基于稳定性方法的随机信道移动通信控制

R. Buche, H. Kushner
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引用次数: 0

摘要

考虑具有单个发射机的移动通信系统的前向链路,并通过随机变化的信道连接到K个目的地。数据以某种随机方式到达并排队直到传输。时间被分成小的调度间隔。新一代的系统可以估计信道(例如,通过导频信号)并使用该信息进行调度。问题是如何以依赖于队列和信道状态的方式将发射机功率和时间分配给各个队列,以保证稳定性和良好的运行。决策是在调度间隔的开始时做出的。在非常弱的条件下,采用随机稳定性方法既保证系统稳定,又能得到适当的时间和功率分配。李雅普诺夫函数的选择允许选择有效的性能标准。由此产生的控制非常合理,并且允许在公平性和队列长度之间进行一系列权衡。涵盖了许多当前感兴趣的计划。例如,可以控制或不控制比特间隔和每比特功率的CDMA,可以控制分配时间、每比特功率和比特间隔的TDMA。通道状态过程可能被很好地估计或只被部分地了解。所有的基本因素都被纳入到一个“平均速率”函数中,因此结果涵盖了许多不同的系统。由于问题的非马尔可夫性质,我们使用了摄动随机李雅普诺夫函数方法,该方法很好地适应了这类问题。这种方法简单、有效、新颖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Control of mobile communications with randomly-varying channels via stability methods
Consider the forward link of a mobile communications system with a single transmitter and connecting to K destinations via randomly varying channels. Data arrives in some random way and is queued until transmitted. Time is divided into small scheduling intervals. The new generation of systems can estimate the channel (e.g, via pilot signals) and use this information for scheduling. The issues are the allocation of transmitter power and time to the various queues in a queue and channel-state dependent way to assure stability and good operation. The decisions are made at the beginning of the scheduling intervals. Stochastic stability methods are used both to assure that the system is stable and to get appropriate time and power allocations, under very weak conditions. The choice of Liapunov function allows a choice of the effective performance criteria. The resulting controls are quite reasonable and allow a range of tradeoffs between fairness and queue lengths. Many schemes of current interest are covered. For example, CDMA with or without control over the bit interval and power per bit, and TDMA with control over the time allocated, power per bit, and bit interval. The channel-state process might be well-estimated or only partially known. All essential factors are incorporated into a "mean rate" function, so that the results cover many different systems. Because of the non-Markovian nature of the problem, we use the perturbed Stochastic Liapunov function method, which is well adapted to such problems. The method is simple, effective, and new.
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