Channel estimation for AF relaying using ML and MAP

Channel state information is very important in cooperative communication for performance improvement or signal demodulation. Previous works on channel state information estimation mainly focus on least squares and minimum mean squared error estimators. In this work, several new maximum likelihood and maximum a posteriori estimators for the cooperative network are proposed. Two of them estimate the individual channel powers of different links using all pilots from the source node, while two of them estimate the individual channel gains of different links using pilots from the source node as well as the relay node. Numerical results show that the estimation error decreases when the signal-to-noise ratio increases or when the number of pilots increases. The estimators for the individual channel gains have normalized mean squared errors of less than 0.001 when the signal-to-noise ratio is 30 dB, and their bit error rate performances are very close to the perfect case. Importantly, channel estimation is performed at the destination only such that there is no extra complexity at the relay node.