Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published April 2011 | Submitted
Journal Article Open

Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret

Abstract

The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement among the secondary users and sensing and access decisions are undertaken by them in a completely distributed manner. We propose policies for distributed learning and access which achieve order-optimal cognitive system throughput (number of successful secondary transmissions) under self play, i.e., when implemented at all the secondary users. Equivalently, our policies minimize the sum regret in distributed learning and access, which is the loss in secondary throughput due to learning and distributed access. For the scenario when the number of secondary users is known to the policy, we prove that the total regret is logarithmic in the number of transmission slots. This policy achieves order-optimal regret based on a logarithmic lower bound for regret under any uniformly-good learning and access policy. We then consider the case when the number of secondary users is fixed but unknown, and is estimated at each user through feedback. We propose a policy whose sum regret grows only slightly faster than logarithmic in the number of transmission slots.

Additional Information

© 2011 IEEE. Manuscript received 1 December 2009; revised 4 June 2010. During the stint of this work, the first author was supported by MURI through AFOSR Grant FA9550-06-1-0324. The second and the third authors are supported in part through NSF grant CCF-0835706. Parts of this paper were presented at [1].

Attached Files

Submitted - 1006.1673v1.pdf

Files

1006.1673v1.pdf
Files (319.8 kB)
Name Size Download all
md5:245f43e85eba84862ebb249a4c51bd60
319.8 kB Preview Download

Additional details

Created:
August 19, 2023
Modified:
October 17, 2023