Simultaneous Representation and Separation for Multiple Interference
Allied with Approximation Message Passing
Abstract
Broadband reliable communication is a competitive 5G technology for
cognitive communication scenarios, but meanwhile introduces multiform
interference to existing broadband transform domain communication system
(TDCS) transmission. In order to facilitate the improvement of the
anti-jamming performance for the coexistence of diverse interference and
TDCS signals in wireless heterogeneous networks, it is important to
separated and eliminate various interference to TDCS systems. In this
paper, a novel sparse learning method-based cognitive transformation
framework of interference separation is formulated for accurate
interference recovery, which can be efficiently solved by iteratively
learning the prior sparse probability distribution of the interference
support. To further improve the separation accuracy and iterative
convergence, the principal component analysis and Bayesian perspective
in orthogonal base learning are exploited to singly recover the multiple
interference and TDCS signals. Moreover, utilizing different sparsity
states of spectrum analysis, the proposed novel interference separation
algorithm is extended to simultaneous separation based on state evolving
of approximation message passing, which iteratively learns the belief
propagation posteriors and keeps shrunk by iterative shrinkage
threshold. Simulation results demonstrate that the proposed methods are
effective in separating and recovering the sparse diversities of
interference to TDCS systems, and significantly outperform the
state-of-the-art methods.