Wideband spectrum sensing in unknown white Gaussian noise

The spectrum sensing of a wideband frequency range is studied by dividing it into multiple subbands. It is assumed that in each subband either a primary user (PU) is active or absent in a additive white Gaussian noise environment with an unknown variance. It is also assumed that at least a minimum given number of subbands are vacant of PUs. In this multiple interrelated hypothesis testing problem, the noise variance is estimated and a generalised likelihood ratio detector is proposed to identify possible spectrum holes at a secondary user (SU). Provided that it is known that a specific PU can occupy a subset of subbands simultaneously, a grouping algorithm which allows faster spectrum sensing is proposed. The collaboration of multiple SUs can also be considered in order to enhance the detection performance. The collaborative algorithms are compared in terms of the required exchange information among SUs in some collaboration methods. The simulation results show that the proposed detector outperforms the energy detector in the presence of noise variance mismatch above 2.3 dB. Some involved trade-offs in the spectrum sensing using the proposed detector are discussed.