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Kang-Da Wu

dblp:327/3150 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Physical-layer communications · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications
modulation
0.912025
On-Off Keying Signal Detection Based on Hidden Markov Model for Rydberg Atomic Sensor · IEEE Trans. Commun. 2025
Physical-layer communications › modulation › amplitude modulation
on-off keying
0.912025
On-Off Keying Signal Detection Based on Hidden Markov Model for Rydberg Atomic Sensor · IEEE Trans. Commun. 2025
Physical-layer communications
signal detection
0.912025
On-Off Keying Signal Detection Based on Hidden Markov Model for Rydberg Atomic Sensor · IEEE Trans. Commun. 2025
Physical-layer communications
channel modeling
0.312025
On-Off Keying Signal Detection Based on Hidden Markov Model for Rydberg Atomic Sensor · IEEE Trans. Commun. 2025
Physical-layer communications › channel modeling
hidden markov model
0.312025
On-Off Keying Signal Detection Based on Hidden Markov Model for Rydberg Atomic Sensor · IEEE Trans. Commun. 2025

Methods — techniques the papers use, named apart from their topics

viterbi algorithm · 0.9monte carlo method · 0.9hidden markov model · 0.9
YearPublicationVenuePosition
2025 On-Off Keying Signal Detection Based on Hidden Markov Model for Rydberg Atomic Sensor
abstract
Rydberg atomic sensors have been seen as novel radio frequency (RF) measurements and the high sensitivity to a large range of frequencies makes it attractive for communications reception. In high-speed symbol transmission scenarios, the effect of rising and falling edges as symbol switching should not be ignored. In this work, we adopt a mixed Gaussian distribution to characterize the output distribution under symbol switching, especially under higher signal power and shorter symbol duration according to experimental measurements. Under high symbol rate, unequal rising and falling edges on signal detection make the system nonlinear. Based on the experimental measurement, we characterize such nonlinear effects via a state transition model, and adopt Hidden Markov Model (HMM) to characterize the received signal. We propose a Monte-Carlo method to compute the achievable transmission rate and bit error rate (BER) performance. In real experiments, a lower BER can be achieved by the Viterbi algorithm compared with single symbol detection (SSD). Moreover, the performance of Viterbi decoding is better with higher symbol rate compared with SSD.
Hao Wu 0136, Xinyuan Yao, Chongwu Xie, Kang-Da Wu, Guo-Yong Xiang, Chen Gong 0001
IEEE Trans. Commun.4