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In this paper, we are interested in the performance of a variable-length stop-feedback (VLSF) code with $m$ optimal decoding times for the binary-input additive white Gaussian noise channel. We first develop tight approximations on the tail…

Information Theory · Computer Science 2022-02-08 Hengjie Yang , Recep Can Yavas , Victoria Kostina , Richard D. Wesel

This paper studies reliability-guaranteed decoding for variable-length stop-feedback (VLSF) codes over correlated noncoherent fading channels. The decoding rule is based on the evolution of the information density associated with a given…

Information Theory · Computer Science 2026-04-20 Guodong Sun , Samir M. Perlaza , Philippe Mary , Jean-Marie Gorce

In this paper, we present new achievability bounds on the maximal achievable rate of variable-length stop-feedback (VLSF) codes operating over a binary erasure channel (BEC) at a fixed message size $M = 2^k$. We provide new bounds for VLSF…

Information Theory · Computer Science 2023-07-31 Hengjie Yang , Richard D. Wesel

We study variable-length codes for point-to-point discrete memoryless channels with noiseless unlimited-rate feedback that occurs in $L$ bursts. We term such codes variable-length bursty-feedback (VLBF) codes. Unlike classical codes with…

Information Theory · Computer Science 2023-06-27 James Y. Chen , Recep Can Yavas , Victoria Kostina

This paper presents a general approach for optimizing the number of symbols in increments (packets of incremental redundancy) in a feedback communication system with a limited number of increments. This approach is based on a tight normal…

Information Theory · Computer Science 2016-02-17 Kasra Vakilinia , Sudarsan V. S. Ranganathan , Dariush Divsalar , Richard D. Wesel

In recent years, the derivation of nonasymptotic converse and achievability bounds on the maximum coding rate as a function of the error probability and blocklength has gained attention in the information theory literature. While these…

Information Theory · Computer Science 2020-09-25 Alejandro Lancho , Jöhan Ostman , Giuseppe Durisi , Tobias Koch , Gonzalo Vazquez-Vilar

This paper presents a saddlepoint approximation of the random-coding union bound of Polyanskiy et al. for i.i.d. random coding over discrete memoryless channels. The approximation is single-letter, and can thus be computed efficiently.…

Information Theory · Computer Science 2014-04-28 Jonathan Scarlett , Alfonso Martinez , Albert Guillén i Fàbregas

We present nonasymptotic achievability and converse bounds on the maximum coding rate (for a fixed average error probability and a fixed average blocklength) of variable-length full-feedback (VLF) and variable-length stop-feedback (VLSF)…

Information Theory · Computer Science 2016-07-26 Rahul Devassy , Giuseppe Durisi , Benjamin Lindqvist , Wei Yang , Marco Dalai

Theoretical analysis has long indicated that feedback improves the error exponent but not the capacity of single-user memoryless channels. Recently Polyanskiy et al. studied the benefit of variable-length feedback with termination (VLFT)…

Information Theory · Computer Science 2013-02-26 Tsung-Yi Chen , Adam R. Williamson , Richard D. Wesel

Variable-length feedback coding has the potential to significantly enhance communication reliability in finite block length scenarios by adapting coding strategies based on real-time receiver feedback. Designing such codes, however, is…

Information Theory · Computer Science 2024-11-14 Wenwei Lai , Yulin Shao , Yu Ding , Deniz Gunduz

This paper investigates variable-length stop-feedback codes for memoryless channels in point-to-point, multiple access, and random access communication scenarios. The proposed codes employ $L$ decoding times $n_1, n_2, \dots, n_L$ for the…

Information Theory · Computer Science 2023-12-12 Recep Can Yavas , Victoria Kostina , Michelle Effros

Incremental redundancy with ACK/NACK feedback produces a variable-length stop-feedback (VLSF) code constrained to have $m$ decoding times, with an ACK/NACK feedback to the transmitter at each decoding time. This paper focuses on the…

Information Theory · Computer Science 2023-02-07 Hengjie Yang , Recep Can Yavas , Victoria Kostina , Richard D. Wesel

In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building blocks. Similarly, neural networks perform a given task by learning features of…

Machine Learning · Computer Science 2022-02-16 Deborah Pereg , Israel Cohen , Anthony A. Vassiliou

It has recently been observed that certain extremely simple feature encoding techniques are able to achieve state of the art performance on several standard image classification benchmarks including deep belief networks, convolutional nets,…

Machine Learning · Computer Science 2013-01-08 Misha Denil , Nando de Freitas

We present a comprehensive framework for structured sparse coding and modeling extending the recent ideas of using learnable fast regressors to approximate exact sparse codes. For this purpose, we develop a novel block-coordinate proximal…

Machine Learning · Computer Science 2012-06-22 Alex Bronstein , Pablo Sprechmann , Guillermo Sapiro

We study variable-length feedback (VLF) codes under a strict delay constraint to maximize their average transmission rate (ATR) in a discrete memoryless channel (DMC) while considering periodic decoding attempts. We first derive a lower…

Information Theory · Computer Science 2015-02-25 Seong Hwan Kim , Dan Keun Sung , Tho Le-Ngoc

The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process. Such an approximation has not yet been rigorously examined, and standard…

Computer Vision and Pattern Recognition · Computer Science 2019-02-20 Hongyu Xu , Zhangyang Wang , Haichuan Yang , Ding Liu , Ji Liu

Sparse and convolutional constraints form a natural prior for many optimization problems that arise from physical processes. Detecting motifs in speech and musical passages, super-resolving images, compressing videos, and reconstructing…

Computer Vision and Pattern Recognition · Computer Science 2014-06-11 Hilton Bristow , Simon Lucey

This paper presents a new class of sparse superposition codes for low-rates and short-packet communications over the additive white Gaussian noise channel. The new code is orthogonal sparse superposition (OSS) code. A codeword of OSS codes…

Information Theory · Computer Science 2020-11-24 Yunseo Nam , Jeonghun Park , Songnam Hong , Namyoon Lee

The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear…

Computer Vision and Pattern Recognition · Computer Science 2017-02-15 Thalaiyasingam Ajanthan , Alban Desmaison , Rudy Bunel , Mathieu Salzmann , Philip H. S. Torr , M. Pawan Kumar
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