User-Level Performance of Channel-Aware Scheduling Algorithms in Wireless Data Networks

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User-Level Performance of Channel-Aware Scheduling Algorithms in Wireless Data Networks (2003)

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by Sem Borst

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216 - 9 self

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@MISC{Borst03user-levelperformance,
    author = {Sem Borst},
    title = {User-Level Performance of Channel-Aware Scheduling Algorithms in Wireless Data Networks},
    year = {2003}
}

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Abstract

Channel-aware scheduling strategies, such as the Proportional Fair algorithm for the CDMA 1xEV-DO system, provide an effective mechanism for improving throughput performance in wireless data networks by exploiting channel fluctuations. The performance of channel-aware scheduling algorithms has mostly been explored at the packet level for a static user population, often assuming infinite backlogs. In the present paper, we focus on the performance at the flow level in a dynamic setting with random finite-size service demands. We show that in certain cases the user-level performance may be evaluated by means of a multi-class Processor-Sharing model where the total service rate varies with the total number of users. The latter model provides explicit formulas for the distribution of the number of active users of the various classes, the mean response times, the blocking probabilities, and the mean throughput. In addition we show that, in the presence of channel variations, greedy, myopic strategies which maximize throughput in a static scenario, may result in sub-optimal throughput performance for a dynamic user configuration and cause potential instability effects.

Keyphrases

channel-aware scheduling algorithm user-level performance wireless data network flow level channel fluctuation explicit formula dynamic setting proportional fair algorithm multi-class processor-sharing model cdma 1xev-do system throughput performance myopic strategy packet level blocking probability total service rate varies static scenario active user various class static user population infinite backlog channel-aware scheduling strategy effective mechanism total number dynamic user configuration certain case random finite-size service demand mean response time latter model mean throughput channel variation present paper sub-optimal throughput performance potential instability effect

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