Performance aspects of data broadcast in wireless networks with user retrials

  • Authors:
  • Natalija Vlajic;Charalambos D. Charalambous;Dimitrios Makrakis

  • Affiliations:
  • Broadband Wireless and Internetworking Research Laboratory, School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, K1N 6N5 Canada;Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus and Broadband Wireless and Internetworking Research Laboratory, School of Information Technology and E ...;Broadband Wireless and Internetworking Research Laboratory, School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, K1N 6N5 Canada

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2004

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Abstract

The User retrial phenomenon and its significant impact on network performance in unicast wireless systems are known and relatively well studied in the literature. However, there have been no previous studies on the impact of the user retrial phenomenon on other types of wireless networks. The objective of this paper is to extend the analysis of the user retrial phenomenon to wireless systems which, in addition to unicast service, also support a data broadcast service.This objective is realized by defining several performance measures appropriate for the analysis of hybrid unicast-broadcast systems in the presence of users' retrials. Subsequently, we derive the exact mathematical expression for each of the measures. Based on these expressions, we prove the existence of a single broadcast scheduling scheme, which ensures optimal system performance, with respect to the given set of proposed measures, the system's throughput, and the grade and quality of service. We also take a closer look at the class of hybrid unicast-broadcast systems with autonomous estimation of data item popularities, and we elaborate on the major challenges associated with such systems. Finally, we evaluate our theoretical expressions through simulation, and we discuss their robustness with respect to moderate deviations in the underlying model.