Adaptive multicoset sampling for wideband spectrum sensing based on POMDP framework

In this paper, we consider the problem of opportunistically accessing a wide range of frequency band in which multiple subbands may be occupied. A major obstacle of utilizing such wideband spectrum is that it is either infeasible or too expansive to perform Nyquist sampling on the wideband signal. In this paper, we propose an adaptive sensing scheme based on a sub-Nyquist sampling method called multicoset sampling, which is similar to the polyphase implementation of Nyquist sampling, but requires less A/D converters. In contrast to the traditional sub-Nyquist sampling approaches where all subbands are considered to design the sampling filters, we develop a method that adaptively selects part of wideband spectrum to do sub-Nyquist sampling, by exploiting its statistical properties. Therefore, the computational overhead for reconstructing the wideband signal from the sub-Nyquist samples can be significantly reduced. Simulation results are provided to demonstrate the effectiveness of the proposed adaptive wideband sensing method.