多业务移动通信系统中的呼叫接入控制

在多业务无线移动通信系统的呼叫接入控制中,不仅要考虑业务之间的优先级,同时也必须考虑切换呼叫的优先级。本文结合可移动边界算法和保护信道策略,提出了一种有效的多业务呼叫接入控制算法。为了方便地分析方案的性能,设计了一个二维的Markov链对方案的性能进行理论上的分析。通过数值分析结果表明,提出的算法同时保证了两种优先级,并提高了系统的性能。1     

分布式虚拟群小区中的接入控制

分布式多天线结构的虚拟群小区系统成为未来无线系统的一种解决方案。该系统具有灵活的组织结构,在增加频谱效率、提高通信质量上具有巨大潜力,并且能为各种新技术的采用提供广阔的发展空间。该文采用Markov链逼近的方法分析多天线情况下虚拟群小区系统的系统性能,对于基于干扰的接入控制策略下的系统阻塞率,切换失败率等给出了数值结果。研究结果表明分布式多天线系统有效地提高了上行系统性能。     

多载波蜂窝移动通信系统中的多业务切换算法

提出一种用于多载波蜂窝移动通信系统的子信道合并切换算法。采用多维Markov链对子信道合并切换算法进行系统建模分析,得到了呼叫阻塞率、切换阻塞率等关键系统性能参数的解析结果。与切换保护信道算法相比,子信道合并切换算法在对其他类型呼叫性能影响很小的前提下,改善了对带宽要求较高的业务的切换性能。该算法还可以与其他资源预留切换算法相结合,改善其性能。     

移动视频业务微移动性管理方案

提出了融合GMPLS和EPON技术各自优势的未来一体化接入网络架构.在此基础上,提出一个分布式的IP微移动性管理方案对移动视频业务提供服务品质保证.详细地阐述了其主要移动性管理过程,如移动节点注册及数据包转发路径的建立、不同类型切换操作的过程.在对用户进行移动性管理的过程中,相对于HMIP协议,提出的方案能够明显减少切换时延.     

带丢包Markov切换线性系统的状态估计问题研究

针对一类带丢包的Markov切换系统,提出一种含有双Markov切换参数的交互式多模型算法.该算法利用一个二态的Markov链对系统是否丢包进行建模,得到双Markov链系统,通过定义乘积集将两个Markov切换参数所对应的模型集进行融合,并给出单个模型集中各模型与乘积集中各模型的对应关系.在此基础上,以交互式多模型算法为框架,采用分层的方法,并利用一种新的最优估计算法对双Markov链系统进行滤波.仿真实验证明了该算法的有效性.     

一种基于速度感知的垂直切换算法

 未来的4G网络不是由单一的接入方式构成,而是采用不同的无线接入技术的多种网络的融合.不同网络接入技术间的切换称为垂直切换.本文提出了一种基于速度感知的垂直切换算法,不需确切的节点坐标和速度信息,利用接收信号强度的历史测量数据来表征节点运动速度的影响,提高对切换触发时机的准确判断.该算法可以自动调整以适应不同的节点运动速度,有效提高垂直切换的效率.仿真试验表明该算法能有效提高垂直切换的性能.     

联合稀疏信号恢复中的分布式路径协同优化算法

基于融合中心的多观测向量联合稀疏信号恢复算法需要将各个传感节点的数据传输到融合中心(融合中心可能远离各个节点),该方法在节点功率受限以及缺少融合中心的传感网络中并不适用。为了克服上述困难,本文提出了一种分布式路径协同优化算法来解决上述问题。由于采用了分布式计算和路径协同优化,各个传感节点只需与其近邻节点进行少量的数据传输,每个节点所消耗的传输数据功率和所承受的计算复杂度较低。实验结果表明,本文提出的算法的性能能够很好的逼近基于融合中心的联合稀疏信号恢复算法的性能。      

分布式媒体服务系统结合请求调度的接入控制研究

由于多媒体业务需要更大的带宽和更高的实时性,所以对服务系统和接入控制提出了更高的要求。文章针对分布式媒体服务系统提出了一种新颖的接入控制方法,与以往方法不同之处在于将请求调度融合在接入控制之中,从而提高了系统的服务性能。我们先为系统建立部分可观Markov决策过程(POMDP)模型,并将请求调度融合在决策中,然后使用基于观测的随机接入控制策略,通过策略梯度优化算法仿真求解模型的最优策略。仿真结果表明,与其他分布式接入控制方法相比,该文所提方法在有效利用系统资源的同时,提高了系统性能。     

Ad Hoc网络中一种具有优先控制的自适应协同MAC协议

ARQ(Auto Repeat reQuest)机制通常用来在传输失败后重传数据,但是在深衰落信道下,源节点多次重传有可能导致系统性能持续变坏,作为未来无线通信的重要技术,协同通信能够大大提高链路的可靠性,可以有效解决上述问题。本文针对Ad Hoc网络,提出了一种基于优先控制的自适应协同MAC(Media Access Control)协议。此协议采用ARQ机制,根据瞬时信道情况,自适应地选择信道质量更好的中继节点来重传数据,并且在优化的退避算法和中继选择算法的控制下,赋予中继节点相对源节点更高的优先级,协议中还在传统RTS/CTS分组交互基础上加入了新的握手交互机制,使得协议的性能进一步提升。本文还对经典二维Markov模型进行了改进,加入了协同机制并提出中继节点冲突模型,对所提协议进行了建模并推导出了吞吐量性能表达式。仿真结果表明,本文所提协议相对IEEE 80211 DCF协议和传统采用ARQ机制的协同MAC协议有着更好的性能。
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基于多种业务流资源共享的在线呼叫接入控制算法

本文针对多种业务类型提出了一种在线呼叫接入控制算法（OCAC）。OCAC算法是基于QoS业务量的服务质量要求和BE业务量的拥塞状态来动态地调整资源的分配,目的是更有效地利用资源并且在QoS业务流调整服务质量的同时,改善BE业务量的吞吐量。针对多跳的情况,本文描述了OCAC算法的两种实现方案,即基于每个节点和基于源节点的OCAC实现方案（ENS和SNS）。最后给出了仿真结果,并对本文提出的算法和静态接入控制算法的仿真结果进行了比较,进一步说明OCAC算法的有效性。     

Joint admission control algorithm based on load transfer in heterogeneous networks

A load-transfer-based joint admission control (LJAC) algorithm in heterogeneous networks was proposed.The access requirements of users were admitted based on load balancing,the dynamic load transfer of traffics in the overlapping coverage areas of heterogeneous networks were introduced,and the influence of such factors as the layout of heterogeneous networks and the vertical handoff was considered in the algorithm.The integrated system of heterogeneous networks was modeled as a multidimensional Markov chain,the steady-state probabilities were obtained and the quality of service (QoS) performance metrics were derived.Based on the Poisson point process theory,the upper bound of capacity of the heterogeneous networks satisfying QoS limitations was obtained.The admission control parameters of the integrated system of heterogeneous networks were optimized in order to maximize the resource utilization rate as well as guaranteeing the QoS of users.The simulation results demonstrate lower traffic blocking probability,lower failure probability of vertical handoff requirements,and larger system capacity gain can be achieved by using the proposed LJAC algorithm.     

TDD-CDMA系统中基于业务活动因子的功率控制算法

功率控制技术是CDMA系统克服远近效应、降低多址干扰的一项关键技术.TDD-CDMA系统所要提供的网络业务更多的是基于突发模式的通信,包括各种速率的实时和非实时业务.分析了突发模式业务的统计特性,根据马尔可夫调制贝努利过程,提出了一种基于业务活动因子的功率控制算法,并给出了相应的功率控制模型.通过建立功能模型,验证和评估了提出的功率控制算法.仿真结果表明,这一新的功率控制算法能够更准确地估计干扰信号强度,加快功率控制的收敛,从而提高能量效率和系统容量.     

马尔可夫参数自适应IFIMM算法研究

针对新息滤波交互式多模型(IFIMM)算法中切换过程模型概率滞后的问题,提出了模型概率转移矩阵马尔可夫参数自适应的新息滤波多模型算法(AMP-IFIMM),该方法采用后验信息修正不准确的先验信息,自适应的调整马尔可夫转移矩阵的参数.切换时刻较多地遗忘非匹配模型的信息,放大匹配模型的信息,在保证滤波精度的同时,大大提高了模型间切换速度.将该算法应用到CA,CV两模型组合导航系统取得了良好的效果.     

A call admission control for service differentiation and fairness management in WDM grooming networks

We investigate a call admission control (CAC) mechanism for providing fairness control and service differentiation in a WDM network with grooming capabilities. A WDM grooming network can handle different classes of traffic streams which differ in their bandwidth requirements. We assume that for each class, call interarrival and holding times are exponentially distributed. Using a Markov Decision Process approach, an optimal CAC policy is derived for providing fairness in the network. The Policy Iteration algorithm is used to numerically compute the optimal policy. Furthermore, we propose a heuristic decomposition algorithm with lower computational complexity and good performance. Simulation results compare the performance of our proposed policy with those of Complete Sharing and Complete Partitioning policies. Comparisons show that our proposed policy provides the best performance in most cases. Although this approach is motivated by WDM networks, it may be deployed to determine the optimal resource allocation in many problems in wireless and wired telecommunications systems.     

Markov game for autonomic joint radio resource management in a multi-operator scenario

This article addresses the autonomy of joint radio resource management (JRRM) between heterogeneous radio access technologies (RATs) owned by multiple operators.By modeling the inter-operator competition as a general-sum Markov game, correlated-Q learning (CE-Q) is introduced to generate the operators' pricing and admission policies at the correlated equilibrium autonomically.The heterogeneity in terms of coverage, service suitability, and cell capacity amongst different RATs are considered in the input state space, which is generalized using multi-layer feed-forward neural networks for less memory requirement.Simulation results indicate that the proposed algorithm can produce rational JRRM polices for each network under different load conditions through the autonomic learning process.Such policies guide the traffic toward an optimized distribution and improved resource utilization, which results in the highest network profits and lowest blocking probability compared to other self-learning algorithms.     

Towards a Sustainable Green Design for Next-Generation Networks

Recently distributed real-time database systems are intended to manage large volumes of dispersed data. To develop distributed real-time data processing, a reality and stay competitive well defined protocols and algorithms must be required to access and manipulate the data. An admission control policy is a major task to access real-time data which has become a challenging task due to random arrival of user requests and transaction timing constraints. This paper proposes an optimal admission control policy based on deep reinforcement algorithm and memetic algorithm which can efficiently handle the load balancing problem without affecting the Quality of Service (QoS) parameters. A Markov decision process (MDP) is formulated for admission control problem, which provides an optimized solution for dynamic resource sharing. The possible solutions for MDP problem are obtained by using reinforcement learning and linear programming with an average reward. The deep reinforcement learning algorithm reformulates the arrived requests from different users and admits only the needed request, which improves the number of sessions of the system. Then we frame the load balancing problem as a dynamic and stochastic assignment problem and obtain optimal control policies using memetic algorithm. Therefore proposed admission control problem is changed to memetic logic in such a way that session corresponds to individual elements of the initial chromosome. The performance of proposed optimal admission control policy is compared with other approaches through simulation and it depicts that the proposed system outperforms the other techniques in terms of throughput, execution time and miss ratio which leads to better QoS.

     

Optimal SMDP‐Based Connection Admission Control Mechanism in Cognitive Radio Sensor Networks

Traffic management is a highly beneficial mechanism for satisfying quality‐of‐service requirements and overcoming the resource scarcity problems in networks. This paper introduces an optimal connection admission control mechanism to decrease the packet loss ratio and end‐to‐end delay in cognitive radio sensor networks (CRSNs). This mechanism admits data flows based on the value of information sent by the sensor nodes, the network state, and the estimated required resources of the data flows. The number of required channels of each data flow is estimated using a proposed formula that is inspired by a graph coloring approach. The proposed admission control mechanism is formulated as a semi‐Markov decision process and a linear programming problem is derived to obtain the optimal admission control policy for obtaining the maximum reward. Simulation results demonstrate that the proposed mechanism outperforms a recently proposed admission control mechanism in CRSNs.     

基于马尔可夫过程的水下运动目标启发式搜索

在搜索海域存在障碍的情况下,该文将启发式搜索算法应用于对水下运动目标的搜索,研究了基于马尔可夫过程的运动目标启发式搜索算法。建立了马尔可夫水下运动目标规避模型、搜索器启发式搜索过程模型和马尔可夫水下运动目标的搜索概率模型。该算法由已知的目标先验位置分布信息不断地对目标的运动位置进行估计、更新,以获得精确的目标后验分布,再利用启发函数得到下一步的最佳搜索节点。仿真分析表明:在对水下运动目标搜索时,搜索器能有效地规避障碍,提高搜索效率,有助于研究水下目标的优化搜索。     

Supporting service differentiation in wireless packet networksusing distributed control

This paper investigates differentiated services in wireless packet networks using a fully distributed approach that supports service differentiation, radio monitoring, and admission control. While our proposal is generally applicable to distributed wireless access schemes, we design, implement, and evaluate our framework within the context of existing wireless technology. Service differentiation is based on the IEEE 802.11 distributed coordination function (DCF) originally designed to support best-effort data services. We analyze the delay experienced by a mobile host implementing the IEEE 802.11 DCF and derive a closed-form formula. We then extend the DCF to provide service differentiation for delay-sensitive and best-effort traffic based on the results from the analysis. Two distributed estimation algorithms are proposed. These algorithms are evaluated using simulation, analysis, and experimentation. A virtual MAC (VMAC) algorithm passively monitors the radio channel and estimates locally achievable service levels. The VMAC estimates key MAC level statistics related to service quality such as delay, delay variation, packet collision, and packet loss. We show the efficiency of the VMAC algorithm through simulation and consider significantly overlapping cells and highly bursty traffic mixes. In addition, we implement and evaluate the VMAC in an experimental differentiated services wireless testbed. A virtual source (VS) algorithm utilizes the VMAC to estimate application-level service quality. The VS allows application parameters to be tuned in response to dynamic channel conditions based on “virtual delay curves.” We demonstrate through simulation that when these distributed victual algorithms are applied to the admission control of the radio channel then a globally stable state can be maintained without the need for complex centralized radio resource management