A low complexity algorithm and architecture design for channel estimation in turbo codes

The iterative maximum a posteriori (MAP) probability algorithm decoding of turbo codes requires knowledge of the channel signal-to-noise ratio (SNR). The mismatch of channel SNR degrades the correction ability of the turbo decoding. In this paper, a low complexity channel estimation, which combines the hard decision rules of the early termination techniques, is proposed. Our algorithm takes the advantage of reducing the iterations, and easy implementation on channel estimation. Based on our simulation results, the proposed method can practically estimate the reliable channel SNR to refine the correction performance after several frames.