动态关断资源栅格的异构无线网络基站节能算法

针对现有的基于载频/时隙关断的基站节能算法没有考虑异构网络之间的协同或者只考虑单一的业务与实际网络情况不相适应的问题,在异构网络环境下提出了动态关断资源栅格(RG,resource grid)的基站节能新算法——DRGSM(dynamic resource grid shut mode)算法。该算法根据网络的即时负载,当有业务接入时,动态打开RG以满足业务的QoS要求;当RG闲置时,即将其关断以实现节能。仿真结果表明,所提算法较现有的基于载频/时隙关断的基站节能算法,明显地降低了能耗,提高了节能百分比。     

关于5G中继技术的节能算法研究

针对5G中继网络的节能降耗问题进行了研究.中继技术是第五代移动通信(5G)的重要技术,通过关闭5G网络中部分业务负载量较低的基站来达到节能降耗的目标,然而在此过程中网络时常会发生基站状态的转换,所以网络的拓扑情况也会随着时间及业务的变化而发生改变.联合考虑网络的服务质量(QoS)及网络功耗,针对5G中继蜂窝网络提出了一种动态节能(DES)算法来优化QoS和网络功耗的综合性能.仿真结果表明,DES算法可以使网络得到重新配置,关闭部分基站及中继节点,从而大幅提高节能效果,尤其是网络负载降低时.     

一种改进的基于时间竞争成簇的路由算法

为平衡无线传感器网络中的簇头负载并进一步降低多跳传输能耗,文中提出了一种改进的基于时间竞争成簇的路由算法。该算法通过限制近基站节点成簇入簇,以防止近基站节点成簇入簇的节能收益无法补偿成簇入簇能耗;利用基站广播公共信息和基于时间机制成簇,以减少节点基本信息交换能耗;通过候选簇头中继来平衡簇头负载。候选簇头的评价函数综合考虑了剩余能量和最优跳数的理想路径,以期在保持中继负载平衡的基础上尽量降低多跳能耗。仿真结果显示,该算法较LEACH和DEBUC算法延长了以30%节点死亡为网络失效的网络生存周期,表明该算法在降低节点能耗和平衡负载方面是有效的。     

异构蜂窝网络中用户关联与基站功率的协同优化

为实现异构蜂窝网络中宏基站和小基站之间的负载均衡,提出了一种基于效用函数最大化模型的用户关联机制和基站功率控制的协同优化方案.通过迭代算法求解该协同优化问题,首先在基站功率固定的情况下求得最佳用户关联策略,然后在所得的用户关联策略基础上通过Zoutendijk可行方向法求得基站最佳功率.通过协同优化获得的用户关联策略和基站功率控制实现了基站之间的负载均衡,通过降低宏基站功率和关闭闲置小基站降低了基站的能耗.仿真实验表明,所提方案和不实施功率控制的用户关联策略相比,实现了宏基站与小基站之间的负载均衡,降低了宏基站对小基站用户的干扰,提升了小基站用户的信号干扰噪声比,用户速率中位值提高了20％.     

一种用于基站休眠的改进粒子群算法

基站休眠技术被认为是提高无线通信网络能效的最有效方法之一,可以通过动态地关闭部分低负载基站来减少整体网络的能耗。在基站休眠算法中,集中式算法的性能往往比分布式算法更好。与此同时,随着无线接入网架构向集中式演进,集中式休眠算法将得到更广泛的应用。因此,针对基站休眠的集中式算法问题,文章提出了一种能用于基站休眠的改进的粒子群集中式算法。该算法对传统的粒子群算法在初始化方面进行了改进,通过每个基站的相邻基站数目和负载进行多目标优化,得到每个基站的初始化阈值,然后每个基站与阈值进行比较来进行初始化。仿真结果表明,文章提出的算法在性能上比传统分布式算法更好,并且与最优解情况相差很小,性能比较好。     

一种超密集网络基站分簇管理算法

为了降低超密集网络中基站管理算法的计算复杂度并提升基站的能源使用效率,根据用户密度、网络负载量等信息,提出了一种基于分簇的动态管理基站算法.该算法首先根据用户测量报告计算出理论最小需求基站数,然后对基站进行合理的网络分簇,最终通过粒子群优化算法确定基站休眠组合.仿真结果表明,与未进行分簇的基站管理算法相比,该算法可以降低约60%的计算复杂度,并能有效降低基站能源消耗.     

基于基站休眠的负载合并动态功率控制算法

在蜂窝系统中,采用基站休眠策略时,如果固定设置工作基站功率,将导致基站低负载时,网络功率效率降低。提出基于基站休眠的负载合并动态功率控制算法,低负载的宏基站进入休眠,负载较高的宏基站为工作基站,工作基站根据本小区及其分担的邻近休眠基站内的负载变化,动态调节自身的功率。仿真结果表明,所提出的动态功率控制算法在满足用户服务质量的前提下,有效降低了整个蜂窝网络的功率消耗。     

一种面向结构化P2P网络的基于闲谈的资源发现方法

 结构化P2P网络下的多属性资源发现一直是一个公开问题.本文针对当前一种新颖的、优于传统方法的多属性资源发现方法－PIRD,深入分析了其在网络动态变化时可能出现的低查询效率问题,并提出一种解决方法:基于闲谈的PIRD(Gossip-based PIRD,G-PIRD).G-PIRD通过闲谈算法估计网络规模,动态调整资源索引的发布以保证高的查询效率.同时针对G-PIRD可能导致的负载不均衡问题,提出一种基于有界LSH(Bounded LSH,B-LSH )的负载均衡策略.试验证明:G-PIRD能动态适应网络变化,保证高效率的多属性资源发现;以及G-PIRD的负载均衡策略在保证高查询效率的同时,大大地降低了节点的索引负载.     

基于指派模型的LTE-A异构网节能负载均衡算法

无线通信能耗问题受到越来越多重视,能量效率成为下一代无线通信(5G)三大效率特性之一。当前异构网络(heterogeneous network,Het Net)负载均衡的研究缺少对网络整体功耗的优化考量,根据异构网重叠覆盖的特性建立了面向指派模型的用户负载功率模型,并针对有限的用户负载问题提出一种扩增指派算法求解宏扇区内所有基站功率最小化问题,在LTE系统级仿真平台中仿真表明,无论基站是否支持休眠功能,此算法都能在保证用户速率的条件下有明显的节能效果。     

基于超级基站的迁移机制应用研究

未来无线通信接入网将是基于池化资源的集中式网络架构,实现集中基站资源的动态管理和负载均衡。对基站协议资源展开研究,通过分析超级基站架构及迁移算法,搭建基于通用处理器的基站协议栈迁移平台,并进行基站协议栈迁移验证实验。仿真结果表明,基于预拷贝的基站协议栈迁移算法能有效平衡总迁移时间和停机时间,在迁移过程中基站协议栈正常处理业务,基本满足了迁移基站协议栈需求。为超级基站整个池内资源动态管理的实现提供参考。     

Distributed fuzzy logic based energy‐aware and coverage preserving unequal clustering algorithm for wireless sensor networks

In an energy‐constrained wireless sensor networks (WSNs), clustering is found to be an effective strategy to minimize the energy depletion of sensor nodes. In clustered WSNs, network is partitioned into set of clusters, each having a coordinator called cluster head (CH), which collects data from its cluster members and forwards it to the base station (BS) via other CHs. Clustered WSNs often suffer from the hot spot problem where CHs closer to the BS die much early because of high energy consumption contributed by the data forwarding load. Such death of nodes results coverage holes in the network very early. In most applications of WSNs, coverage preservation of the target area is a primary measure of quality of service. Considering the energy limitation of sensors, most of the clustering algorithms designed for WSNs focus on energy efficiency while ignoring the coverage requirement. In this paper, we propose a distributed clustering algorithm that uses fuzzy logic to establish a trade‐off between the energy efficiency and coverage requirement. This algorithm considers both energy and coverage parameters during cluster formation to maximize the coverage preservation of target area. Further, to deal with hot spot problem, it forms unequal sized clusters such that more CHs are available closer to BS to share the high data forwarding load. The performance of the proposed clustering algorithm is compared with some of the well‐known existing algorithms under different network scenarios. The simulation results validate the superiority of our algorithm in network lifetime, coverage preservation, and energy efficiency.     

Optimized BS assignment and resource allocation in cooperative OFDM networks

Cooperative transmission (CT) and orthogonal frequency division multiple (OFDM) are promising technologies for extending coverage and increasing throughput in broadband wireless access (BWA) networks. Therefore, we propose a novel BWA network architecture, that can set up inter-cell collaboration using physical layer cooperative transmissions among distributed wired access networks with a powerful coordination capability at the central office. However, conventional base station (BS) assignment and resource allocation schemes cannot be used directly because a user can be serviced by more than one BS with cooperative transmission technology. This study proposes a novel framework of BS assignment and resource allocation in a cooperative OFDM network. We provide three approaches of resource allocation for minimizing bandwidth usage, minimizing transmission power consumption, and balancing resource costs respectively. An optimized resource allocation scheme can be implemented by flexibly choosing one of these approaches based on network load. The simulation results show the efficiency of the proposed mathematical formulations and linearization approach of our scheme. The performance benefit of CT technology on the bandwidth saving is demonstrated by comparing the new BS assignment and resource allocation scheme with conventional non-cooperative transmission.     

An energy‐saving algorithm for cloud resource management using a Kalman filter

Cloud computing has emerged as a promising technique to provide storage and computing component on‐demand services over a network. In this paper, we present an energy‐saving algorithm using the Kalman filter for cloud resource management to predict the workload and to further achieve high resource availability with low service level agreement. Using the proposed algorithm, one can estimate the potential future workload trend then predict the computing component workload utilizations and further retrench energy consumption and achieve load balancing in a cloud system. Experimental results show that the proposed algorithm achieves more than 92.22% accuracy in the computing component workload prediction, improves 55.11% energy in energy consumption, and has 3.71% in power prediction error rate, respectively. Copyright © 2013 John Wiley & Sons, Ltd.     

结构化P2P资源搜索算法及其在未来光网络中的应用

推进绿色节能光网络是未来光网络面临的重要挑战之一。基于DHT的结构化P2P资源搜索算法因为可扩展性、鲁棒性、负载均衡、查询效率高等特点更适用于具有云计算、物联网、移动互联网等特点的未来光网络中的资源搜索。研究结构化P2P资源搜索算法的特点,从降低能耗角度分析算法对未来光网络产生的影响,提出在名址分离架构的未来网中快速资源搜索设计方法,最后探讨P2P资源搜索算法在未来光网络中面临的挑战。     

超密集网中一种基于人工蜂群的节能分簇算法

超密集网络中,密集部署的低功率基站将会加大系统的能耗,并且造成紧缺频谱资源的浪费.探寻干扰协调和系统节能的可行性方法在超密集网络架构下提出基站的休眠—唤醒—活跃机制,减小了休眠基站直接转为活跃状态的开启时间;另外,提出一种基于人工蜂群染色分簇算法,尽可能使用最少的颜色给拓扑图中的小区染色,并对簇内活跃基站进行优化功率分配.经仿真表明,休眠—唤醒—活跃机制能够提升系统的能源效率,染色分簇算法也可以改善用户的频谱效率和吞吐量.     

Design and operation of energy efficient heterogeneous mobile networks

During the last two decades we have witnessed a rapid growth in wireless communication services, which has dramatically increased the number of necessary radio access components to provide the adequate capacity and acceptable quality of service. The power cost for operating this huge number of radio Base Stations (BSs) is under serious consideration by the mobile communication industry and recent research efforts have focused on energy efficient design and optimization of the radio access network in order to alleviate the energy consumption and mobile network operators OPEX. In this work, we consider a deployment of heterogeneous BSs to serve a given geographical area and propose two energy-aware algorithms to optimally determine the operational mode of those BSs under various traffic load conditions. Performance evaluation results show that the proposed algorithms can provide near optimal solutions and achieve substantial network energy consumption reduction without compromising the efficient operation of the mobile network. We further benefit from the outcome of this formulation framework and propose BS activation schemes that yield proper BS activation profiles for continuous-time operation in the same network deployment. Various traffic loads are investigated in several simulation campaigns and our proposed schemes yield quite satisfactory energy saving gains compared to fully operational topology networks in all scenarios of interest examined.     

Cooperation-enabled energy efficient base station management for dense small cell networks

Dense small cell networks are deployed for future wireless communication to meet the ever-increasing mobile traffic demand. However, network densification will significantly increase the energy budget and lead to energy inefficiency due to the constant operation of network hardware. In this paper, we consider cooperation-enabled dynamic base station (BS) management for downlink dense small cell networks. By introducing two traffic-aware sleep modes, i.e., deep sleep mode and opportunistic sleep mode which are operating in different time and energy consumption scales, the network hardwares are turned to be the resources that can be occupied and released dynamically. Small cell BSs (SBSs) with zero or low load are completely switched off and reside in deep sleep mode during a predefined time interval. At each time slot, SBS dynamically turn some antennas and associated physical components into opportunistic sleep mode according to the short term traffic distribution, and the users are jointly served by the remaining antennas via cooperative transmission. The corresponding sleep mode decision making are presented respectively to find the optimal number of SBS and antennas that should be switched off. Numerical results are then presented to illustrate the superior performance in terms of energy efficiency gain. In summary, the proposed cooperation-aided sleep strategies for dense small cell networks take both traffic features and optimal cooperative transmission into account, and can achieve great energy saving while maintaining required quality of service.     

Integrated load‐based power saving for BS and MSS in the IEEE 802.16e network

In our previous work, the limitation of standard type I and II power saving in IEEE 802.16e was discussed, and the idea of load‐based power saving (LBPS) was proposed for better power‐saving efficiency. LBPS measures traffic load and adaptively generates proper sleep schedule for the current load. Three LBPS schemes have been proposed for mobile subscriber station (MSS) power saving. In this paper, base station (BS) power saving is taken into consideration, and our previously proposed LBPS schemes, are extended and revised to integrate both BS and MSS in sleep scheduling. Two strategies of integrated power saving, MSS first and BS first, each with associated LBPS schemes are proposed in the paper. A three‐staged concept combining the proposed strategies is also presented to make the best of integrated power saving. A simulation study shows that the proposed schemes can effectively achieve high power‐saving efficiency for both BS and MSS. Copyright © 2013 John Wiley & Sons, Ltd.