Designing Communication Receivers Using Machine Learning Techniques is coming at 11/20/2017 - 4:00pm
LPSC 125 Mon, 11/20/2017 - 4:00pm Brian M. Kurkoski Associate Professor, Japan Advanced Institute of Science and Technology Abstract: Your smartphone has many communications receivers, not only in its various wireless interfaces, but in the flash memory controller as well. In fixed-precision VLSI receivers, reducing the number of bits used to represent messages will reduce power consumption and increase battery life. This presentation describes the design of fixed-precision receivers from an information theory perspective. This can be called "hardware-aware information theory" because the objective is to maximize mutual information (an information theory quantity) while minimizing the number of message bits (in the hardware implementation). Results from machine learning play a key role, because quantization can be seen as classification. Numerical results show that LDPC decoders based on the proposed max-LUT method can outperform belief-propagation decoders. Bio: Read more: http://eecs.oregonstate.edu/colloquium/designing-communication-receivers... [1] [1] http://eecs.oregonstate.edu/colloquium/designing-communication-receivers-using-machine-learning-techniques
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