Iterative Demodulation and Decoding with Blind Channel Estimator for LoRa Modulation

This paper proposes an iterative detection and decoding (IDD) scheme with a blind channel estimator in the physical layer of long range widearea network (LoRaWAN). The proposed scheme exchanges log-likelihood ratios (LLRs) as soft decision values between the LoRa demodulator and the Hamming decoder to improve the signal detection capability while obtaining the iterative gain. In the typical LoRa demodulation using hard decision without IDD, the received signal is detected by the binary values, i.e. "0" or "1"; and thus, the demodulation and decoding processes are simplified. However, the resulting rounding error occurs the mutual information loss, leading to non-negligible performance degradation. In order to cope with this issue, it is vital to capture the stochastic behavior of the input and output values of the demodulator and decoder. The LLR based on the stochastic signal model enables to improve the error correction capability of the decoder and enhance the iterative gain. Finally, computer simulation results explicitly demonstrate the efficacy of the proposed method in terms of bit error rate (BER) performance.