A unified approach to QoS-guaranteed scheduling for channel-adaptive wireless networks

Xin Wang, Georgios B. Giannakis, Antonio G. Marques

Research output: Contribution to journalArticle

165 Scopus citations

Abstract

Scheduling amounts to allocating optimally channel, rate and power resources to multiple connections with diverse quality-of-service (QoS) requirements. It constitutes a throughput-critical task at the medium access control layer of today's wireless networks that has been tackled by seemingly unrelated information-theoretic and protocol design approaches. Capitalizing on convex optimization and stochastic approximation tools, the present paper develops a unified framework for channel-aware QoS-guaranteed scheduling protocols for use in adaptive wireless networks whereby multiple terminals are linked through orthogonal fading channels to an access point, and transmissions are (opportunistically) adjusted to the intended channel. The unification encompasses downlink and uplink with time-division or frequency-division duplex operation; full and quantized channel state information comprising a few bits communicated over a limited-rate feedback channel; different types of traffic (best effort, non-real-time, real-time); uniform and optimal power loading; off-line optimal scheduling schemes benchmarking fundamentally achievable rate limits; as well as on-line scheduling algorithms capable of dynamically learning the intended channel statistics and converging to the optimal benchmarks from any initial value. The take-home message offers an important cross-layer design guideline: judiciously developed, yet surprisingly simple, channel-adaptive, on-line schedulers can approach information-theoretic rate limits with QoS guarantees.

Original languageEnglish (US)
Article number4383376
Pages (from-to)2410-2431
Number of pages22
JournalProceedings of the IEEE
Volume95
Issue number12
DOIs
StatePublished - Dec 2007

Keywords

  • Adaptive modulation and coding
  • Convex optimization
  • Quality of service (QoS)
  • Scheduling and resource allocation
  • Stochastic approximation

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