Abstract
This letter is focused on quantized compressed sensing, assuming that Lasso is used for signal estimation. Leveraging recent work, we propose a constrained Lloyd–Max-like framework to optimize the quantization function in this setting, and show that when the number of observations is high, this method of quantization gives a significantly better recovery rate than standard Lloyd–Max quantization. We support our theoretical analysis with numerical simulations.
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