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Training-Assisted Channel Estimation for Low-Complexity Squared-Envelope Receivers
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0001-5988-2763
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-6534-108X
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-7926-5081
2018 (English)In: IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, Institute of Electrical and Electronics Engineers Inc. , 2018Conference paper, Published paper (Refereed)
Abstract [en]

Squared-envelope receivers, also known as energy detectors, are, due to their simplified circuitry, low-cost and low-complexity receivers. Hence they are attractive implementation structures for future Internet-of-Things (IoT) applications. Even though there is considerable work on the wider research area of squared-envelope receivers, a comprehensive comparison and statistical characterization of training-assisted channel estimators for squared-envelope receivers appear to be absent from the literature. A detailed description of practical channel estimation schemes is necessary for the optimal training design of latency-constrained IoT applications. In this paper, various channel estimators are derived, their bias and variance are studied, and their performance is numerically compared against the Cramer-Rao lower bound.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018.
Keywords [en]
channel estimation, IoT, Low-complexity receivers, Cramer-Rao bounds, Internet of things, Signal processing, Wireless telecommunication systems, Bias and variance, Channel estimator, Comprehensive comparisons, Cramer Rao lower bound, Implementation structure, Low-complexity receiver, Optimal training, Statistical characterization
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-238005DOI: 10.1109/SPAWC.2018.8445974Scopus ID: 2-s2.0-85053459901ISBN: 9781538635124 (print)OAI: oai:DiVA.org:kth-238005DiVA, id: diva2:1279051
Conference
19th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2018, 25 June 2018 through 28 June 2018
Note

Conference code: 139030; Export Date: 30 October 2018; Conference Paper; Funding details: SSF, Stiftelsen för Strategisk Forskning; Funding text: This work was funded in part by the Swedish foundation for strategic research.

QC 20190115

Available from: 2019-01-15 Created: 2019-01-15 Last updated: 2019-01-15Bibliographically approved

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Celebi, Hasan BasriPitarokoilis, AntoniosSkoglund, Mikael

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