超密集网络中最大化网络吞吐量的预测资源分配

在超密集网络中,小区间干扰严重制约了小区边缘用户的性能体验以及网络吞吐量。无线大数据分析的飞速发展,使得人们有可能通过预测未来的信道状态来分配资源,在无干扰网络中可达到很大的性能增益。但是在干扰网络中如何利用预测信息,在分配资源的同时有效协调干扰还是一个尚未研究的问题。本文分析了干扰网络中预测资源分配的设计难点和存在的问题,针对该问题提出了相应的解决方法,将资源分配建模成一个凸优化问题,通过求解优化问题得到最优的资源分配方法。仿真表明,与未知预测信息的最大化网络吞吐量方法相比,所提方法能够有效提高用户的成功传输率、平均传输进度和网络的吞吐量。当用户数据需求较大时,所提方法可以提供较大的网络性能增益。      

基于用户分簇的认知超密集网络资源分配

针对5G时代小基站的密集部署带来的复杂干扰问题,对下行的认知无线电超密集网络下的资源分配进行了研究。为减小网络干扰,提高次用户吞吐量,提出了一种改进的基于用户分簇的资源分配算法。基于基站的覆盖范围,选出用户的强干扰基站,以用户-基站干扰关系建立用户-用户干扰图,按用户受到的平均弱干扰划分优先级对用户分簇,再为簇集群预分配频段,为每个簇分配对应频段中效用最大的信道。该资源分配算法能准确反映用户间的干扰关系,保障资源分配公平性。仿真结果表明,当用户密度与基站密度均较大时,与相同场景的已有算法相比,该改进算法有较好的抗干扰能力,能有效提高次用户的吞吐量。     

超密集网络中基于轨迹预测的资源规划

在超密集网络中,小区间干扰严重制约了小区边缘用户的性能体验以及网络吞吐量。利用用户轨迹预测和信号地图可获得用户未来的平均信道信息,从而为用户规划未来的传输资源,相对于非预测方法可以大幅度提升网络性能。现有对资源规划的研究大都考虑较为理想的假设,为了分析基于实际预测性能达到的资源规划性能增益,本文研究了超密集网络中的轨迹预测方法和基于实际轨迹预测性能的资源规划性能。仿真结果表明,所提出的轨迹预测方法能够满足资源规划的要求,所提出的资源规划策略在任意预测窗长度下都能达到较好的性能,当预测窗长为3分钟时,相对于非预测的干扰管理方法能够将用户满意率提高45%以上。      

超密集异构网中的Q学习资源调度算法

在超密集异构蜂窝网络中,随着低功率基站大量增加,且复用相同的频谱资源,小区间干扰(ICI)可能会变得很强,从而降低系统整体吞吐量。因此,文中提出一种基于Q学习的资源调度(QLRS)算法以尽可能地最大化系统容量。算法首先将小基站进行分簇,在每个调度周期根据簇内用户数量为每个簇调度资源;然后以系统整体吞吐量和能效为优化目标,对簇内有关联用户的小小区进行资源变更和优化,并将收益记录于Q表中,Q表经多次迭代收敛后,得到系统最优资源分配方案。仿真结果表明,与其他资源分配算法相比,文中提出的算法在保证能源效率与宏蜂窝吞吐量的条件下,进一步提高了系统整体吞吐量。     

5G密集组网资源分配方法研究

针对当前5G超密集网络部署的干扰和用户切换失败率显著增加的问题，提出了一种5G超密集组网资源分配方法，利用贪婪策略对系统状态进行决策，计算每个状态对应的即时奖励；将每个状态所对应的即时奖励数据输入到深度神经网络中，从而获得近似长期价值；将近似长期价值和实际长期价值做对比，以更新长期价值函数；当长期价值函数收敛时，得到每个时刻的资源分配方案并进行资源分配。该方法基于深度强化学习来解决随机优化问题，降低了求解的复杂性，可有效降低干扰和切换次数以及切换失败率。     

超密集小区中基于二分图优先级的资源分配算法

由于在超密集网络中小基站密集的部署,用户数据量空前增加,对数据速率的要求不断提高,所以在有限的资源下如何高效地将资源分配给用户尤为重要。提出了一种小区分簇算法,根据小区簇的通信业务、通信负载量等条件将各个小区分为不同的优先级,引入二分图,以小区簇的优先级为依据建立频谱资源与小区簇之间的匹配关系,并提出一种低复杂度的贪婪算法。仿真结果表明所提算法能够有效提高系统性能,并且有效完成频谱资源的分配。     

基于能量效率优化的超密集网络干扰协调方案

针对超密集网络场景,提出一种基于能量效率优化的干扰协调方案,通过粒子群优化算法联合调整基站活跃状态概率、基站发射功率以及协作用户数量,并在确保边缘用户满意度的前提下,优化系统的能量效率。实验结果证明,提出的方案相比传统联合传输方案可获得10.30%的能量效率增益,相比非协作方案可获得18.73%的能量效率增益。     

机器学习在5G超密集网络切换中的应用

超密集网络（Ultra-dense Network,UDN）作为5G网络架构的关键技术,其切换时延及无效切换等已成为网络发展的巨大挑战。提出了一种基于集成策略的机器学习算法,并结合用户的移动性数据,进行较高精确度的切换预测,减少切换时延及非必要切换等目标。仿真结果表明,采用结合改进机器学习算法的切换策略,不必要切换率降低了40.2%,平均时延降低了28.6%。     

超密集网络中基于业务特征的干扰协调技术研究

为了应对未来爆炸性的移动数据流量增长、海量的设备连接、不断涌现的各类新业务和应用场景,第五代移动通信（5G）系统应运而生。3GPP提出的超密集网络（UDN）已成为保证5G时代业务量千倍增长的核心技术,但同时它也面临着大量的问题：超密集部署的基站以及超密集分布的用户使得同频干扰异常严重,小区间干扰协调不可避免;不同用户有不同的业务需求,业务多样性使得干扰协调技术的设计变得更加困难;密集部署基站将耗费大量的能源,不利于绿色通信。针对以上问题,本文以业务特征为着眼点,设计了两种适用于超密集网络的干扰协调技术——大规模CoMP（协作多点传输）、ON/OFF（动态小区开关）机制。通过对三种典型业务的分析与建模,并基于三种业务对两种干扰协调技术进行仿真,仿真结果表明在特定的业务模型下,两种干扰协调技术均能有效地降低系统干扰,提升系统性能。     

Low Complexity Joint Spectrum Resource and Power Allocation for Ultra Dense Networks

In this paper, we propose a low complexity spectrum resource allocation scheme cross the access points(APs) for the ultra dense networks(UDNs), in which all the APs are divided into several AP groups(APGs) and the total bandwidth is divided into several narrow band spectrum resources and each spectrum resource is allocated to APGs independently to decrease the interference among the cells. Furthermore,we investigate the joint spectrum and power allocation problem in UDNs to maximize the overall ...     

Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

With the evolution of the sixth generation(6G) mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge comp...     

Green Resource Allocation for Multiple OFDMA Based Networks: A Survey

Orthogonal frequency division multiple access (OFDMA) is a popular and widely accepted multiple access technique to provide high data rate services in a mobile environment in the area of wireless communications. OFDMA can provide better flexibility in allocating the radio spectra by utilizing subcarrier allocations, scheduling, and energy control to obtain multi-dimension diversity gains. Due to its resource allocation flexibility, OFDMA has been widely used as a green air interface technology for the emerging broadband wireless access networks. This paper extensively addresses the integration of green OFDMA to the future air interface technologies, for instance:two-tier cellular, multi radio access technologies (RATs), FemtoCell, and relay networks. The main focus of the paper is to review and analyze the current OFDMA techniques to address the green resource allocation in multiuser diversity, where the critical constraints are the computational complexity, energy efficiency, and the sub-channel assignment. The future trend of OFDMA based networks will aim to maximize the energy efficiency of the exclusive channel assignment through a joint sub-channel and power allocation to accommodate high data traffic networks specially the relay based 5G cellular networks.     

同构与异构网中预测资源分配的性能

预测资源分配能利用蜂窝网络的残余资源大大提升吞吐量。本文面向视频点播等非实时业务,研究在使95%用户播放视频的卡顿时间小于其预期值时预测资源分配能够使网络支持的非实时业务请求到达率提升多少。为了研究预测窗长对预测资源分配性能的影响,考虑一种性能接近最优解的低复杂度双门限策略,分析了预测窗长度、残余带宽、预测方法、用户接入和小区间干扰对其性能的影响。研究结果表明,通过对所需各种信息设计合理的预测方法,预测误差对双门限策略影响很小;预测窗越长,该策略相对于传统非预测方法的吞吐量增益越大、但增速随窗长增加逐渐变缓;网络残余带宽的方差越大,双门限策略相对于非预测方法的吞吐量增益越大;基于残余带宽的接入方法在异构网络中性能远优于基于接收功率最大的用户接入,且网络负载越重、增益越大。      

Predictive and Adaptive Resource Reservation (PARR) for Cellular Networks

In cellular networks, a higher priority can, out of Quality of Service reasons, be given to ongoing connections than new call attempts. This can be achieved by having capacity in each cell (guard channels) exclusively reserved for arriving handover calls. In this paper, an advanced guard channel scheme, called Predictive and Adaptive Resource Reservation (PARR), will be introduced. The PARR scheme determines the amount of guard channels to allocate in a cell through subscriber movement predictions. The novelty of PARR is that both positioning and history-based measurements are used as input for the predictions. To improve the prediction accuracy, different data sets are applied for calls with different mobility and latest visited cells. Simulations will be used to show that significant performance improvements are obtainable with PARR.     

Survey on Ultra-Dense Networks (UDNs) and Applied Stochastic Geometry

The innovation of services offered by cellular networks gained the attention of researchers in the communication field. Thus, mobile industries deal with remarkable technological competition regarding service quality. The quality is determined by how superior, consistent, and quick a service is delivered to the user. Thus, mobility management is a basic factor as it deals with imperative information for managing user’s mobility. However, due to the expansion of connected devices, the users are set up densely which inspires the researcher for devising a novel mode switching model. This paper devises a novel mode switching model using the Naive Bayes classifier. Here, the switching of modes is based on certain quality parameters, like link utilization, bandwidth, delay, energy consumption, and signal strength. Whenever the network switches the communication link from cellular-mode to user-mode, it must maintain the quality parameters. For enhancing the performance of network mobility management, a mobility management model is devised in which user mobility is computed. Thus, the proposed method is essential for supporting improved user mobility during communication The proposed mode switching using Naïve Bayes classifier provides superior performance with a minimal delay of 0.164 s, maximal power of 58.786 bpm, maximal link utilization ratio of 0.727 and maximal throughput of 1,641,723 respectively.

     

Channel and Time Slot Allocation for Dense RFID Networks

This paper presents a constraint satisfaction approach to the reader collision problem in dense mode environments for static RFID networks. Our method assigns available channels and time slots to the RFID readers to increase the read rate while satisfying all the interference constraints. We model the problem with a hybrid frequency and time division multiplexing constraint satisfaction for reader anti-collision and assign workable channels and time slots to each reader using backtracking search algorithms. A depth-first-search based on backtracking was performed to find solutions to constraint satisfaction problems. Instead of performing an exhaustive search for the optimal result, we adopted good variable ordering heuristics as branching strategies where the search can be completed quickly. Thus, the search space is greatly reduced, and approximate solutions are found instantly. To find appropriate heuristics, we applied a problem classifying rule to determine the quality of the variable ordering heuristics. Computer simulations of the comparable performance of some variable ordering algorithms in the channel and time slot allocation for dense RFID networks are presented.     

基于中断概率的多输入多输出异构网络资源分配

为充分提高通信网络的频谱效率,异构网络采用多个微小基站同时在相同频段上支持用户数据传输的方式;为保障系统通信效能,需要有效控制不同数据接口上信号之间存在的相互干扰。提出了采用资源分配方法利用通信中断概率作为衡量不同微小基站数据分支通信性能的准则,通过将中断概率约束条件有效近似,从而使原复杂矩阵优化问题转换为基于标量的几何规划问题,进而极大地降低计算复杂度。数值仿真结果验证了所提方法的有效性。     

异构无线网络干扰效率最大顽健资源分配算法

针对能效提升、宏用户干扰减小的问题,该文研究了基于干扰效率最大的异构无线网络顽健资源分配算法.首先,考虑宏用户干扰约束、微蜂窝用户速率需求约束和最大发射功率约束,将资源优化问题建模为多变量非线性规划问题.其次,考虑有界信道不确定性模型,利用Dinkelbach辅助变量方法和连续凸近似方法结合对数变换方法,将原分式规划顽健资源分配问题转换为等价的确定性凸优化问题,并利用拉格朗日对偶算法获得解析解.理论分析了计算复杂度和参数不确定性对性能的影响.仿真结果表明该算法具有较好的干扰效率和鲁棒性.