An HMM approach to adaptive modulation and coding with multicodes for fading channels

In this paper, the impact of channel state estimation errors in a system employing AMC and multicodes is studied. The channel is modelled as a finite-state Markov chain (FSMC), and a hidden Markov model (HMM) formulation for the above problem is presented. An HMM filter is used and shown to yield an improved estimate of the channel state. Since the transition probabilities of the FSMC are required by the HMM filter, the problem of estimating these probabilities is also addressed. The performance of the proposed scheme is evaluated using computer simulation. The results show that the HMM filter provides a significant throughput improvement over the unfiltered case, especially when the channel state information (CSI) is quite noisy or the normalized Doppler rate (defined as the product of the Doppler rate of the channel and the transmission period) is small