Stability region adaptation using transmission power control for transport capacity optimization in IEEE 802.16 wireless mesh networks

Authors:
G. Vejarano;D. Wang;J. McNair
Affiliations:
Wireless and Mobile Systems Laboratory, Department of Electrical and Computer Engineering, University of Florida, United States;Wireless and Mobile Systems Laboratory, Department of Electrical and Computer Engineering, University of Florida, United States;Wireless and Mobile Systems Laboratory, Department of Electrical and Computer Engineering, University of Florida, United States
Venue:
Computer Networks: The International Journal of Computer and Telecommunications Networking
Year:
2011

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Abstract

Transmission power control in multihop wireless networks is a challenging problem due to the effects that different node transmission powers have across the layers of the protocol stack. In this paper, we study the problem of transmission power control in IEEE 802.16 mesh networks with distributed scheduling. We consider the effects of transmission power control on the link-scheduling performance when a set of end-to-end flows established in the network are given. The problem is approached by means of the stability region of the link-scheduling policy. Specifically, the stability region is adapted using transmission-power control to the paths of the flows. This adaptation enables the flows to support higher levels of data traffic under lower levels of end-to-end delay. To the best of our knowledge, the approach of stability-region-based transmission power control has not been studied before. We propose a heuristic transmission-power-control algorithm for solving the problem of adapting the stability region to the flows. It is shown, by means of simulation, that the algorithm outperforms the transmission power control based on spatial reuse, which is a widely used approach. Also, it is shown that the solution of the algorithm has performance close to the optimal solution for moderate-sized networks, i.e., networks with no more than 25 nodes and 25 flows.