Cognitive radio has more extensive application in recent years, and it may operate in complex wireless environmental condition such as communication systems with time-variant multipath flat-fading channel. As an essential technology for cognitive radio, most existing spectrum sensing methods are designed for time-invariant propagation channel; thus, it could be extremely difficult to achieve acceptable sensing performance when we apply them to deal with time-variant multipath fading channel. In order to overcome this obstacle, we design a novel spectrum sensing method in this investigation. Firstly, a dynamic state-space model is proposed in which two different hidden Markov models are employed to abstract the evolution of primary user state and time-variant multipath flat-fading channel gain. Based on the dynamic state-space model, the spectrum sensing problem is formulated as blind estimation problem. Relying on maximum a posteriori probability criterion and particle filtering technology, a joint estimation algorithm of the time-variant channel gain and primary user state is presented. Experimental simulations demonstrate the superior performance of our presented sensing scheme, which could be used potentially in realistic cognitive radio systems.
In REF , a new method has been proposed to improve the sensing performance over a time-variant multipath flat-fading (TVMFF) channel.
Blind spectrum sensing for cognitive radio over time-variant multipath flat-fading channels
EURASIP Journal on Wireless Communications and Networking
Computer Science