Abstract
It is proposed that a dynamic Bayesian network (DBN) is used to perform turbo equalization in a system transmitting information over a Rayleigh fading multipath channel. The DBN turbo equalizer (DBN-TE) is modeled on a single directed acyclic graph by relaxing the Markov assumption and allowing weak connections to past and future states. Its complexity is exponential in encoder constraint length and approximately linear in the channel memory length. Results show that the performance of the DBN-TE closely matches that of a traditional turbo equalizer that uses a maximum a posteriori equalizer and decoder pair. The DBN-TE achieves full convergence and near-optimal performance after small number of iterations.
Highlights
Turbo equalization has its origin in the Turbo Principle, first proposed in [1] where it was applied to the iterative decoding of concatenated convolutional codes
Turbo equalization becomes exceedingly complex in terms of the number of computations, due to the high computational complexity of the maximum a posteriori (MAP) equalizer and MAP decoder most often used in a turbo equalizer
In order to demonstrate the speed of convergence of the dynamic Bayesian network (DBN)-TE, Figure 20 shows the performance of the DBN turbo equalizer (DBN-TE) for different numbers of iterations (Z), where the channel impulse response (IR) length is L = 5 at a mobile speed of 20 km/h using eight frequency hops
Summary
Turbo equalization has its origin in the Turbo Principle, first proposed in [1] where it was applied to the iterative decoding of concatenated convolutional codes. The proposed DBN-TE addresses this problem by modeling the turbo equalizer as a quasi-DAG by applying a transformation to the ISI-corrupted received symbols, in order to ensure that there will always exist a dominant connection between the hidden variable (codeword symbols) and the observed variable (received symbols) at a given time instant, and only weak connections between the observed variable at the current time instant and hidden variables at past and future time instances With this transformation the DBN-TE achieves full convergence and is able to perform near-optimal inference in a small number of iterations, in order to estimate the coded sequence c, and the uncoded sequence s. The coded bits are interleaved with a random interleaver and passes through a multipath channel with an IR of length L
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