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By Theorem 1, it implies that the mean quantization errors of all users on each subcarrier n are identical, v k, n = v k', n.
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Figure 3 RDF (capacity of feedback channel) versus mean quantization error.
The relationship (when distortion is defined as the mean quantization error) is plotted in Figure 5.
where D k denotes the upper bound of the mean quantization error and Idenotes the mutual information.
Thus, we were interested in finding ideal intervals for the tested parameters that could simultaneously minimize the mean quantization error, while maximizing the mean neuron utility.
Here, the variable v k, n can be regarded as the mean quantization error for the channel power gain α k, n.
However, we can simplify this criterion in the case where on subcarrier n, the mean quantization error v k, n is identical among all users k.
We use the RDF to characterize the lower bound on the required feedback channel's capacity for a given mean quantization error under OFDMA downlink channels [2].
To shed some light on how these parameters could affect learning, we opted to measure the mean quantization error [(Mean (E{^{prime }}(t))], so as to obtain a single value that could characterize the quantization procedure across the entire stream.
By the rate-distortion theory [2], this RDF gives a minimum number of bits for the index I k that can describe the channel power gain A k without exceeding the mean quantization error D k.
We selected a SOM projection with a small mean quantization error.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com