Sammendrag
We consider the problem of a user operating within
a Cognitive Radio Network (CRN) which involves N channels
each associated with a Primary User (PU). The problem consists
of allocating a channel which, at any given time instant is not
being used by a PU, to a Secondary User (SU). Within our study,
we assume that a SU is allowed to perform “channel switching”,
i.e., to choose an alternate channel S times (where S +1 ≤ N) if
the previous choice does not lead to a channel which is vacant.
The paper first presents a formal probabilistic model for the
problem itself, referred to as the Formal Secondary Channel
Selection (FSCS) problem, and the characteristics of the FSCS
are then analyzed. Thereafter, the paper proposes a fascinating
solution to the FSCS problem by invoking the recently devised
Bayesian Learning Automaton (BLA). The crucial advantage of
the BLA is that unlike traditional Learning Automata (LA), it
does not involve an action probability vector, but rather relies
on “sampling” as per the a posteriori Bayesian estimates of the
channel occupation probabilities. However, rather than utilize the
BLA in the form that was earlier proposed, we shall extend it to
the so-called Switchable Bayesian Learning Automaton (SBLA),
which, indeed, attains the optimal solution in the overall composite
action space. Apart from proposing the solution, the paper also
contains detailed simulation results which demonstrate the power
of the solution proposed.
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