异构无线网络的端到端重配置技术研究

E2R（端到端重配置）技术利用SDR（软件无线电）提供的重配置能力,结合先进的资源管理机制和灵活的空中接口实现技术,实现对异构环境的灵活适应和对异构无线资源的有效利用。本文论述了E2R技术的产生背景和基本概念,对重配置研究中的关键技术（体系结构、资源管理和空中接口实现）进行了系统总结,最后就重配置未来研究的重点提出了一些看法。     

端到端重配置技术研究

端到端重配置技术起源于软件无线电,软件无线电技术实现了终端的多模式支持功能,并实现了软件从空中接口的下载.端到端重配置技术利用软件无线电提供的重配置能力,构建起以可重配置的终端、基站等网元为主体的体系结构,结合先进的动态网络规划、灵活频谱管理和联合无线资源管理技术,实现对重配置能力和异构无线资源的有效利用,保证用户的无缝业务体验.本文论述了端到端重配置技术的产生背景、基本概念和研究现状,对重配置研究中的主要问题和关键技术(包括架构、规划和管理等方面的内容)进行了系统的总结,最后就重配置未来研究的重点提出了自己的看法.     

基于认知的重配置无线网络技术

端到端重配置技术旨在解决异构无线网络融合和无线资源的最优化利用,随着重配置技术研究的发展和认知理论研究的不断深入,使得未来具有认知能力的重配置无线网络的实现成为可能。目前,对端到端重配置技术的研究包括重配置无线网络体系结构及实现向重配置无线网络的演进过程中的关键技术。     

端到端重配置技术综述

端到端重配置(E2R)技术起源于软件无线电(SDR)技术,利用SDR终端和基站等可重配置实体为基础定义网络架构,结合先进的资源管理机制和灵活的空中接口实现技术,实现了对异构环境的灵活适应和对异构无线资源的有效利用。E2R技术研究中有3个关键点,包括E2R的体系结构、E2R的资源管理和E2R的空中接口实现,已取得较大成果。E2R技术作为一种具有高适变能力的技术,是使异构无线系统从目前的隔离态走向互通与协同、实现网络融合的最具发展潜质的无线网络技术。目前,E2R的研究已成为未来无线通信网络领域的关键课题之一。     

可重配置无线网络中联合无线资源管理研究

对可重配置网络进行资源管理的研究是当前无线领域内的一个热点问题,而其中联合无线资源管理机制和算法是影响网络协同性能的最关键因素之一.本文介绍了可重配置系统的联合无线资源管理研究领域的最新成果和进展,并对研究热点和未来发展方向进行了分析展望.     

B3G无线网络中一种提供QoS保证的动态资源管理技术

B3G无线网络具有一个全IP的核心网，并且融合了多种无线接入技术实现网间的垂直切换和无缝漫游。随着用户业务种类的增加，网络环境变得愈加复杂，这给资源管理带来了巨大的技术挑战。本文在介绍B3G无线网络架构、端到端的QoS机制的基础上，提出一种能在核心网中实现的动态资源管理方案，在方案中引入了资源池的概念。     

异构无线网络中基于多网协同优化的资源管理技术研究

在移动信息高速发展的今天,为了满足不同用户的需求,人们已经研究卡发出了多种类型的无线通信系统。就目前的发展情况来看,由于2G和3G都存在一定的客观限制因素,且现在正在向着4G网络不断迈进;因此,如何进行多种异构网络的协同优化来有效利用网络资源,也是主要的研究对象之一;基于此,本文将简要分析多网下同优化的资源管理及其相关管理技术。     

认知无线网络中基于微观经济学的动态频谱管理算法

该文提出了认知网络中基于微观经济学的动态频谱管理机制,实现异构无线网络中频谱资源的动态分配,从而最大化频谱资源的利用率并提高运营商的收益.文中引入了频谱资源的经济价值因素以保证运营商间频谱交易的合理性.此外,为了保证系统的性能,文中提出了一种基于地理信息系统的空分模型,并设计了干扰抑制技术以限制动态频谱管理带来的干扰.博弈论是一种能够有效解决自主分布式无线资源优化的算法,该文将其引入,为不同运营商提供双赢的频谱交易策略.仿真结果表明,该频谱管理机制提高了频谱利用率以及运营商的收益,并有效地抑制了系统间的干扰.     

下一代异构IP网络的关键技术

异构无线网络融合是移动通信系统发展的重要趋势，即融合的网络将统一于全IP。不同网络的实现技术、传输机理、组织方式、控制机制存在巨大差异，这对异构网络中诸如移动性管理、联合无线资源管理、端到端的QoS保证等关键问题提出了极大的挑战。文章就异构网络的关键技术发展进行了分析和讨论。     

下一代异构IP网络的关键技术

异构无线网络融合是移动通信系统发展的重要趋势,即融合的网络将统一于全IP.不同网络的实现技术、传输机理、组织方式、控制机制存在巨大差异,这对异构网络中诸如移动性管理、联合无线资源管理、端到端的QoS保证等关键问题提出了极大的挑战.文章就异构网络的关键技术发展进行了分析和讨论.     

Adaptive Resource Management Platform for Reconfigurable Networks

Users’ expectations towards technology, in terms of quality, service availability and accessibility are ever increasing. Aligned with this, the wireless world is rapidly moving towards the next generation of systems, featuring cooperating and reconfiguring capabilities for coexisting (and upcoming) Radio Access Technologies (RATs), so that to improve connectivity and reduce costs. In this respect, conventional planning and management techniques ought to be replaced by advanced schemes that consider multidimensional characteristics, increased complexity and high speeds. To this effect, means are needed capable to support scalability and to cater for advanced service features, provided to users at high rates and cost-effectively. This article provides a scheme to optimize resource management in future systems, by describing a platform that accommodates engineering mechanisms that deal with dynamic, demand driven planning and managing of spectrum and radio resources in reconfigurable networks. To do so, it first discusses the fundamentals and the approach followed in the proposed architecture and then investigates the basic functional modules. The architecture is validated through a set of use-cases that exemplify the operational applicability and efficiency in a wide range of communication scenarios.

多代理系统在异构无线网络资源管理中的应用

在网络向着高速化、宽带化、泛在化发展的过程中,出现了多种多样的无线通信系统,给网络带来了丰富的异构性。然而异构网络缺少互通机制,缺乏有效的协调,具有系统间的干扰、单一网络业务提供能力有限、频谱资源稀缺等问题,网络资源利用率低下。为使网络资源能够高效使用,联合资源管理必不可少。而传统的集中式资源管理架构不能满足异构网络环境的需求,分布式基于多代理的网络资源管理架构将是未来异构无线网络联合资源管理的主要形式。     

Traffic Characteristics Based Dynamic Radio Resource Management in Heterogeneous Wireless Networks

The traffic with tidal phenomenon in Heterogeneous Wireless Networks （HWNs） has radically increased the complexity of radio resource management and its performance analysis. In this paper, a Simplified Dynamic Hierarchy Resource Management （SDHRM） algorithm exploiting the resources dynami- cally and intelligently is proposed with the consideration of tidal traffic. In network-level resource allocation, the proposed algorithm first adopts wavelet neural network to forecast the traffic of each sub-area and then allocates the resources to those sub-areas to maximise the network utility. In connection-level net- work selection, based on the above resource allocation and the pre-defined QoS require- ment, three typical network selection policies are provided to assign traffic flow to the most appropriate network. Furthermore, based on multidimensional Markov model, we analyse the performance of SDHRM in HWNs with heavy tailed traffic. Numerical results show that our theoretical values coincide with the simulation results and the SDHRM can im- prove the resource utilization.     

分层异构网络无线资源管理技术探讨

随着通信技术的不断发展,用户随时随地获得高质量无线业务体验的需求造成网络的分层异构覆盖特征越来越明显.对分层异构网络中的无线资源管理关键技术进行了探讨,利用无线资源管理技术可以极大地提高分层异构无线网络环境下的传输速率并节约能量.     

Performance Analysis of Dynamic Spectrum Allocation in Heterogeneous Wireless Networks

Increasing convergence among heterogeneous radio networks is expected to be a key feature of future ubiquitous services. The convergence of radio networks in combination with dynamic spectrum allocation (DSA) could be a beneficial means to solve the growing demand for radio spectrum. DSA might enhance the spectrum utilization of involved radio networks to comply with user requirements for high‐quality multimedia services. This paper proposes a simple spectrum allocation algorithm and presents an analytical model of dynamic spectrum resource allocation between two networks using a 4‐D Markov chain. We argue that there may exist a break‐even point for choosing whether or not to adopt DSA in a system. We point out certain circumstances where DSA is not a viable alternative. We also discuss the performance of DSA against the degree of resource sharing using the proposed analytical model and simulations. The presented analytical model is not restricted to DSA, and can be applied to a general resource sharing study.     

A Framework for JRRM with Resource Reservation and Multiservice Provisioning in Heterogeneous Networks

Inter-working and convergence of heterogeneous wireless networks are paving the way to scenarios in which end users will be capable of using simultaneously services through different Radio Access Technologies (RATs), by means of reconfigurable mobile terminals and different network elements. In order to exploit the potential of these heterogeneous networks scenarios, optimal RAT selection and resource utilization mechanisms are required. As a result, the heterogeneous networks are introducing a new dimension to the Radio Resource Management (RRM) problem, so that new algorithms dealing with the dissimilarities and complementarities of the multiple RATs from a joint perspective have to be considered. In this sense, this paper proposes a Joint Radio Resource Management (JRRM) strategy in a multi-RAT, multicellular and multiservice scenario. An approach based on Fuzzy Neural methodology is presented. Firstly, the way how the proposed Fuzzy Neural framework deals with the multiservice allocation in a heterogeneous scenario is presented. A reinforcement learning algorithm based on neural networks allows guaranteeing a multidimensional QoS focusing on those QoS requirements which mainly affect the user perception of the service. In addition to this, the performances obtained by the Fuzzy Neural JRRM for both real-time and non real-time services, are compared to the ones offered by alternative JRRM strategies. Secondly, special attention is paid to real-time services and to mechanisms to improve their performances. An approach based on predicting future JRRM decisions and on accordingly reserving radio resources for handoff calls is presented. Simulation results will show improvements in terms of both new connection blocking and handoff call dropping probabilities. Finally, the full set of results provides the sufficient insight into the problem to allow stating that the present Fuzzy Neural framework can be a firm candidate for JRRM.

基于BP和RBF神经网络的C-V2X无线资源管理

为了提升蜂窝车联网（Cellular Vehicle-to-Everything,C-V2X）资源复用的有效性和降低终端间的干扰,提出通过神经网络对未来时刻车流量的预测辅助无线资源管理方案。依据车载单元（On Board Unit,OBU）与路侧单元（Road Side Unit,RSU）间的车联网消息,获取RSU覆盖区域内各时刻的车流情况,分别采用BP（Back Propagation）神经网络和RBF（Radial Basis Function）神经网络进行短时交通流预测。RSU根据预测结果进行自适应分簇,簇间复用相同资源,簇内进行资源池的划分,RSU覆盖内的OBU在划分的资源池中选择发送资源,从而减少终端间的干扰,并保证热点区域车辆拥有更多的资源。仿真结果表明,在道路交通拥塞的场景下,所提方案的数据包接收率较标准中的方案提升14%,较典型文献方案提升10%,保证了通信的可靠性。     

Wireless IP and Its Challenges for the Heterogeneous Environment

The tremendous momentum toward building a mobile wireless Internet using the Internet Protocol (IP) has thrust many challenges upon the technology developers and the operators. These challenges encompass the 2.5G, 3G, and future solutions and standards. Some of the key issues deal with supporting IP traffic across different wireless and MAC protocols, end-to-end QoS and mobility, traffic engineering and scalability, protection/restoration, operations and management, authentication/privacy/security, billing, and content and context aware networking. This article addresses the drivers, the issues, and solutions (wherever possible) in the above areas, and concludes with a summarization of the challenges that lie ahead.     

一种5G异构云无线接入网络的D2D资源分配算法

为了解决设备对设备(Device-to-Device,D2D)资源共享带来的信号干扰问题,提出了一种5G异构云无线接入网络的D2D通信资源分配算法.在保证服务质量的前提下,将宏用户设备的频谱资源分配给D2D和中继用户设备,并且把资源分配问题看作一对一的匹配博弈.采用婚姻匹配理论,得到初始的匹配方案.在初始匹配的基础上,提出了一种遵循卡尔多-希克斯(Kaldor-Hicks)原则的资源交换策略,以提高系统的吞吐量.仿真结果表明,该资源分配算法收敛较快,与现有方案相比,能使系统吞吐量提升15％以上,能给系统用户带来约10％的增益,并且有较强抗信道干扰能力.