适应数据中心网络中高动态和强突发的流量特性,多种针对数据中心的负载均衡方法不断涌现.围绕数据中心中负载均衡的基本问题,介绍了当前国际国内的研究现状,包括基于中央控制器、基于交换机和基于主机的负载均衡方法,并展望了数据中心网络负载均衡的发展趋势.

异构分层无线网络中的混合动态流量均衡算法研究

结合信道借用和流量转移技术提出了一种混合动态流量均衡算法。使用该算法热点小区可以向紧凑模式中的同类型轻负载小区借用信道,同时还可以按一定条件将呼叫转移到与热点小区重叠覆盖的异构轻负载小区中,每个流量周期借用的信道数和转移的呼叫数将根据热点小区的剩余可用信道数和新呼叫到达率来计算。仿真结果表明,该流量均衡算法能够显著降低热点小区系统和整个异构系统的新呼叫阻塞概率、切换呼叫掉线率,而且整个异构系统的利用率也得到了相应提高。