Dynamic channel-sensitive scheduling algorithms for wireless data throughput optimization

Sem Borst*, Phil Whiting

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


The relative delay tolerance of data applications, together with bursty traffic characteristics, opens up the possibility for scheduling transmissions so as to optimize throughput. A particularly attractive approach in fading environments is to exploit the variations in the channel conditions and transmit to the user with the currently "best" channel. We show that the "best" user may be identified as the maximum-rate user when feasible rates are weighed with some appropriately determined coefficients. Interpreting the coefficients as shadow prices, or reward values, the optimal strategy may thus be viewed as a revenue-based policy, which always assigns the transmission slot to the user yielding the maximum revenue. Calculating the optimal-revenue vector directly is a formidable task, requiring detailed information on the channel statistics. Instead, we present adaptive algorithms for determining the optimal-revenue vector online in an iterative fashion, without the need for explicit knowledge of the channel behavior. Starting from an arbitrary initial vector, the algorithms iteratively adjust the reward values to compensate for observed deviations from the target throughput ratios. The algorithms are validated through extensive numerical experiments. Besides verifying long-run convergence, we also examine the transient performance, in particular the rate of convergence to the optimal-revenue vector. The results show that the target throughput ratios are tightly maintained and that the algorithms are able to track sudden changes in the channel conditions or throughput targets well.

Original languageEnglish
Pages (from-to)569-587
Number of pages19
JournalIEEE Transactions on Vehicular Technology
Issue number3
Publication statusPublished - May 2003
Externally publishedYes


  • High data rate
  • Scheduling
  • Stochastic control
  • Throughput optimization


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