Feedback quantization strategies for multiuser diversity systems

Pak Tao Lau, Frank R. Kschischang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)


In a system utilizing multiuser diversity, regular feedback of channel-quality predictions to the base station is required for each user. Typically, the measure of channel quality must be quantized at each mobile station before it can be sent back. In this paper, we present two distributed scalar quantization schemes that optimize two different performance criteria: a) the minimization of the probability Peof incorrectly identifying the user with the best channel quality and b) maximization of the resulting throughput R. For a typical Rayleigh-fading system with 30 users per sector, numerical optimization results show that the Peand R realized by the uniform quantization strategy with 16 quantization levels for each user can be achieved by only three quantization levels using the two proposed strategies. A practical approximation of the proposed schemes is studied and is shown to provide near-optimal performance for both performance criteria as the number of quantization levels becomes large.
Original languageEnglish
Pages (from-to)1386-1400
Number of pages15
JournalIEEE Transactions on Information Theory
Issue number4
Publication statusPublished - 1 Apr 2007
Externally publishedYes


  • Channel fading
  • Distributed quantization
  • Multiuser diversity
  • Order statistics

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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