Joint detection scheme for spectrum sensing over time-variant flat fading channels

As the application scope of cognitive radio grows continuously, time-variant flat fading (TVFF) channels become common in practical spectrum sensing scenarios. Unfortunately, most existing spectrum sensing methods which are designed for time-invariant propagation channels could hardly obtain good performance when they operate in realistic TVFF channels. To combat this difficulty, in this investigation the authors design a promising spectrum sensing method. Firstly, a novel dynamic state-space model is proposed in which a two-state Markov chain is employed to abstract the evolution of primary user states and a finite-state Markov channel model is utilised to characterise the TVFF channel. Secondly, based on the maximum a posteriori probability criteria and the particle filtering mechanic, a joint estimation algorithm of the time-dependent fading channel gain and the state of primary user is presented. Experimental simulations verify the performance superiority of the authors presented joint detection scheme, which could be properly applied to spectrum sensing in realistic TVFF channels.