Dynamic power allocation and routing for satellite and wireless networks with time varying channels
Dynamic power allocation and routing for satellite and wireless networks with time varying channels
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发表时间：
2003
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通讯作者：
M. Neely
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作者：
M. Neely
卫星和无线网络在取决于衰减条件、功率分配决策和信道间干扰的时变信道上操作。为了将这些系统可靠地集成到高速数据网络中并满足日益增长的高吞吐量和低延迟需求，有必要开发充分利用每个网络元件物理层能力的高效网络层策略。在这篇论文中，我们发展了网络层容量的概念，并描述了实现无线链路和自适应传输速率的一般网络的功率分配和路由算法的容量。延迟，吞吐量最优性，公平性，实现的复杂性和鲁棒性随时间变化的信道条件和不断变化的用户需求的基本问题进行了讨论。分析是在数据包级别进行的，并充分考虑了系统中的随机动态，潜在的突发性，到达过程。这项研究的应用程序进行检查的卫星网络和ad-hoc无线网络的特定情况下。事实上，在第3章中，我们考虑了多波束卫星下行链路，并开发了一个动态功率分配算法，分配功率的每个链路的反应队列积压和当前的信道条件。该算法的操作没有知识的到达流量或信道统计，并实现最大吞吐量，同时保持平均延迟保证。在第4章的最后，这样的卫星被认为是一个交联的收集和卫星分离原则的发展，证明了联合最优控制可以实现单独的算法的下行链路和交叉。Ad-hoc无线网络将在第6章中得到特别关注。针对具有N个用户的移动的ad-hoc网络，建立了一个简单的小区划分模型，推导出了容量和时延的精确表达式。端到端延迟被证明是O（N），因此随着网络规模的增加而变大。为了减少延迟，在多条路径上发送冗余分组信息的传输协议被开发，并示出以减少吞吐量为代价提供O（N）延迟。建立了一个基本的速率-延迟权衡曲线，给出了实现O（N）和O（N）延迟的协议，并在该曲线的不同边界点上运行。在第四章和第五章中，我们考虑一般时变网络的最优控制。开发了一种跨层策略，尽可能稳定网络，并在输入超过容量时公平地决定服务哪些数据。该策略被解耦为用于动态流控制、功率分配和路由的单独算法，并且允许每个用户独立于其他用户的行为做出贪婪决策。所示的组合策略产生的数据速率是任意接近的最佳公平的工作点，实现时，所有的网络控制器进行协调，并有完美的知识，未来的事件。接近该公平操作点的成本是网络所服务的数据的端到端延迟增加。（副本可从麻省理工学院图书馆，RM。14-0551，剑桥，MA 02139-4307。电话：617-253-5668;传真：617-253-1690。）
Satellite and wireless networks operate over time varying channels that depend on attenuation conditions, power allocation decisions, and inter-channel interference. In order to reliably integrate these systems into a high speed data network and meet the increasing demand for high throughput and low delay, it is necessary to develop efficient network layer strategies that fully utilize the physical layer capabilities of each network element. In this thesis, we develop the notion of network layer capacity and describe capacity achieving power allocation and routing algorithms for general networks with wireless links and adaptive transmission rates. Fundamental issues of delay, throughput optimality, fairness, implementation complexity, and robustness to time varying channel conditions and changing user demands are discussed. Analysis is performed at the packet level and fully considers the queueing dynamics in systems with arbitrary, potentially bursty, arrival processes. Applications of this research are examined for the specific cases of satellite networks and ad-hoc wireless networks. Indeed, in Chapter 3 we consider a multi-beam satellite downlink and develop a dynamic power allocation algorithm that allocates power to each link in reaction to queue backlog and current channel conditions. The algorithm operates without knowledge of the arriving traffic or channel statistics, and is shown to achieve maximum throughput while maintaining average delay guarantees. At the end of Chapter 4, a crosslinked collection of such satellites is considered and a satellite separation principle is developed, demonstrating that joint optimal control can be implemented with separate algorithms for the downlinks and crosslinks. Ad-hoc wireless networks are given special attention in Chapter 6. A simple cell-partitioned model for a mobile ad-hoc network with N users is constructed, and exact expressions for capacity and delay are derived. End-to-end delay is shown to be O(N), and hence grows large as the size of the network is increased. To reduce delay, a transmission protocol which sends redundant packet information over multiple paths is developed and shown to provide O( N ) delay at the cost of reducing throughput. A fundamental rate-delay tradeoff curve is established, and the given protocols for achieving O(N) and O( N ) delay are shown to operate on distinct boundary points of this curve. In Chapters 4 and 5 we consider optimal control for a general time-varying network. A cross-layer strategy is developed that stabilizes the network whenever possible, and makes fair decisions about which data to serve when inputs exceed capacity. The strategy is decoupled into separate algorithms for dynamic flow control, power allocation, and routing, and allows for each user to make greedy decisions independent of the actions of others. The combined strategy is shown to yield data rates that are arbitrarily close to the optimally fair operating point that is achieved when all network controllers are coordinated and have perfect knowledge of future events. The cost of approaching this fair operating point is an end-to-end delay increase for data that is served by the network. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)