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In recent years many sparse estimation methods, also known as compressed sensing, have been developed for channel identification problems in digital communications. However, all these methods presume the transmitted sequence of symbols to be known at the receiver, i.e. in form of a training sequence. We consider blind identification of the channel based on maximum likelihood (ML) estimation via the...
In recent years many sparse estimation methods, also known as compressed sensing, have been developed for channel identification problems in digital communications. However, all these methods presume the transmitted sequence of symbols to be known at the receiver, i.e. in form of a training sequence. We consider blind identification of the channel based on maximum likelihood (ML) estimation via the...
We consider trellis-based algorithms for data estimation in digital communication systems. We present a general framework which includes approximate Viterbi algorithms like the M-algorithm and the T-algorithm as well as particle filtering algorithms. The algorithmic concepts are very close, since the difference is simply the choice of the norm in the weights calculation. The general framework yields...
We discuss approximate maximum-likelihood methods for blind identification and deconvolution. These algorithms are based on particle approximation versions of the expectation-maximization (EM) algorithm. We consider three different methods which differ in the way the posterior distribution of the symbols is computed. The first algorithm is a particle approximation method of the fixed-interval smoothing...
We discuss approximate maximum likelihood methods for blind identification and deconvolution. These algorithms are based on particle approximation versions of the EM algorithm. We consider two different methods which differ in the way the posterior distribution of the symbols is computed. The first algorithm is based on a novel particle approximation method of the fixed-interval smoothing whereas...
Particle filtering has been successfully used to approximate the fixed-lag or fixed-interval smoothing distributions in digital communication and to perform approximate maximum likelihood inference. Because the state-space is finite, it is possible at each step to consider all the offsprings (path) of any given particle. Because each particle has typically several possible offsprings, the population...
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