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Projects Adaptive, Wireless, Signal Detection and Decoding
In this project, we propose a new Bayesian receiver for signal detection in flat-fading channels. First, the detection problem is formulated as an inference problem in a hybrid dynamic system that has both continuous and discrete variables. Then, an expectation propagation algorithm is proposed to address the inference problem. As an extension of belief propagation, expectation propagation efficiently approximates a Bayesian estimation by iteratively propagating information between different nodes in the dynamic system and projecting exact messages into the exponential family. Compared to sequential Monte Carlo filters and smoothers, the new method has much lower computational complexity since it makes analytically deterministic approximations instead of Monte Carlo approximations. Our simulations demonstrate that the new receiver achieves accurate detection without the aid of any training symbols or decision feedbacks. Future work involves joint decoding and channel estimation, where convolutional codes are used to protect signals from noise corruption. Initial results are promising.

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