基于用户QoS的网络资源分配策略研究

针对5G网络在网络资源分配存在的问题,从用户业务QoS等级角度提出基于贝叶斯模型的网络资源优化分配策略模型。在对用户业务QoS等级划分的基础上采用优先节点自主控制策略进行网络资源分配,通过构建贝叶斯模型评价网络状态并进行动态学习推理,指导网络优先节点的控制决策,实现优先节点对最佳信道的优先选择。实验表明,与基于信道容量的信道资源分配策略相比,本文的策略在网络总体容量和网络公平性上具有更好的表现,能够更好满足异构网络的实际需求。     

Prediction Mechanisms to Improve 5G Network User Allocation and Resource Management

As technology rapidly advances, the number of devices constantly communicating, transmitting and receiving data through the cellular networks keeps rising, posing an unprecedented load on them. Such an increase calls for establishing new methods to manage these devices as well as utilize the data produced by them to establish network architectures that can prevent cellular networks from overloading. To achieve the desired results, we need to optimally allocate network resources to existing users. Resource allocation has traditionally been considered an optimization problem where proposed solutions are hard to implement in real time, resulting in the use of inferior solutions with reduced produced performance. With the introduction of Machine Learning, we propose three mechanisms, intending to utilize network data to improve real time network performance. The first mechanism, a Decision Trees based mechanism aims to improve real time decision making by predicting the optimal matching of users and Base Stations. The second mechanism, a K-means based mechanism intends to tackle network congestion and ensure uninterrupted Quality of Service by predicting the optimal coordinates for placing Base Stations along the network based on traffic data. Finally, a Regression based mechanism manages to predict user movement along the network, resulting in improved resource management and reduced energy waste. These mechanisms can work cooperatively, essentially establishing a network architecture that utilizes prediction to efficiently allocate users and manage available resources.

     

Downlink Resource Allocation for NOMA-Based Hybrid Spectrum Access in Cognitive Network

To solve the contradiction between limited spectrum resources and increasing communication demand, this paper proposes a wireless resource allocation scheme based on the Deep Q Network(DQN) to allocate radio resources in a downlink multi-user cognitive radio(CR) network with slicing. Secondary users(SUs) are multiplexed using non-orthogonal multiple access(NOMA). The SUs use the hybrid spectrum access mode to improve the spectral efficiency(SE). Considering the demand for multiple services, the ...     

联合网络容量与认知用户满意度的资源分配优化方案

机会频谱接入技术是提高频谱利用率的一种有效方法,论文综合考虑信道的时变性、子信道空闲的检测可信度、认知网络与主网络间的互干扰等因素,建立了一种最大化认知网络有效容量和认知用户满意度的多目标优化模型,提出了一种联合资源分配和用户调度的PAUS算法.仿真结果表明,在主用户分布密度较低的环境中,该模型较最大化网络有效容量模型具有更高的用户满意度;较最大化认知用户满意度模型可获得更高的网络有效容量.     

基于能效优化的用户调度与资源分配算法

该文针对多用户OFDMA移动通信下行系统,提出了基于能效优化的用户调度和资源分配方法。所提方法在满足用户服务质量(QoS)要求的前提下,以最大化系统能效为准则建立优化模型,假设在发射端完全已知信道状态信息(CSI)的情况下,充分利用系统内在的分集,给出了用户调度与速率分配的策略,有效地提高了系统的能量使用效率。仿真结果表明,所提算法的性能在较低复杂度前提下能够取得接近最优算法的性能。     

基于簇用户设备分类和频谱共享分配的D2D网络覆盖分析

D2D通信是5G/B5G网络的关键技术之一,同时小小区的中心化部署形成了以父点为中心的簇.因此,文章构建了一个基于泊松点和泊松簇过程的网络模型,提出了一种基于簇的用户设备(User Equipment,UE)分类方案,并设计了联合正交与共享的频谱资源分配方案.基于簇的UEs分类方案将UEs分为不同类型的簇中心和簇边缘U...     

机会频谱接入中的最优资源分配

感知无线电(CR)网络中的机会频谱接入(OSA)可以有效提高频谱的利用率。研究了OSA中的信道分配和功率控制问题,通过将Overlay模式和Underlay模式相结合,求解了次用户吞吐量的最优化问题。在满足干扰温度约束的前提下,优化目标为所有次用户的带权速率之和,同时可提供QoS支持和最小速率保障。此优化问题可转化为二部图最大带权匹配问题并使用匈牙利方法进行求解。仿真结果显示,采用混合模式的信道分配和功率控制算法较单一模式可以获得更大的频谱资源利用率。     

基于用户需求的改进型CSGC频谱分配算法

针对基于图论着色模型的频谱分配算法目前未能很好解决用户自身需求这一重要问题,研究了基于用户需求的改进型颜色敏感的图论着色(Color Sensitive Graph Coloring,CSGC)频谱分配算法。该算法通过降低已满足需求用户的分配优先级,使系统未满足的带宽需求总量达到最小化,在保证原CSGC算法性能的同时,较好地实现了频谱分配与自身需求相匹配。仿真结果表明该算法是有效的。     

TelMED: Dynamic User Clustering Resource Allocation Technique for MooM Datasets Under Optimizing Telemedicine Network

Wireless Personal Communications - Tele-Medical data communication via general purpose networking protocols and techniques are major set-back under low line channels and adequate resources such as...     

Coordinated Caching and QoS-Aware Resource Allocation for Spectrum Sharing

Wireless Personal Communications - In these days, Wireless Sensor Networks (WSN) shows a huge impact on all appliances but one of the huge challenges in WSN is management of energy because the...     

一种面向系统公平性的频谱资源分配方法

针对认知无线网络频谱分配过程中公平性和全局优化问题,提出了一种面向网络系统公平的频谱分配方法,以系统公平性为目标函数,基于认知无线网络的特性及改进的量子遗传算法,将频谱分配模型中的分配矩阵与改进量子遗传算法中的可行解相对应,在保证系统公平性的同时,避免了局部最优现象的出现。仿真结果表明,该算法能更好地实现网络效益最大化和系统公平性。     

认知无线网络中基于用户需求和频谱聚合的动态频谱分配

认知无线电技术可以感知周围无线环境择机利用频谱空洞,从而达到提高频谱效率的目的.在认知无线电网络中,现有的频谱分配算法大部分是基于连续频谱分配并且没有考虑用户的需求.但是连续频谱分配会生成许多小于用户需求的频谱片段,这些频谱片段不能被充分利用,从而造成频谱浪费.本文基于图论提出了一种联合考虑用户需求和非连续频谱聚合的频谱分配算法.在该算法中,非连续的频谱片段被聚合以尽可能多地满足用户总需求,充分利用了小的频谱片段,避免了频谱浪费,从而提高频谱效率.     

可变带宽光网络路由与资源分配研究

文章针对灵活速率光信道数据单元（ODUflex）以及带宽可变光正交频分复用（OFDM）两种光网络分别提出路由与速率分配和路由与频谱分配课题。该课题将降低系统总能耗作为最优化目标,采用最优化软件ILOG CPLEX进行仿真,分别提出了相应的启发式算法,并将最优化结果同启发式算法的结果进行了性能上的比较与讨论。     

基于QoE的无线网络资源调度优化研究

为了提高用户观看高清视频、IPTV以及收听高质量音乐等业务时的满意度,采集用户所处情景的环境状况、设备状况和网络状况,分析不同情景对用户无线资源需求的影响,提出了基于QoE的无线资源调度优化算法。通过系统级仿真的结果表明,基于QoE的资源分配方法可以有效地提高用户的QoE。     

User Scheduling and Slicing Resource Allocation in Industrial Internet of Things

Heterogeneous base station deployment enables to provide high capacity and wide area coverage. Network slicing makes it possible to allocate wireless resource for heterogeneous services on demand. These two promising technologies contribute to the unprecedented service in 5G. We establish a multiservice heterogeneous network model, which aims to raise the transmission rate under the delay constraints for active control terminals, and optimize the energy efficiency for passive network terminals. ...     

多小区放大转发系统基于用户公平性的分布式资源分配

全频率复用的OFDMA系统中,小区间干扰严重降低了整个系统,特别是小区边缘的频谱效率。文中考虑了多小区放大转发系统下行链路的资源管理,须联合优化用户调度、载波和功率的分配,并设计了一个分布式的资源分配算法,算法基于本小区局部信息分步完成用户调度和功率控制,小区之间只需要较少的交互信息。仿真结果表明,文中算法较传统算法具有更好的系统性能。     

多无人机辅助通信中用户匹配与频谱资源联合优化方法

针对多无人机作为空中基站为地面设备提供临时服务的动态频谱分配问题,主要考虑无人机与地面用户匹配、子信道分配和功率分配三个方面。为了保证用户通信的公平性,在考虑频谱复用和共信道干扰的情况下,以最大化地面用户最小传输速率为目标,提出了一种用户匹配与频谱资源联合优化算法来解决上述混合整数非线性优化问题,通过聚类算法优化无人机与地面用户的最佳匹配,通过块坐标下降法迭代优化子信道分配和功率分配。仿真实验分析表明,提出的求解方法可以有效提升用户的传输速率,保证用户通信公平性。     

Power and Resource Allocation in Wireless Communication Network

Wireless Personal Communications - Present-day wireless methods are necessary to support a variety of higher-speed data communication facilities for its subscribers such as cloud-based video...     

Joint Access Point Selection and Resource Allocation in MEC-Assisted Network: A Reinforcement Learning Based Approach

A distributed reinforcement learning(RL)based resource management framework is proposed for a mobile edge computing(MEC) system with both latency-sensitive and latency-insensitive services.We investigate joint optimization of both computing and radio resources to achieve efficient on-demand matches of multi-dimensional resources and diverse requirements of users. A multi-objective integer programming problem is formulated by two subproblems, i.e., access point(AP) selection and subcarrier alloca...     

基于UWB的Ad hoc网络资源分配的研究

基于超宽带的Ad hoc网络的应用,提出了为多媒体业务提供QoS支持的自适应的资源分配方案。方案考虑了超宽带和Ad hoc网络的特性,同时也考虑了QoS的要求。提出的资源分配方案采取了业务区分和资源预留的方法,为链路分配网络资源（速率、功率）。根据吞吐量,丢包率和时延,对提出的方案做了详尽的性能评估,仿真结果证明了提出的方案的优越性。