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Quantum Machine Learning
Performance vs. Spectral Properties For Single-Sideband Continuous Phase Modulation
arXiv
Authors: Karim Kassan, Haïfa Farès, D. Christian Glattli, Yves Louët
Year
2020
Paper ID
19118
Status
Preprint
Abstract Read
~2 min
Abstract Words
131
Citations
N/A
Abstract
This study revokes the performance of continuous phase modulation (CPM) able to generate a single-sideband (SSB) spectrum directly. This signal is analyzed in terms of modulation indices, pulse lengths, and pulse widths, all of which affect error probability, bandwidth, SSB property, and receiver complexity. The error probability performance is based on an approximation of the minimum Euclidean distance. A numerical power spectral density calculation for this particular SSB modulation in terms of modulation index is presented. Reasonable tradeoffs in designing modulation schemes have been proposed using multi-objective optimization to ensure sizable improvements in bit error rate (BER), spectral efficiencies, and complexity without losing the property of being a SSB signal. Performance comparisons are made with known CPM schemes, e.g., Gaussian Minimum Shift Keying (GMSK) and Raised Cosine based CPM (RC)
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2020 reference point for readers tracking recent quantum research.
- This study revokes the performance of continuous phase modulation (CPM) able to generate a single-sideband (SSB) spectrum directly.
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