Universal decoding with an erasure option
Neri Merhav, Meir Feder
Abstract
Motivated by applications of rateless coding, decision feedback, and ARQ, we study the problem of universal decoding for unknown channels, in the presence of an erasure option. Specifically, we harness the competitive minimax methodology developed in earlier studies, in order to derive a universal version of Forney's classical erasure/list decoder, which in the erasure case, optimally trades off between the probability of erasure and the probability of undetected error. The proposed universal erasure decoder guarantees universal achievability of a certain fraction ξ of the optimum error exponents of these probabilities (in a sense to be made precise in the sequel). A single--letter expression for ξ, which depends solely on the coding rate and the threshold, is provided. The example of the binary symmetric channel is studied in full detail, and some conclusions are drawn.
Create a lesson
Related papers
Deterministic Identification over Additive Gaussian Channels
Jonathan E. W. Huffmann, Holger Boche
Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks
Qifei Wang, Zhen Gao, Li Qiao et al.
Real-Time Reconstruction of Markov Sources over MPR Channels
Pansee S. Elessawy, Nikolaos Pappas
Minimum Rate For Partially Observable Linear System with Side Information: LQG Plant and Gaussian-Markov Source
Sijie Li, Hyeji Kim
Spectral Approximation and Ergodic-Capacity Convergence of HMIMO Channels under Spatial-Wavenumber Domain Mismatch
Hangsong Yan, Hong Yang, Shu Sun
Sharp Minimax Regret for Infinite-Memory Logistic Prediction
Vaneet Aggarwal