publications

2024

1. ISIT

   One-Shot Wyner-Ziv Compression of a Uniform Source

   Oğuzhan Kubilay Ülger, and Elza Erkip

   In 2024 IEEE International Symposium on Information Theory (ISIT), 2024

   Abs arXiv HTML Slides

   In this paper, we consider the one-shot version of the classical Wyner-Ziv problem where a source is compressed in a lossy fashion when only the decoder has access to a correlated side information. Following the entropy-constrained quantization framework, we assume a scalar quantizer followed by variable length entropy coding. We consider compression of a uniform source, motivated by its role in the compression of processes with low-dimensional features embedded within a high-dimensional ambient space. We find upper and lower bounds to the entropy-distortion functions of the uniform source for quantized and noisy side information, and illustrate tightness of the bounds at high compression rates.

2. ISIT

   Robust Distributed Compression with Learned Heegard–Berger Scheme

   Eyyüp Taşçı, Ezgi Özyılkan, Oğuzhan Kubilay Ülger, and Elza Erkip

   In 2024 IEEE International Symposium on Information Theory Workshops (ISIT-W), 2024

   Abs arXiv HTML Poster

   We consider lossy compression of an information source when decoder-only side information may be absent. This setup, also referred to as the Heegard–Berger or Kaspi problem, is a special case of robust distributed source coding. Building upon previous works on neural network-based distributed compressors developed for the decoder-only side information (Wyner–Ziv) case, we propose learning-based schemes that are amenable to the availability of side information. We find that our learned compressors mimic the achievability part of the Heegard–Berger theorem and yield interpretable results operating close to information-theoretic bounds. Depending on the availability of the side information, our neural compressors recover characteristics of the point-to-point (i.e., with no side information) and the Wyner–Ziv coding strategies that include binning in the source space, although no structure exploiting knowledge of the source and side information was imposed into the design.

2023

1. Allerton

   Single-Shot Lossy Compression for Joint Inference and Reconstruction

   Oğuzhan Kubilay Ülger, and Elza Erkip

   In 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2023

   Abs arXiv HTML Slides

   In the classical source coding problem, the compressed source is reconstructed at the decoder with respect to some distortion metric. Motivated by settings in which we are interested in more than simply reconstructing the compressed source, we investigate a single shot compression problem where the decoder is tasked with reconstructing the original data as well as making inferences from it. Quality of inference and reconstruction is determined by a distortion criteria for each task. Given allowable distortion levels, we are interested in characterizing the probability of excess distortion. Modeling the joint inference and reconstruction problem as direct-indirect source coding one, we obtain lower and upper bounds for excess distortion probability. We specialize the converse bound and present a new easily computable achievability bound for the case where the distortion metric for reconstruction is logarithmic loss.

2020

1. TSP

   Efficient Physical Layer Spoofing Detection with an Autoregressive Model

   Oğuzhan Kubilay Ülger, Mehmet Özgün Demir, Ozan Alp Topal, Güneş Karabulut Kurt, and 1 more author

   In 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), 2020

   Abs HTML

   The broadcast nature of the wireless medium makes it vulnerable to impersonation attacks. Traditionally, cryptographic techniques are used against these threats. However, these methods are computationally complex and demanding for lesser equipped devices. The randomness of the wireless channels can be exploited to achieve authentication at the physical layer. In this paper, we propose an efficient channel-based authentication rule to detect the presence of an illegal transmitter. We also build a hypothesis test and then develop an effective likelihood test. The performance of this method is analyzed by studying miss detection and false alarm rates under various parameters. We conclude that a spoofing detection strategy can be developed using adjacent channel coefficients.