Joint optimization of placement and bandwidth reservation for relays in IEEE 802.16j mobile multihop networks

  • Authors:

  • Dusit Niyato;Ekram Hossain;Dong In Kim;Zhu Han

  • Affiliations:

  • School of Computer Engineering, Nanyang Technological University, Singapore;Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea;Electrical and Computer Engineering Department, University of Houston, Houston, TX

  • Venue:
  • ICC'09 Proceedings of the 2009 IEEE international conference on Communications
  • Year:
  • 2009

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Abstract

Mobile multihop relay (MMR) networks based on the IEEE 802.16j standard are able to extend the service area as well as improve the performance of mobile WiMAX networks. We present an optimization framework for jointly optimizing the placement and bandwidth reservation for a relay station in an MMR network. The objective of this framework is to maximize utility of the MMR network service provider. The decision on the placement of the relay corresponds to finding the best location for the relay station under uncertainty about the number of active users in the extended service area of the MMR network. This uncertainty could be due to random connection initiation and termination by the users or due to the random arrivals and departures of the mobile subscriber stations in the extended service area. However, this decision on relay placement may not achieve the highest utility when the users dynamically adapt their decisions on whether to transmit directly to the base station or transmit through the relay station. In this scenario, optimal decision on bandwidth reservation by the relay station needs to be made (over a relatively shorter period of time) which takes the dynamics of users' decision into account. The placement of the relay station (over a relatively longer period of time) can then be optimized based on the optimal bandwidth reservation. A stochastic programming formulation and a Markov decision process formulation are used to obtain the long-term and short-term optimization solutions, respectively.