SNR estimation in generalized fading channels and its application to turbo decoding

In this paper, we propose an online SNR estimation scheme for generalized fading channels. We derive the SNR estimate based on a statistical ratio of observables over a block of data, when the channel undergoes Nakagami fading. An online SNR estimator for AWGN channels has been derived recently by Summers and Wilson. Our SNR estimation scheme in this paper is for a general Nakagami fading channel, where the SNR estimates in Rayleigh fading and AWGN can be obtained as special cases corresponding to the Nakagami parameter m = 1 and m = 1, respectively. As an example, we use our SNR estimate in the iterative decoding of turbo codes on both i.i.d and correlated Rayleigh fading channels. We show that the turbo decoder performance on Rayleigh fading channels using our SNR estimate is close to the performance using perfect knowledge of the fade amplitudes and the SNR.