Results 21 to 30 of about 5,046,998 (306)
Detection with embedded known symbols: optimal symbol placement and equalization [PDF]
2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2002 The detection of a data sequence with embedded known symbols is considered. For a class of symbol-by-symbol decision feedback receivers, known symbol distributions optimal with respect to the criterion of average mean square error (A-MSE) are presented. Optimal design of the decision feedback receiver is also obtained.
S. Adireddy, null Lang Tong
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Data-Driven Symbol Detection Via Model-Based Machine Learning [PDF]
Symposium on Software Performance, 2020 We present a data-driven framework to symbol detection design that combines machine learning (ML) and model-based algorithms. The resulting data-driven receivers are most suitable for systems where the underlying channel models are poorly understood ...
N. Farsad +3 more
semanticscholar +1 more source
Analysis of Low Probability of Detection Capability for Chaotic Standard Map-Based FH-OFDMA System
Applied Sciences, 2021 A frequency hopping orthogonal frequency division multiple access (FH-OFDMA) can provide low probability of detection (LPD) and anti-jamming capabilities to users against adversary detectors. To obtain an extreme LPD capability that cannot be provided by
Junwoo Jung +4 more
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Blind Channel Estimation and Joint Symbol Detection With Data-Driven Factor Graphs [PDF]
IEEE Transactions on CommunicationsWe investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels.
Luca Schmid +3 more
semanticscholar +1 more source
IEEE Access, 2019 In massive multiple-input multiple-output (MIMO) systems using a large number of antennas, it would be difficult to connect high-resolution analog-to-digital converters (ADCs) to each antenna component due to high cost and energy consumption problems. To
Seungsik Moon +5 more
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Symbolic Privacy Analysis through Linkability and Detectability [PDF]
, 2013 More and more personal information is exchanged on-line using communication protocols. This makes it increasingly important that such protocols satisfy privacy by data minimisation. Formal methods have been used to verify privacy properties of protocols; but so far, mostly in an ad-hoc way.
Veeningen, Meilof +2 more
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ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection [PDF]
IEEE Transactions on Wireless Communications, 2019 Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not require channel state information (CSI).
Nir Shlezinger +3 more
semanticscholar +1 more source
Data-Driven Factor Graphs for Deep Symbol Detection [PDF]
International Symposium on Information Theory, 2020 Many important schemes in signal processing and communications, ranging from the BCJR algorithm to the Kalman filter, are instances of factor graph methods.
Nir Shlezinger +3 more
semanticscholar +1 more source
Method for Symbol Detection in Substation Layout Diagrams Based on Text-Image MultimodalFusion [PDF]
Jisuanji kexueTo address the issues of inconvenient operation,low efficiency,and difficulty in managing recognition data during the manual identification of substation layout drawings,this paper proposes a morphology-based large-size drawing segmentation method and a ...
FAN Jiabin, WANG Baohui, CHEN Jixuan
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Neural Network-Aided BCJR Algorithm for Joint Symbol Detection and Channel Decoding [PDF]
IEEE Workshop on Signal Processing Systems, 2020 Recently, deep learning-assisted communication systems have achieved many eye-catching results and attracted more and more researchers in this emerging field.
Wen-Chiao Tsai +3 more
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