超密集异构无线网络中基于位置预测的切换算法

在密集异构蜂窝网络和无线局域网络构成的超密集异构无线网络中, 变速移动的车辆终端会面临更加频繁的切换,导致用户服务质量(QoS)变差。该文针对上述问题,首先,利用高斯马尔可夫移动模型,预测车辆下一时刻的位置,筛选出满足终端服务质量的候选网络集,与当前的候选网络集做交运算,其次,当前接入网络不在交集中,则使用变步长的萤火虫算法寻找最佳网络;再次,对因预测误差导致的切换失效,则把终端用户迁移到宏蜂窝,以保证通信的持续性。仿真结果表明,在超密集异构无线网络中,使用该文所提算法能够减少乒乓切换等频繁切换现象,同时,提升了用户的服务质量和网络吞吐量。

     

异构云无线接入网络:原理、架构、技术和挑战

为了缓解密集异构无线网络节点间严重的干扰,提高节点间分布式协作处理增益,同时解决云无线接入网络控制信息传输复杂、无法和已有移动通信网络融合等问题,提出了异构云无线接入网络(H-CRAN)作为5G移动通信系统的接入网解决方案.所提H-CRAN的核心是将云无线接入网络与密集异构无线网络融合,将控制平面功能从云无线接入网络中抽离,通过已存的异构大功率节点实现控制平面功能和全网的无线覆盖,利用无线射频单元实现热点区域海量业务的大容量传输.介绍了H-CRAN的系统架构、关键技术组织和研究技术挑战等.     

异构无线网络路由策略研究综述

异构无线网络能够利用各种网络的链路迅速建立连接、实现数据的传递,在军事、水灾、火灾、地震等重大灾害上具有重要的应用价值.因此,设计一种能够提高数据发送效率和网络抗毁性的异构无线网络自适应路由策略具有重要意义.首先介绍了无线局域网、无线蜂窝网络、自组网、延迟容忍网络等各种无线网络的特点;然后对各种异构无线网络路由进行分类并阐述;最后总结现有异构无线网络路由的不足,并对未来研究工作进行了展望.     

面向5G无线网络的智能干扰管理技术

作为5G关键技术之一的超密集组网是满足未来千倍数据流量需求的主要手段.然而,网络密集化部署将导致严重的干扰问题,因此干扰管理是5G超密集网络中的重要研究课题.首先从规避及利用两个方面归纳总结现有的干扰管理算法.其次,为了更好地适应未来5G无线网络智能化、异构化的发展趋势,提出了一种智能的无线网络干扰管理体系,实现了无线网络环境与干扰管理的动态紧耦合.最后,从干扰管理与资源管理的本质联系这一角度出发,介绍了3种联合异构网络资源的新型干扰管理算法,有效实现了网络资源的高效利用和网络容量的提升.     

5G绿色超密集无线异构网络:理念、技术及挑战

超密集无线异构网络被公认为是大幅提升无线网络容量、解决蜂窝网所面临的1 000倍数据量挑战最富有前景的一种5G组网技术.但是,与5G愿景背道而驰的是网络的超密集化部署在提升谱效的同时也极度地增大了系统的能耗,降低了通信能效.旨在利用超密集异构网络具有的全网管理能力和网络分集效应,从宏观的网络匹配与微观的资源聚合两方面探索超密集异构网络绿色演进的关键理论与技术.首先论述了绿色演进的基本理念,然后提出了利用负载自适应的基站开关控制、主动缓存和干扰感知的跨网资源分配三大关键技术来提升网络的能效,同时也讨论了每种技术在应用时所面临的挑战.     

非均匀密集无线组网

无线密集网络作为未来移动通信系统的主要场景,能提供超高的、非均匀的数据量及海量的连接服务,并有提高无线链路的能效以及利用更高的频谱等诸多优势,将成为未来移动通信系统的主要架构之一,研究无线密集组网架构及关键技术意义重大。无线密集网络的网络架构、网络建模分析以及异构密集网络之间的融合是非均匀密集无线网络中的重要研究问题。     

分层组网场景下小基站ID分配算法研究

分层异构网络是下一代无线蜂窝网络的发展趋势,密集的多类型节点混合组网将给系统ID分配带来巨大的挑战。基于此,提出了一种适用于无线分层异构网络场景的小基站ID优化配置算法,将多层复杂网络进行扁平化,并灵活运用平面图理论有效地解决网络节点ID冲突和混淆问题,降低复用干扰,从而达到准确区分小区的目的。     

认知异构蜂窝网络中改进蜉蝣算法的资源分配策略

针对认知异构蜂窝网络中上行链路资源分配的优化问题,提出认知异构蜂窝网络中改进离散蜉蝣算法的资源分配算法。认知异构蜂窝网络模型中,考虑用户层间干扰和带外干扰引入功率控制策略控制发射功率来干扰抑制,基于用户服务质量（QoS）需求和干扰阈值约束,最大化能量效率为优化目标,利用改进离散蜉蝣算法优化求解得出最优分配方案。引入不完全Gamma和Beta分布函数的动态自适应权重、黄金正弦位置更新策略,提升蜉蝣算法的收敛速度和搜索能力。仿真实验表明,基于接收SINR的闭环功率控制动态调整用户端的发射功率,能有效抑制用户间的干扰,GSWBMA求解资源分配问题具有良好的寻优效率和收敛性能,有效提升系统能量效率和用户传输速率,保证用户QoS需求。     

面向移动云计算接入网络的高能效无线资源管理

双层宏蜂窝/小蜂窝异构网络不但能解决室内用户的覆盖问题,还可以大幅度降低网络能量消耗。但是,网络中的跨层和层间干扰会降低网络性能,高能效的无线资源分配和优化方法无疑将成为解决此问题的有效途径。首先,给出了一个移动云计算管理框架。然后,针对无线单链路定义了空间传输效率,比较和分析了多种单链路效率度量的含义和数学性质,并针对基于LTE-A(long term evolution-advanced)的移动云计算接入网络,提出了一种高能效的无线资源管理算法。为了快速地达到最优解,设计了一个快速收敛的迭代算法。仿真分析说明此资源管理算法实现了移动云计算系统能量效率和吞吐量的高效折中。     

分层异构无线网络的协议架构和关键技术

分层异构无线网络技术因其可以有效提高网络频谱效率、满足不同热点覆盖需求、大幅提升系统容量等优点,而成为当今LTE-Advanced系统中的关键技术之一.总结了分层异构无线网络的背景、定义以及架构,并重点讨论了由于传统网络拓扑结构的改变而引发的干扰问题,详述了3GPP标准中的增强干扰协调方案,最后分析了分层异构无线网络中的其他关键技术,如网络自组织、回程链路设计、小区选择和切换、节能、认知无线电技术等.这些关键技术对于分层异构无线网络商用化部署具有指导意义.     

基于区域感知贝叶斯决策的5G超密集异构网络联合垂直切换技术研究

针对无线通信异构网络切换中最优接入网的选择问题,现有研究主要集中于解决在两个网络间选择切换,而对于未来5G（5th Generation）通信系统中超密集网络的切换问题研究较少.本文就研究了5G无线通信系统中超密集异构网络的选择切换.文中提出了一种基于区域感知贝叶斯决策的联合垂直切换方式,该方案通过选择超密集异构接入网络的最优切换概率从而解决超密集网络切换问题.通过对移动用户（mobile station）从宏基站进入超密集服务小区,以及在小区内和小区间移动的不同场景进行仿真分析,研究结果表明本文决策方式能够准确地选择要切换的网络,因而该方案能够适用于超密集网络的应用场景.     

NOMA-MEC系统中基于改进遗传算法的协作式计算卸载与资源管理

为平衡网络负载与充分利用网络资源,针对超密集异构的多用户和多任务边缘计算网络,在用户时延约束下,该文构造了协作式计算任务卸载与无线资源管理的联合优化问题以最小化系统能耗。问题建模时,为应对基站超密集部署导致的严重干扰问题,该文采用了频带划分机制,并引入了非正交多址技术(NOMA)以提升上行频谱利用率。鉴于该目标优化问题具备非线性混合整数的形式,根据多样性引导变异的自适应遗传算法(AGADGM),设计出了协作式计算卸载与资源分配算法。仿真结果表明,在严格满足时延约束条件下,该算法能获取较其他算法更低的系统能耗。     

一种超密集异构网中联合干扰协调方法研究

针对5G超密集异构网中严重的跨层干扰问题,该文提出一种基于现有的增强型小区间干扰协调技术和协同多点传输技术的联合干扰协调方法。运用随机几何理论工具推导了两层超密集异构网下用户的中断概率,频谱效率和网络平均遍历容量表达式。仿真结果表明：该文提出的联合的干扰协调方案,相比于传统协同多点传输技术,不仅降低了协作用户数目,同时使得用户在信干比阈值为0 dB时的中断概率降低了15%;相比较于增强型小区间干扰协调技术,在偏置值为10 dB时,扩展区域的用户频谱效率改善为35%,整个网络平均遍历容量提升了3.4%。

     

Energy-Efficient Cooperative Transmission in Heterogeneous Wireless Networks with QoS Constraint

Heterogeneous wireless network is an effective scheme to improve both the spectrum efficiency and energy efficiency of cellular system. However, interference limits the performance of such heterogeneous wireless network seriously. Hence cooperative decision making, i.e., cooperative transmission is necessary. In this paper, we develop an energy-efficient scheme for cooperative transmission in heterogeneous wireless networks with QoS constraint. Since too much signaling is needed for absolutely centralized cooperation, which is infeasible in practical system, partly cooperation is considered in this paper. We first formulate the problem as a two-stage Stackelberg game. In the first stage, an energy-efficient transmission scheme for macrocell users (MUE) is proposed based on the interference state of the MUEs. In the second stage, after obtaining the SINR and target SINR of the MUEs, the femtocell base stations update their beamformers and optimize the transmission power individually through a non-cooperative game, the existence and uniqueness of the Nash equilibrium is proved. Then an iterative algorithm is proposed to obtain the Stackelberg equilibrium. Simulation results show the performance gain of the proposed scheme.     

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.     

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.     

The analysis of coverage probability,ASE and EE in heterogeneous ultra-dense networks with power control

The ultra-dense network is a promising technology to increase the network capacity in the forthcoming fifth-generation (5G) mobile communication networks by deploying lots of low power Small Base Stations (SBSs) which overlap with Macro Base Stations (MBSs). The interference and energy consumption increase rapidly with the number of SBSs although each SBS transmits with small power. In this paper, we model a downlink heterogeneous ultra-dense network where a lot of SBSs are randomly deployed with MBSs based on the Poisson point process. We derive the coverage probability and its variance, and analyze the area spectral efficiency and energy efficiency of the network considering three Fractional Power Control (FPC) strategies. The numerical results and Monte Carlo simulation results show that power control can mitigate the interference and balance the performances of inner-user and edge-user equipments. Especially, a great improvement of energy efficiency is archived with a little loss of area spectral efficiency when FPC is adopted. Finally, we analyze the effect of base stations’ (BSs’) sleeping on the performance of the network when it is partially loaded.     

基于无线传感网络的小世界演化研究

提高无线传感网络的传输效率、节约整个网络的能量消耗是我们研究无线传感网络的重要内容。通常都是通过改变网络的拓扑结构来实现效率的提高,本文给出了一种新的思路去节约能耗。本文讨论了权重与与无线传感网传输效率的关系,将有权的无线传感网络链路上的权值进行随机的分配,我们发现将权重随机分配后网络的传输效率得到了提高并且随着权重分布概率的增大网络的传输效率不断增大。这为我们研究权重对无线传感网络的影响提供了基础。