Alper Köse

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Ahmet Arda Atalik, Michael Christoph Gastpar, Alper Köse

The conditional mean is a fundamental and important quantity whose applications include the theories of estimation and rate-distortion. It is also notoriously difficult to work with. This paper establishes novel bounds on the differential entropy of the conditional mean in the case of finite-variance input signals and additive Gaussian noise. The main result is a new lower bound in terms of the differential entropies of the input signal and the noisy observation. The main results are also extended to the vector Gaussian channel and to the natural exponential family. Various other properties such as upper bounds, asymptotics, Taylor series expansion, and connection to Fisher Information are obtained. Two applications of the lower bound in the remote-source coding and CEO problem are discussed.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

The Price of Distributed: Rate Loss in the CEO Problem

Ahmet Arda Atalik, Michael Christoph Gastpar, Alper Köse

In the distributed remote (CEO) source coding problem, many separate encoders observe independently noisy copies of an underlying source. The rate loss is the difference between the rate required in this distributed setting and the rate that would be required in a setting where the encoders can fully cooperate. In this sense, the rate loss characterizes the price of distributed processing. We survey and extend the known results on the rate loss in various settings, with a particular emphasis on the case where the noise in the observations is Gaussian, but the underlying source is general.

2022

Differential Entropy of the Conditional Expectation under Gaussian Noise

Ahmet Arda Atalik, Michael Christoph Gastpar, Alper Köse

This paper considers an additive Gaussian noise channel with arbitrarily distributed finite variance input signals. It studies the differential entropy of the minimum mean-square error (MMSE) estimator and provides a new lower bound which connects the differential entropy of the input, output, and conditional mean. That is, the sum of differential entropies of the conditional mean and output is always greater than or equal to twice the input differential entropy. Various other properties such as upper bounds, asymptotics, Taylor series expansion, and connection to Fisher Information are obtained. An application of the lower bound in the remote-source coding problem is discussed, and extensions of the lower and upper bounds to the vector Gaussian channel are given.

IEEE2021

Resource Allocation for Underlaying Device-to-Device Communications Using Maximal Independent Sets and Knapsack Algorithm

Alper Köse

In this paper, we address the resource allocation problem of device-to-device (D2D) communications underlaying orthogonal frequency division multiple access (OFDMA) based cellular systems by exploiting the efficiency that comes from an ensemble of graph theory and Knapsack problem. It is possible to construct the conflict graph of the D2D pairs by finding the maximal independent sets. Then, we use those independent sets as inputs to Knapsack problem iteratively in order to find D2D groups which allocate the subchannels. In Knapsack problem, we consider a maximum interference level that the base station is exposed at each subchannel. We illustrate that the proposed resource allocation method significantly outperforms graph coloring in terms of average data rate for the high number of underlaying D2D pairs in cellular systems.

IEEE2018