1. What are the contributions mentioned in the paper "Distributed functional scalar quantization simplified" ?
The authors show that a much simpler decoder has equivalent asymptotic performance to the conditional expectation estimator studied previously, thus reducing decoder design complexity.. Finally, through simulation results, the authors demonstrate that performance at moderate coding rates is well predicted by the asymptotic analysis, and they give new insight on the rate of convergence.
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2. What is the simplest way to optimize quantizers?
In a distributed network where the encoders employ scalar quantization and the decoder performs a reconstruction using on the quantized data to approximate a desired computation , optimizing the quantizers for rather than source fidelity can lead to substantial gains.
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3. Why is the quantizer of the form shown in Fig. 2 unique?
Because of the limiting conditions on , there is a one-to-one correspondence between and , and hence a quantizer of the form shown in Fig. 2 can be uniquely specified using a point density function and codebook size.
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4. What is the optimal quantizer for a distribution with unbounded support?
In general, the optimal entropy-constrained quantizer (at a finite rate) for a distribution with unbounded support can have an infinite number of codewords [28].
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