Browsing by Subject "Finite number"

Now showing 1 - 3 of 3

Open Access
Beyond Nyquist sampling: a cost-based approach
(Optical Society of America, 2013) Özçelikkale, A.; Özaktaş, Haldun M.
A sampling-based framework for finding the optimal representation of a finite energy optical field using a finite number of bits is presented. For a given bit budget, we determine the optimum number and spacing of the samples in order to represent the field with as low error as possible. We present the associated performance bounds as trade-off curves between the error and the cost budget. In contrast to common practice, which often treats sampling and quantization separately, we explicitly focus on the interplay between limited spatial resolution and limited amplitude accuracy, such as whether it is better to take more samples with lower amplitude accuracy or fewer samples with higher accuracy. We illustrate that in certain cases sampling at rates different from the Nyquist rate is more efficient.
Open Access
On the capacity of fading channels with amplitude-limited inputs
(IEEE, 2016) Elmoslimany, A.; Duman, Tolga M.
We address the problem of finding the capacity of fading channels under the assumption of amplitude-limited inputs. Specifically, we show that if the fading coefficients have a finite support and the channel state information is only available at the receiver side, there is a unique input distribution that achieves the channel capacity and this input distribution is discrete with a finite number of mass points.
Open Access
Representation of optical fields using finite numbers of bits
(Optical Society of America, 2012-06-04) Özçelikkale, A.; Özaktaş, Haldun M.
We consider the problem of representation of a finite-energy optical field, with a finite number of bits. The optical field is represented with a finite number of uniformly spaced finite-accuracy samples (there is a finite number of amplitude levels that can be reliably distinguished for each sample). The total number of bits required to encode all samples constitutes the cost of the representation. We investigate the optimal number and spacing of these samples under a total cost budget. Our framework reveals the trade-off between the number, spacing, and accuracy of the samples. When we vary the cost budget, we obtain trade-off curves between the representation error and the cost budget. We also discuss the effect of degree of coherence of the field.