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Tight Frames, Non-convex Regularizers, and Quantized Neural Networks for Solving Linear Inverse Problems
The recovery of a signal/image from compressed measurements involves formulating an optimization problem and solving it using an efficient algorithm. The optimization objective involves data fidelity, which is responsible ...
Sparse Bayesian Learning For Joint Channel Estimation Data Detection In OFDM Systems
(2018-08-30)
Bayesian approaches for sparse signal recovery have enjoyed a long-standing history in signal processing and machine learning literature. Among the Bayesian techniques, the expectation maximization based Sparse Bayesian ...
Signal Processing Algorithms for Next-Generation Wireless Systems: Reconfigurable Intelligent Surfaces and Integrated Sensing and Communications
Next-generation wireless systems aim to revolutionize communication by achieving data rates up to terabits per second while also supporting diverse applications such as autonomous mobility, industrial automation, and ...