Treffer: Support vector machines for optimal channel decoding.

In this work, we investigate channel decoding techniques based on machine learning, and more specifically, on support vector machines (SVMs). Existing SVM-based decoders suffer from a scalability problem, characterized by the exponential growth of both the number of classifiers required to decode and the size of the training dataset as a function of the code dimension. This phenomenon, often referred to as the curse of dimensionality, renders existing SVM approaches impractical for larger codes. To partly address this limitation, we introduce a novel bit-wise SVM decoding strategy coupled with a noiseless optimization framework, which significantly reduces the computational burden. Specifically, for a code of length n and dimension k, our approach decreases the number of SVM classifiers from an exponential dependence to a linear in k, while the required training dataset is minimized to a single noiseless codeword per class, which remains exponential in k. We formulate the resulting optimization problem and derive its analytical solution. Moreover, we demonstrate that, under specific conditions, the solution of the optimization process produced by the proposed framework is equivalent to the optimal Maximum A Posteriori (MAP) decoding rule when applied to transmissions over additive white Gaussian noise (AWGN) channels. [ABSTRACT FROM AUTHOR]