SNR Estimation in Nakagami Fading with Diversity for Turbo Decoding

In this paper, we propose an online SNR estimation scheme for Nakagami-m fading channels with equal gain diversity combining. We derive the SNR estimates based on the statistical ratio of certain observables over a block of received data. An online SNR estimator for an AWGN channel has been derived by Summers and Wilson. Recently, we derived an SNR estimation scheme for Nakagami-m fading channels without diversity combining and used this estimate in the decoding of turbo codes. Now, we extend the work and solve the SNR estimation problem on Nakagami fading channels with L branch equal gain diversity combining. We use our SNR estimates in the iterative decoding of turbo codes on Rayleigh fading channels (m = 1) with 2-branch equal gain combining. We show that the turbo decoder performance using our SNR estimates is quite close (within 0.5 dB) to the performance using perfect knowledge of the SNR and the fade amplitudes.