Abstract
Digital wireless communication systems can be regarded as solving a statistical learning problem in real time. The sender-side process of encoding and/or modulating information to be sent can be viewed as generation process of training data in the statistical learning point of view, while the receiver-side process of decoding and/or demodulating the information on the basis of possibly noisy received signals as the learning process based on the training data set. Based on this view one can analyze digital wireless communication systems within the framework of statistical learning, where an approach based on statistical physics provides powerful tools. Analysis of the code-division multiple-access (CDMA) user detection problem is discussed in detail as a demonstrative example of this approach.
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Tanaka, T. (2004). Statistical Learning in Digital Wireless Communications. In: Ben-David, S., Case, J., Maruoka, A. (eds) Algorithmic Learning Theory. ALT 2004. Lecture Notes in Computer Science(), vol 3244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30215-5_35
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DOI: https://doi.org/10.1007/978-3-540-30215-5_35
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