Maximum Likelihood Estimation of Trellis Encoder and Modulator Transition Utilizing HMM for Adaptive Channel Coding and Modulation Technique

In order to achieve adaptive channel coding and adaptive modulation, the main causes of degradation to system performance are the decoder selection error and modulator estimation error. The utilization of supplementary information, in an estimation system utilizing channel estimation results, blind modulation estimation, and blind encoder estimation using several decoders information and encoder transitions have been considered to overcome these two problems. There are however many issues in these methods, such as the channel estimation difference between transmitter and receiver, computational complexity and the assumption of perfect Channel State Information (CSI). Our proposal, on the other hand, decreases decoder and demodulator selection error using a Hidden-Markov Model (HMM). In order to estimate the switching patterns of the encoder and modulator, our proposed system selects the maximum likelihood encoder and modulator transition patterns using both encoder and modulator transition probability based on the HMM obtained by CSI and also Decoder and Demodulator Selection Error probabilities. Therefore, the decoder and demodulation results can be achieved efficiently without any restraint on the pattern of switching encoder and modulation. key words: hidden-Markov model, maximum likelihood encoder and modulator transition, decoder and demodulator selection error