Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networks
Abstract
We consider distributed optimization over orthogonal collision channels in spatial random access networks. Users are spatially distributed and each user is in the interference range of a few other users. Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability. We study both the non-cooperative and cooperative settings. In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users. In the latter, the goal is to achieve proportionally fair rates among users. Simple distributed learning algorithms are developed to solve these problems. The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.
Cite
@article{arxiv.1507.05664,
title = {Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networks},
author = {Kobi Cohen and Angelia Nedich and R. Srikant},
journal= {arXiv preprint arXiv:1507.05664},
year = {2016}
}
Comments
40 pages, 6 figures, accepted for publication in the IEEE Transactions on Automatic Control, part of this work was presented at the 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), 2015