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Joint Identification and Sensing for Discrete Memoryless Channels

  • Technische Universität München
  • Bmbf Research Hub 6G-life
  • Munich Quantum Valley (MQV)
  • Max-Planck-lnstitut für Kohlenforschung
  • Munich Center for Quantum Science and Technology (MCQST)

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

12 Zitate (Scopus)

Abstract

In the identification (ID) scheme proposed by Ahlswede and Dueck, the receiver only checks whether a message of special interest to him has been sent or not. In contrast to Shannon transmission codes, the size of ID codes for a Discrete Memoryless Channel (DMC) grows doubly exponentially fast with the blocklength, if randomized encoding is used. This groundbreaking result makes the ID paradigm more efficient than the classical Shannon transmission in terms of necessary energy and hardware components. Further gains can be achieved by taking advantage of additional resources such as feedback. We study the problem of joint ID and channel state estimation over a DMC with independent and identically distributed (i.i.d.) state sequences. The sender simultaneously sends an ID message over the DMC with a random state and estimates the channel state via a strictly causal channel output. The random channel state is available to neither the sender nor the receiver. For the proposed system model, we establish a lower bound on the ID capacity-distortion function.

OriginalspracheEnglisch
Titel2023 IEEE International Symposium on Information Theory, ISIT 2023
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten442-447
Seitenumfang6
ISBN (elektronisch)9781665475549
DOIs
PublikationsstatusVeröffentlicht - 2023
Veranstaltung2023 IEEE International Symposium on Information Theory, ISIT 2023 - Taipei, Taiwan
Dauer: 25 Juni 202330 Juni 2023

Publikationsreihe

NameIEEE International Symposium on Information Theory - Proceedings
Band2023-June
ISSN (Print)2157-8095

Konferenz

Konferenz2023 IEEE International Symposium on Information Theory, ISIT 2023
Land/GebietTaiwan
OrtTaipei
Zeitraum25/06/2330/06/23

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