Abstract
We consider capacity maximization algorithms for wireless networks with changing availabilities of spectrum. There are n sender-receiver pairs (called links) and k channels. We consider an iterative round-based scenario, where in each round the set of channels available to each link changes. Each link independently decides about access to one available channel in order to implement a successful transmission. Transmissions are subject to interference and noise, and we use a general approach based on affectance to define which attempts are successful. This includes recently popular interference models based on SINR.
Our main result is that efficient distributed algorithms from sleeping-expert regret learning can be used to obtain constant-factor approximations if channel availability is stochastic and independently distributed among links. In general, sublinear approximation factors cannot be obtained without the assumption of stochastic independence among links. A direct application of the no-external regret property is not sufficient to guarantee small approximation factors.
This work has been supported by DFG through Cluster of Excellence MMCI, UMIC Research Centre at RWTH Aachen University, and grant Ho 3831/3-1 and it has been supported by a fellowship within the Postdoc-Programme of the German Academic Exchange Service (DAAD).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andrews, M., Dinitz, M.: Maximizing capacity in arbitrary wireless networks in the SINR model: Complexity and game theory. In: Proc. 28th IEEE Conf. Computer Communications (INFOCOM), pp. 1332–1340 (2009)
Asgeirsson, E.I., Mitra, P.: On a game theoretic approach to capacity maximization in wireless networks. In: Proc. 30th IEEE Conf. Computer Communications (INFOCOM), pp. 3029–3037 (2011)
Blum, A.: Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain. Machine Learning 26(1), 5–23 (1997)
Dams, J., Hoefer, M., Kesselheim, T.: Jamming-resistant learning in wireless networks. CoRR, abs/1307.5290 (2013)
Dams, J., Hoefer, M., Kesselheim, T.: Scheduling in wireless networks with Rayleigh-fading interference. In: Proc. 24th Symp. Parallelism in Algorithms and Architectures (SPAA), pp. 327–335 (2012)
Dinitz, M.: Distributed algorithms for approximating wireless network capacity. In: Proc. 29th IEEE Conf. Computer Communications (INFOCOM), pp. 1397–1405 (2010)
Erlebach, T., Jansen, K., Seidel, E.: Polynomial-time approximation schemes for geometric graphs. SIAM J. Comput. 34(6), 1302–1323 (2005)
Freund, Y., Schapire, R., Singer, Y., Warmuth, M.: Using and combining predictors that specialize. In: Proc. 29th Symp. Theory of Computing (STOC), pp. 334–343 (1997)
Goussevskaia, O., Halldórsson, M., Wattenhofer, R., Welzl, E.: Capacity of arbitrary wireless networks. In: Proc. 28th IEEE Conf. Computer Communications (INFOCOM), pp. 1872–1880 (2009)
Halldórsson, M., Wattenhofer, R.: Wireless communication is in APX. In: Albers, S., Marchetti-Spaccamela, A., Matias, Y., Nikoletseas, S., Thomas, W. (eds.) ICALP 2009, Part I. LNCS, vol. 5555, pp. 525–536. Springer, Heidelberg (2009)
Hoefer, M., Kesselheim, T., Vöcking, B.: Approximation algorithms for secondary spectrum auctions. In: Proc. 23rd Symp. Parallelism in Algorithms and Architectures (SPAA), pp. 177–186 (2011)
Kanade, V., McMahan, B., Bryan, B.: Sleeping experts and bandits with stochastic action availability and adversarial rewards. J. Machine Learning Res. - Proc. Track 5, 272–279 (2009)
Kanade, V., Steinke, T.: Learning hurdles for sleeping experts. In: Proc. 3rd Symp. Innovations in Theoret. Computer Science (ITCS), pp. 11–18 (2012)
Kleinberg, R., Niculescu-Mizil, A., Sharma, Y.: Regret bounds for sleeping experts and bandits. Machine Learning 80(2-3), 245–272 (2010)
Moscibroda, T., Wattenhofer, R.: The complexity of connectivity in wireless networks. In: Proc. 25th IEEE Conf. Computer Communications (INFOCOM), pp. 1–13 (2006)
Nieberg, T., Hurink, J., Kern, W.: Approximation schemes for wireless networks. ACM Trans. Algorithms 4(4) (2008)
Schneider, J., Wattenhofer, R.: Coloring unstructured wireless multi-hop networks. In: Proc. 28th Symp. Principles of Distrib. Comput. (PODC), pp. 210–219 (2009)
Schneider, J., Wattenhofer, R.: An optimal maximal independent set algorithm for bounded-independence graphs. Distributed Computing 22(5-6), 349–361 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dams, J., Hoefer, M., Kesselheim, T. (2013). Sleeping Experts in Wireless Networks. In: Afek, Y. (eds) Distributed Computing. DISC 2013. Lecture Notes in Computer Science, vol 8205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41527-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-41527-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41526-5
Online ISBN: 978-3-642-41527-2
eBook Packages: Computer ScienceComputer Science (R0)