In order to show the performance of the proposed codebook design, the CDI of the selected users and clusters is quantized by using different codebooks.
Suppose that random vector, x, with pdf, f, is quantized by quantizer q and the corresponding reconstructed vector is x ̂.
The remaining constraints imply that CDI for each serving and interfering BSs is quantized by using at least one bit.
The reduced set of LSF vectors is quantized by multistage vector quantizers, and the corresponding LT model is recalculated at the decoder from the quantized reduced set of LSFs.
If each transform coefficient U m, m = 1,..., M1, where U = T T X, is quantized by a high-rate scalar quantizer and the quantizer output is encoded by a SW code which uses Y as decoder side-information, then the transform T which asymptotically minimizes E X - X ^ 2 is the CKLT of X given Y and the bit allocation is given by (2).
The measurement signal is quantized by a logarithmic quantizer.
Assume that the system measurement output is quantized by a static, memoryless and logarithmic quantizer before it being transmitted to the filter, while the quantization errors can be treated as sector-bound uncertainties.
where (hat {textbf {v}}_{i}) is the normalized i-th column vector of (hat {textbf {H}}^{dag }(hat {textbf {H}}hat {textbf {H}}^{dag })^{-1}) given (hat {textbf {H}}=[hat {textbf {h}}_{1},...,hat {textbf {h}}_ M]) and (hat {textbf {h}}_{i}) is quantized by a B-bit random codebook that minimizes the chordal distance, for analytical simplicity1.
By the use of this quantization matrix, each subband is quantized by a value that weights the overall resulting quantized image at the threshold of artifacts visibility.
