Compressed spectrum sensing based on correlation of spectrum occupation states between sensing periods

In order to reduce sampling costs and computational complexity in the signal reconstruction process of existing compressed spectrum sensing (CSS), we propose a novel two-step compressed spectrum sensing (TS-CSS) scheme exploiting correlation of occupation states between sensing periods. At the first step of TS-CSS, we detect busy sub-channels of last sensing period to find the sub-channels that are still busy in current sensing period. In this way, part of supports of received signal is known and the remainder of the signal is a sparser signal than the original. Then the second step adjusts number of samples collected in the compressive sampling process according to number of unknown supports. By doing so, overall sampling rate and computational complexity are minimized. Compared with traditional one step compressed spectrum sensing (OS-CSS) scheme, our TS-CSS needs fewer samples and lower computations to yield an accurate sensing outcome. Valid simulation is conducted in the work.