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Decentralized Neyman-Pearson Test with Belief Propagation for Peer-to-Peer Collaborative Spectrum Sensing
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Cooperative Spectrum Sensing based on the Limiting Eigenvalue Ratio Distribution in Wishart Matrices
No full textRecent advances in random matrix theory have
spurred the adoption of eigenvalue-based detection techniques for
cooperative spectrum sensing in cognitive radio. These techniques
use the ratio between the largest and the smallest eigenvalues
of the received signal covariance matrix to infer the presence
or absence of the primary signal. The results derived so far
are based on asymptotical assumptions, due to the difficulties
in characterizing the exact eigenvalues ratio distribution. By
exploiting a recent result on the limiting distribution of the
smallest eigenvalue in complex Wishart matrices, in this paper we
derive an expression for the limiting eigenvalue ratio distribution,
which turns out to be much more accurate than the previous
approximations also in the non-asymptotical region. This result
is then applied to calculate the decision sensing threshold as
a function of a target probability of false alarm. Numerical
simulations show that the proposed detection rule provides a
substantial improvement compared to the other eigenvalue-based
algorithms
Uniformly Reweighted Belief Propagation for Estimation and Detection in Wireless Networks
Full text linkIn this paper, we propose a new inference algorithm, suitable for distributed processing over wireless networks. The algorithm, called uniformly reweighted belief propagation (URW-BP), combines the local nature of belief propagation with the improved performance of tree-reweighted belief propagation (TRW-BP) in graphs with cycles. It reduces the degrees of freedom in the latter algorithm to a single scalar variable, the uniform edge appearance probability ρ. We provide a variational interpretation of URW-BP, give insights into good choices of ρ, develop an extension to higher-order potentials, and complement our work with numerical performance results on three inference problems in wireless communication systems: spectrum sensing in cognitive radio, cooperative positioning, and decoding of a low-density parity-check (LDPC) code
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