【作者】 黄琳；

【导师】 汪小燕；

【作者基本信息】 华中科技大学 ， 通信与信息系统， 2007， 硕士

【摘要】 低轨卫星移动通信系统(LEO MSSs)作为构建全球无缝通信系统的重要组成部分正在飞速的发展。无线资源管理(Radio Resource Management)负责空中接口资源的利用,保证移动用户的QoS需求,维持系统预规划的覆盖区,为系统提供大容量,是低轨卫星通信系统的研究热点。无线资源管理策略中信道分配和包调度策略主要负责无线频率资源的分配,而有限的无线频谱资源与不断扩大的用户需求之间的矛盾越来越严重,因此信道分配和包调度策略是未来无线资源管理的重点研究方向。目前无线网络正在从支持单一话音业务到综合传输包括实时流在内的多种数据业务。多媒体业务大都具有非常严格的服务质量(Quality of Service,QoS)要求和较高的带宽需求。在进行资源分配时,应充分考虑业务的QoS,系统有限的频率资源,系统的性能等因素.在进行无线资源分配时如何在这些因素之间取得均衡的问题可以归纳为组合优化问题,已有很多学者对此进行了研究。遗传算法具有搜索能力强,鲁棒性好的特点,通常用于求解离散的NP组合优化问题。在此利用遗传算法求解信道分配和包调度过程中的资源分配问题。基于以上研究背景,本文分别提出了一种适用于低轨卫星通信系统的基于遗传算法的自适应带宽分配策略和包调度策略。该信道分配算法的基本思想是降低正在通话的多媒体业务的带宽,将这部分带宽收回以接入切换呼叫。在降低带宽时,设计了一种针对本问题的改进的遗传算法来决定降低哪些呼叫的带宽,以及降低带宽的数量。本文提出的分组调度策略综合考虑了信道状态,缓冲区中等待发送的分组数,业务的QoS等动态和静态因素,将这些参数作为遗传算法的适应度函数参数。每次循环调度时,根据遗传算法计算的结果来决定资源的分配。通过仿真验证,该信道分配策略能够在不降低新呼叫的阻塞率的情况下,有效地降低切换呼叫的切换掉话率;包调度策略实时性较强,能够有效地利用系统的资源,保证各业务的QoS需求。同时遗传算法的收敛率也比较高。更多还原

【Abstract】 Low earth orbit mobile satellite communication systems (LEO MSSs)acting as an important component of global seamless communication systems, are developing fast. Radio Resource Management takes charge of the utilization of aero- interface resources, guarantees the QoS demand of mobile subscribers, sustains the pre planning systematic overlay region, provides large capacity for the system. Therefore it is a research hotspot in low-orbiting satellite communication system. There among channel allocation and packet scheduling strategy is mainly in charge of the allocation of wireless frequency, so the contradiction between the limited resource of wireless spectrum and the constant expanding user requirement is more and more serious. Therefore channel allocation and packet scheduling strategy is an important research direction of wireless resource management in the future.For the moment wireless network is sustaining from simplex voice service to comprehensive transmission including diversified data traffic such as real-time stream. Multimedia services mostly demand extremely strict QoS and superior bandwidth. The factors such as professional QoS, the limited frequency resource of the system, the performance of the system, and so on, should be considered comprehensively in the channel allocation and packet scheduling strategy. The problem of how to keep balance among these factors when allocating channel could be induced to nonlinear optimization problems, and there have been many scholars doing researches in it. Genetic algorithm bores with the feature of powerful searching capability and fine robustness, and is usually used for solving the problem of diverging NP combinatorial optimization problems. At this point, we made use of genetic algorithm to solve the resource allocation problem in the channel allocation and packet scheduling procedure. Based on the above research background, this paper proposes a GA(genetic algorithm)Based Adaptive Bandwidth Allocation Scheme and packet schedule strategy for low earth orbit satellite communication systems. The basic principle of the bandwidth allocation scheme is to decrease the bandwidth grades of the ongoing calls and withdrawal partial bandwidth for handover calls, then the handover calls dropping probability decrease. In the process of decreasing bandwidth, an improved genetic algorithm is designed to resolve this problem and to decide which and how much bandwidth should be decreased. The packet schedule strategy proposed by this paper focuses on static and dynamic parameters of services, such as channel state, the number of packets which will be sent in the buffer and QoS of services, and uses the parameters as fitness function of the genetic algorithm. The packet scheduling strategy assigns of the resources according to the GA calculation results in every schedule circulation.The simulation results show that the bandwidth allocation scheme can decrease dropping probability, while not decreasing the new call blocking probability. Packet schedule strategy can effectively make use of the system resources and guarantee the QoS of services, and has real time performance. The convergence efficiency of the genetic algorithm is comparatively high.更多还原