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Goodput Maximization With Quantized Feedback in the Finite Blocklength Regime for Quasi-Static Channels
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering. ABB AB, S-72161 Västerås, Sweden..ORCID iD: 0000-0001-5988-2763
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0002-7926-5081
2022 (English)In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 70, no 8, p. 5071-5084Article in journal (Refereed) Published
Abstract [en]

In this paper, we study a quantized feedback scheme to maximize the goodput of a finite blocklength communication scenario over a quasi-static fading channel. It is assumed that the receiver has perfect channel state information (CSI) and sends back the CSI to the transmitter over a resolution-limited error-free feedback channel. With this partial CSI, the transmitter is supposed to select the optimum transmission rate, such that it maximizes the overall goodput of the communication system. This problem has been studied for the asymptotic blocklength regime, however, no solution has so far been presented for finite blocklength. Here, we study this problem in two cases: with and without constraint on reliability. We first formulate the optimization problems and analytically solve them. Iterative algorithms that successfully exploit the system parameters for both cases are presented. It is shown that although the achievable maximum goodput decreases with shorter blocklengths and higher reliability requirements, significant improvement can be achieved even with coarsely quantized feedback schemes.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. Vol. 70, no 8, p. 5071-5084
Keywords [en]
Transmitters, Communication systems, Fading channels, Receivers, Reliability, Quantization (signal), Optimization, Channel coding, channel state information, goodput maximization, low-complexity receivers, quantized feedback, URLLC
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-317334DOI: 10.1109/TCOMM.2022.3186389ISI: 000846884500011Scopus ID: 2-s2.0-85133637437OAI: oai:DiVA.org:kth-317334DiVA, id: diva2:1694558
Note

QC 20220909

Available from: 2022-09-09 Created: 2022-09-09 Last updated: 2022-09-09Bibliographically approved

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Celebi, Hasan BasriSkoglund, Mikael

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