10,543 research outputs found
Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret
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. 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
regret in distributed learning and access. We first consider the scenario when
the number of secondary users is known to the policy, and prove that the total
regret is logarithmic in the number of transmission slots. Our distributed
learning and access policy achieves order-optimal regret by comparing to an
asymptotic 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 through feedback. We propose a policy in this
scenario whose asymptotic sum regret which grows slightly faster than
logarithmic in the number of transmission slots.Comment: Submitted to IEEE JSAC on Advances in Cognitive Radio Networking and
Communications, Dec. 2009, Revised May 201
Coalition Formation Game for Cooperative Cognitive Radio Using Gibbs Sampling
This paper considers a cognitive radio network in which each secondary user
selects a primary user to assist in order to get a chance of accessing the
primary user channel. Thus, each group of secondary users assisting the same
primary user forms a coaltion. Within each coalition, sequential relaying is
employed, and a relay ordering algorithm is used to make use of the relays in
an efficient manner. It is required then to find the optimal sets of secondary
users assisting each primary user such that the sum of their rates is
maximized. The problem is formulated as a coalition formation game, and a Gibbs
Sampling based algorithm is used to find the optimal coalition structure.Comment: 7 pages, 2 figure
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