Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912245304262656 |
|---|---|
| author | Kozlenko, Mykola Vialkova, Vira |
| author_facet | Kozlenko, Mykola Vialkova, Vira |
| contents | In this paper we present the symbol and bit error rate performance of the weak signal digital communications system. We investigate orthogonal multiple frequency shift keying modulation scheme with supervised machine learning demodulation approach using simple dense end-to-end artificial neural network. We focus on the interference immunity over an additive white Gaussian noise with average signal-to-noise ratios from -20 dB to 0 dB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16371 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications Kozlenko, Mykola Vialkova, Vira Signal Processing Machine Learning In this paper we present the symbol and bit error rate performance of the weak signal digital communications system. We investigate orthogonal multiple frequency shift keying modulation scheme with supervised machine learning demodulation approach using simple dense end-to-end artificial neural network. We focus on the interference immunity over an additive white Gaussian noise with average signal-to-noise ratios from -20 dB to 0 dB. |
| title | Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2502.16371 |