基于能量采集异构蜂窝网络的功率分配算法研究

针对能量采集异构蜂窝网络,由于能量到达和信道状态的随机性导致离线功率分配算法只能取得理论最优,本文提出了一种在线功率分配算法.算法在每个时隙开始时,基站控制器通过能量判别选出满足开启条件的小蜂窝基站,然后采用基于拉格朗日乘子的两层迭代算法对所选择的小蜂窝基站分配发射功率,能够实际最大化系统在每个时隙的能效.仿真表明在满足基站开启条件的情况下,所提算法可以为密集异构网络提供更高的能量效率.该算法适用于信道状态和能量状态不可预测的网络.     

基于免疫计算的TD-SCDMA网络基站选址优化

为了降低TD-SCDMA网络基站建设代价,给出了一种基于免疫计算的基站选址优化方案。介绍了TD-SCDMA基站建设的困难及基站选址原则,设计了基于实数编码的克隆增殖算子、克隆变异算子及克隆选择算子,给出了求解基站选址优化问题的免疫记忆克隆算法框架,并与文献中的算法进行了对比实验。实验结果表明,该算法获得的基站部署方案能以相对较低的基站建设总代价获得较高的网络覆盖率,具有较好的应用价值。     

基于能量定价的协作OFDM系统网络寿命优化算法

 本文研究两跳协作多中继正交频分复用(OFDM)系统的网络寿命优化问题.为使网络寿命最大化,基于对节点能量的定价提出一种穷举算法,即首先列举所有的子载波配对与中继选择联合决策;在每种决策下利用拉格朗日法求解最优功率分配,使得网络在满足一定吞吐量的前提下消耗能量总价值最小;然后选择损耗能量价值最小的联合决策.由于穷举算法受到计算复杂度的限制,进而基于子载波的单位信噪比(SNR)代价将中继选择与子载波配对分步优化,提出两种低复杂度算法.仿真结果表明,本文各算法的网络寿命性能比已有算法均有显著提高.     

一种基于LEACH的改进型无线传感器网络路由算法

路由算法是无线传感器网络研究的核心技术之一.在LEACH算法的基础上,提出了一种基于距离和能量考虑选择第二层簇头的两层LEACH算法DE-LEACH,有效避免了低能量且离基站较远的节点与基站直接通信,提高了网络生存时间和数据采集能力.利用事件驱动的方法,减少了发送数据量,进一步延长了网络生存期.     

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.     

基于扇形分簇的无线传感器网络路由算法

无线传感网络中低功耗自适应聚类分簇(LEACH)路由算法等概率选取簇首节点,容易导致整个网络节点能量损耗出现极端化,减少网络生存时间。为此,提出一种针对簇首节点选取和分簇的改进LEACH算法。该算法把整个网络区域分为四个扇形区域,在每个区域内独立进行分簇路由;然后基站根据节点剩余能量和与基站的距离进行簇首节点选择,节点根据簇首节点和基站接收信号强度选择路由方式,以均衡网络能量消耗。仿真结果表明,改进LEACH算法的网络寿命是原有LEACH算法的150%,数据吞吐量提升了3倍。     

基于能量和密度的WSNs动态分区成簇路由算法

针对传统的层次型网络存在的分簇不合理和能耗不均衡等问题,提出了一种基于能量和密度的动态非均匀分区成簇路由算法。该算法先根据节点与基站之间的距离将网络合理地进行动态的区域划分,在区域内成簇,使靠近基站的簇规模小于距离基站较远的簇,减少靠近基站的簇首负担和能量消耗;通过综合考虑节点剩余能量和节点密度等因素来优化簇的非均匀划分和簇首的选择,簇首间采取基于数据聚合的多跳传输机制。仿真结果表明,与经典路由算法LEACH相比,该算法能有效均衡节点能耗,延长网络生命周期。     

基于免疫算法的 TD-SCDMA 网络基站选址优化

针对已有3G基站选址优化算法的不足和TD-SCDMA网络的特点,提出了一种基于免疫算法的TD-SCDMA网络基站选址优化方案。建立了基站选址问题的数学模型,设计了基于反学习的种群初始化方案和精英交叉策略,给出了免疫优化算法框架。实验结果表明,该算法不仅能够以较小的建站代价获得较高的网络覆盖率,而且算法具有较好的收敛性。     

基于链路质量的WSN代价均衡路由选择算法

该文针对无线传感器网络中不可靠链路通信耗能过大的问题,基于链路质量进行路由代价函数构建,并为各条路径分配适当的选择概率,采用最小跳数转发策略设计了代价均衡的路由选择算法CBLQ;为了进一步降低和均衡路由代价,分析同跳节点的备选转发条件,又扩展形成了新的路由选择算法CBLQ-E。仿真实验结果证明,两种算法均使网络的能量利用率得到有效提高,同时还降低了网络的数据传输时延。     

5G网络中基于距离感知的动态基站关闭算法

无线通信的能耗问题受到越来越多的重视,能量效率成了5G网络中三大效率特性之一.一般而言,流量负载较低时,基站资源利用率也相对较低,造成基站资源浪费.为解决这个问题,提出一种基于距离感知的动态基站关闭算法.该算法通过动态关闭多余的基站来降低网络能耗,在满足基站负载均衡的条件下,通过估计用户与其关联基站的距离,计算可关闭基站的最大数目.仿真结果表明,所提算法较基于随机关闭的基站节能算法,明显地降低了能耗,并将平均节能百分比提高到88％.     

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.     

A novel antenna allocation technique for green single cell MIMO and MIMO-CoMP downlink transmission

To meet the increasing traffic and energy consumption demands of wireless networks, energy efficiency and energy efficient transmission techniques have become an urgent need for cellular networks. In this work, the problem of base station (BS) power consumption reduction for increased network energy efficiency of downlink TDMA-based transmission is considered. To meet network’s high traffic demand due to high data rates required by large numbers of users, multiple-input multiple-output (MIMO) and coordinated multi-point (CoMP) transmission have been considered. By adopting realistic power consumption models for single cell MIMO and multi-cell MIMO-CoMP networks, enhanced antenna allocation techniques are proposed and their energy efficiency is compared to the conventional power allocation schemes. It is shown that for a target signal to interference plus noise ratio (SINR), the proposed techniques consume less total power compared to traditional schemes, which leads to higher energy efficiency. In addition, for same power level, the symbol error rate (SER) is reduced and system’s sum rate increases, which leads to higher spectral efficiency.     

Energy-aware infrastructure placement for secure communication

Physical layer security is attracting more and more attention due to its inherent channel properties, while the increasing computing ability is not the obstacle for the traditional encryption any longer. In this paper, we study the base station (BS) and relay station (RS) placement problem in a cooperative secure communication system. Moreover, the system energy consumption problem also has been considered, and an energy-aware infrastructure placement for secure communications (EIPSC) scheme is proposed. Based on the analysis results of different candidate position of the security performance, location of the BS is determined and some imperative RSs are placed to guarantee the eavesdropped subscribers' secure communication. To decrease the system energy consumption, we propose a conception of sharing set of RS in order to place the RS as few as possible. During the BS and RS placing as well as adjusting procedure, renewable energy is also been considered to reduce dirty-energy consumption. Through computational experiments, we show our proposed algorithm can get better performance than the traditional placement algorithm not only at the system security guarantee but also at the system energy saving.     

Massive MIMO异构网的高能效资源分配

为满足第五代移动通信系统高频谱效率和高能量效率的需求,提出一种工作在不同频段下行两层异构网中的高能量效率资源分配方法,考虑用户数据率需求和基站最大发射功率。天线和传输带宽是影响系统能量效率的关键因素。通过研究宏基站和小基站的天线资源和带宽分配发现:当系统天线数很大时,发射功耗的影响可以忽略不计;给定带宽分配因子时,达到宏基站或微基站最大发射功率的天线分配因子几乎可以达到最高能效;给定天线分配因子时,系统平均总功耗是关于带宽分配因子的下凸函数,存在全局最优带宽分配因子使能效最高。仿真结果表明,与给定带宽和天线资源的异构网和小小区网络相比,所提出的异构网可以显著提高系统能量效率,而且在大量用户、高数据率需求时能效提升更明显。      

5G网络中基于覆盖区域优先级的微基站休眠算法

随着5G移动通信技术日渐成熟，移动终端数量快速增长，5G无线通信系统基站密集能耗问题突出，提出一种微基站区域分级休眠算法。考虑微基站负载、站间距离、层间配合对微基站休眠的影响，宏基站与宏基站之间重叠覆盖区域中微基站状态转换次数少，优先休眠操作节能效果好。仿真结果表明，节能率为23%，能适应不同的网络规模，在大规模网络中节能效果更优越。     

基于TinyOS的无线传感器网络LEACH算法的改进

LEACH协议是无线传感器网络中最流行的分簇路由协议之一.针对LEACH算法簇分布不均匀以及网络能耗不均衡等问题提出了一种高效节能多跳路由算法.在簇建立阶段,新算法根据网络模型计算出最优簇头间距值,调整节点通信半径以控制簇的大小,形成合理网络拓扑结构;在数据传输阶段,簇头与基站之间采用多跳的通信方式,降低了节点能耗.在TinyOS操作系统下,使用nesC语言设计实现了LEACH-EEMH算法.基于TOSSIM平台的仿真结果表明,新算法较LEACH算法在均衡网络能耗、延长网络寿命方面具有显著优势.     

Energy efficient power allocation strategy for downlink MU-MIMO with massive antennas

With the increasing energy consumption, energy efficiency （EE） has been considered as an important metric for wireless communication networks as spectrum efficiency （SE）. In this paper, EE optimization problem for downlink multi-user multiple-input multiple-output （MU-MIMO） system with massive antennas is investigated. According to the convex optimization theory, there exists a unique globally optimal power allocation achieving the optimal EE, and the closed-form of the optimal EE only related to channel state information is derived analytically. Then both the approximate and accurate power allocation algorithms with different complexity are proposed to achieve the optimal EE. Simulation results show that the optimal EE obtained by the approximate algorithm coincides to that achieved by the accurate algorithm within the controllable error limitation, and these proposed algorithms perform better than the existing equal power allocation algorithm. The optimal EE and corresponding SE increase with the number of antennas at base station, which is promising for the next generation wireless communication networks.     

A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks

In wireless sensor network, a large number of sensor nodes are distributed to cover a certain area. Sensor node is little in size with restricted processing power, memory, and limited battery life. Because of restricted battery power, wireless sensor network needs to broaden the system lifetime by reducing the energy consumption. A clustering‐based protocols adapt the use of energy by giving a balance to all nodes to become a cluster head. In this paper, we concentrate on a recent hierarchical routing protocols, which are depending on LEACH protocol to enhance its performance and increase the lifetime of wireless sensor network. So our enhanced protocol called Node Ranked–LEACH is proposed. Our proposed protocol improves the total network lifetime based on node rank algorithm. Node rank algorithm depends on both path cost and number of links between nodes to select the cluster head of each cluster. This enhancement reflects the real weight of specific node to success and can be represented as a cluster head. The proposed algorithm overcomes the random process selection, which leads to unexpected fail for some cluster heads in other LEACH versions, and it gives a good performance in the network lifetime and energy consumption comparing with previous version of LEACH protocols.     

Energy efficiency of massive MIMO wireless communication systems with antenna selection

Massive multiple-input multiple-output (MIMO) requires a large number (tens or hundreds) of base station antennas serving for much smaller number of terminals, with large gains in energy efficiency and spectral efficiency compared with traditional MIMO technology. Large scale antennas mean large scale radio frequency (RF) chains. Considering the plenty of power consumption and high cost of RF chains, antenna selection is necessary for Massive MIMO wireless communication systems in both transmitting end and receiving end. An energy efficient antenna selection algorithm based on convex optimization was proposed for Massive MIMO wireless communication systems. On the condition that the channel capacity of the cell is larger than a certain threshold, the number of transmit antenna, the subset of transmit antenna and servable mobile terminals (MTs) were jointly optimized to maximize energy efficiency. The joint optimization problem was proved in detail. The proposed algorithm is verified by analysis and numerical simulations. Good performance gain of energy efficiency is obtained comparing with no antenna selection.     

An efficient resource optimization scheme for D2D communication

With the rapid development of wireless technologies, wireless access networks have entered their Fifth-Generation (5G) system phase. The heterogeneous and complex nature of a 5G system, with its numerous technological scenarios, poses significant challenges to wireless resource management, making radio resource optimization an important aspect of Device-to-Device (D2D) communication in such systems. Cellular D2D communication can improve spectrum efficiency, increase system capacity, and reduce base station communication burdens by sharing authorized cell resources; however, can also cause serious interference. Therefore, research focusing on reducing this interference by optimizing the configuration of shared cellular resources has also grown in importance. This paper proposes a novel algorithm to address the problems of co-channel interference and energy efficiency optimization in a long-term evolution network. The proposed algorithm uses the fuzzy clustering method, which employs minimum outage probability to divide D2D users into several groups in order to improve system throughput and reduce interference between users. An efficient power control algorithm based on game theory is also proposed to optimize user transmission power within each group and thereby improve user energy efficiency. Simulation results show that these proposed algorithms can effectively improve system throughput, reduce co-channel interference, and enhance energy efficiency.