|This thesis is concerned with dynamic spectrum access in cognitive radio networks.
The main objective is designing online learning and access policies which maximize
the total throughput of the secondary users in a cognitive radio network. As the first
approach, we consider the auction-based formulation in design of dynamic spectrum
access mechanisms where it is assumed that primary channels are heterogeneous with
distinct availability statistics unknown to each secondary user (SU). Considering this
approach, we first apply a unit demand (UD) auction which is called DGS (Demange-
Gale-Sotomayor) auction. Applying the DGS auction, we explore the instantaneous
link condition of each SU for its throughput maximization. To tackle the issues of this
UD auction, we propose a learning-based unit demand (LBUD) auction. Our proposed
auction mechanism incorporates a distributed learning of the primary channels
into the auction mechanism to explore both primary channel availability statistics
and instantaneous link gains of the SUs for their throughput maximization. This new
mechanism substantially improves the communication overhead and also the SUs’
throughputs where the primary channels have dissimilar availability statistics. The
proposed LBUD auction preserves the strong property of the UD auction, i.e., it is
dominant strategy incentive compatible. To improve convergence speed of the iterative
procedure used in the auction, we further propose an adaptive price increment
algorithm. Simulation results show the effectiveness of the proposed LBUD auction
mechanism in terms of the throughput gain.
As our second approach, we model the problem of designing decentralized dynamic
spectrum access policies as a decentralized multi-armed bandit(DMAB) problem.
Using DMAB formulation, we first propose a truly decentralized online learning
and access policy where in addition to channel availability statistics, the secondary
user population is also assumed to be unknown to the SUs. To reduce collision events
at different learning stages, we then improve an existing access policy by exploiting a
”perceived population” by each secondary user. We also develop a distributed learning
and access policy which is effective in a wide range of primary channel conditions.
As our last approach, we investigate designing of a decentralized online learning
and channel access in a cognitive radio network with M secondary users. We formulate
the distributed channel selection problem in a cognitive network as a strategic
game which is proved to be an exact potential game. Applying stochastic learning
automata, we propose an adaptive decentralized access policy where each SU probabilistically
selects one of the M-best channels to access. Based on collision events, we
update the channel selection probability. In our proposed adaptive policy, two underlying
distributed learning algorithms are utilized in parallel: i) Learning from sensing
history on the primary channel availability, and ii) Learning from collision history on
channel selections among SUs to avoid further collision. Simulation results show the
effectiveness of our proposed adaptive policy in various distributions of mean channel
availabilities across primary channels, as compared with other existing policies.