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Item type:Publication, Access status: with files , Machine learning for reliable communication under coarse quantization(2021-09-24); This thesis addresses the use of machine learning methods for reliable transmission despite coarsely quantized soft information at the receiver. In particular, the design of powerful error correction algorithms under coarse quantization is presented. This requires an interdisciplinary approach based on the interplay of information theory, machine learning, and communications engineering. A particular focus is put on the information bottleneck method. Interestingly, the designed coarsely quantized signal processing units achieve almost the same performance in terms of reliability as conventional non-quantized methods.Publicationtype: Doctoral Thesis365 1460 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The information bottleneck method in communications(2020); The Information Bottleneck method is a generic information theoretical framework which aims for the compression of an observed random variable to a compressed random variable. The focal aim in designing this compression is to preserve relevant information. The method originates from machine learning and so far only has a few practical applications in communications. This thesis describes the application of the Information Bottleneck method to problems of receiver-sided signal processing in communications. The complexity of the receiver-sided baseband processing algorithms for demodulation and channel decoding causes a severe bottleneck in modern digital communication receivers. Their implementation complexity is mainly influenced by the bit width used to represent the signals processed in the hardware of the receiver and the arithmetical operations involved in the signal processing algorithms. Practical receiver implementations, therefore, have to be strongly quantized. Moreover, the involved signal processing algorithms have to be as simple as possible. The Information Bottleneck method provides algorithms which aim to maximize the preserved relevant information for a given bit width, hence motivating to apply the Information Bottleneck method to receiver design. The problems covered in this thesis are the design of scalar channel output quantizers, the decoding of low-density parity-check codes and channel estimation and detection algorithms. It is shown that the Information Bottleneck design principle allows to build signal processing blocks for these problems which allow for very small bit widths, typically around four to five bits per sample. Anyway, performance close to that of signal processing algorithms with double precision can be achieved. A key to achieve this small bit width is the signal representation using only quantization indices instead of real or complex representation values. Moreover, all operations required in the obtained signal processing blocks degenerate to simple lookup operations. As a result, the aforementioned design goals for communication receivers are inherently achieved by the receiver design with the Information Bottleneck method.Publicationtype: Doctoral Thesis540 1267