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We define the error exponent of the typical random code as the long-block limit of the negative normalized expectation of the logarithm of the error probability of the random code, as opposed to the traditional random coding error exponent,…

Information Theory · Computer Science 2017-08-25 Neri Merhav

Recent contrastive representation learning methods rely on estimating mutual information (MI) between multiple views of an underlying context. E.g., we can derive multiple views of a given image by applying data augmentation, or we can…

Machine Learning · Computer Science 2021-06-28 Alessandro Sordoni , Nouha Dziri , Hannes Schulz , Geoff Gordon , Phil Bachman , Remi Tachet

We propose a source/channel duality in the exponential regime, where success/failure in source coding parallels error/correctness in channel coding, and a distortion constraint becomes a log-likelihood ratio (LLR) threshold. We establish…

Information Theory · Computer Science 2017-01-27 Sergey Tridenski , Ram Zamir

This paper studies the concentration properties of random codes. Specifically, we show that, for discrete memoryless channels, the error exponent of a randomly generated code with pairwise-independent codewords converges in probability to…

Information Theory · Computer Science 2022-03-16 Lan V. Truong , Giuseppe Cocco , Josep Font-Segura , Albert Guillén i Fàbregas

A lower bound on the maximum likelihood (ML) decoding error exponent of linear block code ensembles, on the erasure channel, is developed. The lower bound turns to be positive, over an ensemble specific interval of erasure probabilities,…

Information Theory · Computer Science 2019-01-23 Enrico Paolini , Gianluigi Liva

We analyze two-user single-antenna fading interference channels with perfect receive channel state information (CSI) and no transmit CSI. We compute the diversity-multiplexing tradeoff (DMT) region of a fixed-power-split Han and Kobayashi…

Information Theory · Computer Science 2009-05-07 Cemal Akcaba , Helmut Boelcskei

We consider a broadcast channel with a degraded message set, in which a single transmitter sends a common message to two receivers and a private message to one of the receivers only. The main goal of this work is to find new lower bounds to…

Information Theory · Computer Science 2009-06-09 Yonatan Kaspi , Neri Merhav

Coding and testing schemes for binary hypothesis testing over noisy networks are proposed and their corresponding type-II error exponents are derived. When communication is over a discrete memoryless channel (DMC), our scheme combines…

Information Theory · Computer Science 2018-06-15 Sadaf Salehkalaibar , Michele Wigger

The likelihood decoder is a stochastic decoder that selects the decoded message at random, using the posterior distribution of the true underlying message given the channel output. In this work, we study a generalized version of this…

Information Theory · Computer Science 2016-11-17 Neri Merhav

High-capacity NAND flash memories use multi-level cells (MLCs) to store multiple bits per cell and achieve high storage densities. Higher densities cause increased raw bit error rates (BERs), which demand powerful error correcting codes.…

Information Theory · Computer Science 2012-02-08 Jiadong Wang , Guiqiang Dong , Tong Zhang , Richard Wesel

End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which comes close to well-developed classical encoder-decoder…

Information Theory · Computer Science 2019-03-12 Rick Fritschek , Rafael F. Schaefer , Gerhard Wunder

This paper establishes the exact strong converse exponent of the soft covering problem in the classical setting. This exponent characterizes the slowest achievable convergence speed of the total variation to one when a code of rate below…

Information Theory · Computer Science 2026-04-01 Xingyi He , S. Sandeep Pradhan , Andreas Winter

An approach is established for maximizing the Lower bound on the Mismatch capacity (hereafter abbreviated as LM rate), a key performance bound in mismatched decoding, by optimizing the channel input probability distribution. Under a fixed…

Information Theory · Computer Science 2024-05-02 Lingyi Chen , Shitong Wu , Xinwei Li , Huihui Wu , Hao Wu , Wenyi Zhang

In continuation to an earlier work, where error exponents of typical random codes were studied in the context of general block coding, with no underlying structure, here we carry out a parallel study on typical random, time-varying trellis…

Information Theory · Computer Science 2019-03-05 Neri Merhav

In this paper, we study the upper and the lower bounds on the joint source-channel coding error exponent with decoder side-information. The results in the paper are non-trivial extensions of the Csiszar's classical paper [5]. Unlike the…

Information Theory · Computer Science 2009-01-26 Cheng Chang

A new lower bound for the average probability or error for a two-user discrete memoryless (DM) multiple-access channel (MAC) is derived. This bound has a structure very similar to the well-known sphere packing packing bound derived by…

Information Theory · Computer Science 2010-10-08 Ali Nazari , S. Sandeep Pradhan , Achilleas Anastasopoulos

Several recent works in communication systems have proposed to leverage the power of neural networks in the design of encoders and decoders. In this approach, these blocks can be tailored to maximize the transmission rate based on…

Information Theory · Computer Science 2020-07-15 Sina Molavipour , Germán Bassi , Mikael Skoglund

This work studies optimal detection for communication over diffusion-based molecular timing (DBMT) channels. The transmitter simultaneously releases multiple information particles, where the information is encoded in the time of release.…

Information Theory · Computer Science 2017-09-12 Yonathan Murin , Nariman Farsad , Mainak Chowdhury , Andrea Goldsmith

K.A.S. Immink and J.H. Weber recently defined and studied a channel with both gain and offset mismatch, modelling the behaviour of charge-leakage in flash memory. They proposed a decoding measure for this channel based on minimising Pearson…

Information Theory · Computer Science 2015-09-10 Simon R. Blackburn

In this paper, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed Cross-Modal Info-Max Hashing…

Computer Vision and Pattern Recognition · Computer Science 2021-12-14 Tuan Hoang , Thanh-Toan Do , Tam V. Nguyen , Ngai-Man Cheung
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