Blind Multiple Frequency Offsets and Channel Estimation Using Particle Filter in Cooperative Transmission Wireless Networks

Precise estimation of Carrier Frequency Offsets (CFO) and channel gains is significant for coherent receiver in cooperative transmission (CT) wireless networks. In this paper, we introduce an unknown parameters estimation approach using a preamble, which is based on Bayesian estimation framework and implemented via Sequential Monte Carlo Method (SMC) to estimate multiple CFOs between transmitters and receivers and the channel gains. The desired data can be subsequently detected based on the estimated parameters. We derive a particle filtering algorithm (PFA) for the parameters estimation of distributed frequency-shift keying (FSK) CT wireless networks. The computer simulations for our proposed approach indicate the particle filtering algorithm is accurate and superior for the parameters estimation compared to the existing algorithms.