Finite State Markov Model of Rayleigh Fading Channels with Time-varying Noise Power

The model of fading channel is very important for channel prediction, which appears in many modern wireless communication systems, such as adaptive wireless communication, cognitive radio and so on. In order to adapt to the application of wireless communication, in which there power of the noise is time-varying, a novel finite-state Markov model (FSMM) representing Rayleigh fading channels was proposed. The range of the received signal amplitude is partitioned into a finite number of intervals, which are associated with the states of the Markov model. The relationship between the amplitude thresholds, the states transition probabilities and the distribution probabilities of the states are derived in theory. An equal-probability channel model, which is realized easily, is proposed. The theoretical analysis and the Monte Carlo simulation results illustrate that the model based on signal-noise-rate is invalid when the power of the noise is time-varying, while the model proposed in this paper fits with the Rayleigh fading channel very well.