Öz
In this work, the mapping diversity technique, applied to enhance the bit/symbol error rate performance in wireless fading channels, is designed using deep learning based autoencoder. Specifically, the multiple signal constellations required in mapping diversity technique are obtained using autoencoder structure. In this framework, the multiple signal constellations, obtained by assuming multiple channel use in autoencoder structure, are used in mapping diversity and the performance of this proposed system is compared with classical repetitive transmission technique. Desing and simulations are repeated for different modulation types and thereby, the performance of the proposed system is investigated for various data rates under fading channel conditions. Consequently, supported with simulation results, the proposed deep learning based mapping diversity technique is shown to attain better performance than classical repetitive transmission technique.
Anahtar Kelimeler
Proje Numarası
MF-22003
Kaynakça
- Benelli, G. (1992). A New Method for the Integration of Modulation and Channel Coding in an ARQ Protocol. IEEE Transactions on Communications, 40(10), 1594–1606.
- Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2019). Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 21(4), 3039-3071.
- Dai, L., Jiao, R., Adachi, F., Poor, H. V., & Hanzo, L. (2020). Deep learning for wireless communications: An emerging interdisciplinary paradigm. IEEE Wireless Communications, 27(4), 133-139.
- Erpek, T., O’Shea, T. J., Sagduyu, Y. E., Shi, Y., & Clancy, T. C. (2020). Deep learning for wireless communications. Development and Analysis of Deep Learning Architectures, 223-266.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
- Kim, H., Oh, S., & Viswanath, P. (2020). Physical layer communication via deep learning. IEEE Journal on Selected Areas in Information Theory, 1(1), 5-18.
- Metzner, J. J. (1977). Improved Sequential Signaling and Decision Techniques for Nonbinary Block Codes. IEEE Transactions on Communications, 25(5), 561–563.
- O’Shea, T., & Hoydis, J. (2017). An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking, 3(4), 563-575.
- O'Shea, T. J., Karra, K., & Clancy, T. C. (2016). Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention. In 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 223-228).
- Proakis, J. G. (2008). Digital communications. McGraw-Hill, Higher Education.
- Qin, Z., Ye, H., Li, G. Y., & Juang, B. H. F. (2019). Deep learning in physical layer communications. IEEE Wireless Communications, 26(2), 93-99.
- Samra, H., Ding, Z., & Hahn, P. M. (2005). Symbol mapping diversity design for multiple packet transmissions. IEEETransactions on Communications, 53(5), 810–817.
- Simeone, O. (2018). A very brief introduction to machine learning with applications to communication systems. IEEE Transactions on Cognitive Communications and Networking, 4(4), 648-664.
- Wang, T., Wen, C. K., Wang, H., Gao, F., Jiang, T., & Jin, S. (2017). Deep learning for wireless physical layer: Opportunities and challenges. China Communications, 14(11), 92-111.
- Wengerter, C., Golitschek Edler Von Elbwart, A., Seidel, E., Velev, G., ve Schmitt, M. P. (2002). Advanced hybrid ARQ technique employing a signal constellation rearrangement. IEEE Vehicular Technology Conference, 56(4), 2002–2006.
Öz
Bu çalışmada kablosuz sönümlemeli haberleşme kanallarında bit/sembol hata oranı performansını iyileştirmek amacıyla uygulanan eşlemleme çeşitlemesi tekniği, derin öğrenme tabanlı özkodlayıcı kullanılarak tasarlanmıştır. Spesifik olarak eşlemleme çeşitlemesi tekniğinde gerekli çoklu işaret kümeleri özkodlayıcı kullanılarak elde edilmiştir. Bu çerçevede, özkodlayıcı yapısında çoklu kanal kullanımı varsayımıyla elde edilen çoklu işaret kümeleri eşlemleme çeşitlemesinde kullanılmış ve tasarlanan bu sistemin performansı eşlemleme çeşitlemesi uygulanmayan klasik tekrarlı iletim tekniği ile karşılaştırılmıştır. Tasarım ve benzetimler farklı modülasyon türleri için tekrar edilmiştir ve böylelikle tasarlanan sistemin farklı bilgi oranları için sönümlemeli kanal şartlarında performansı incelenmiştir. Sonuç olarak, tasarlanan derin öğrenme tabanlı eşlemleme çeşitlemesi tekniği ile klasik tekrarlamalı iletim tekniğinden daha iyi bir başarım elde edildiği benzetim sonuçları ile desteklenerek gösterilmiştir.
Anahtar Kelimeler
Derin öğrenme Eşlemleme çeşitlemesi Özkodlayıcı Sönümlemeli kanal
Destekleyen Kurum
Aydın Adnan Menderes Üniversitesi BAP Birimi
Proje Numarası
MF-22003
Teşekkür
Bu çalışma, Aydın Adnan Menderes Üniversitesi BAP Birimi tarafından MF-22003 proje numarası ile desteklenmiştir.
Kaynakça
- Benelli, G. (1992). A New Method for the Integration of Modulation and Channel Coding in an ARQ Protocol. IEEE Transactions on Communications, 40(10), 1594–1606.
- Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2019). Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 21(4), 3039-3071.
- Dai, L., Jiao, R., Adachi, F., Poor, H. V., & Hanzo, L. (2020). Deep learning for wireless communications: An emerging interdisciplinary paradigm. IEEE Wireless Communications, 27(4), 133-139.
- Erpek, T., O’Shea, T. J., Sagduyu, Y. E., Shi, Y., & Clancy, T. C. (2020). Deep learning for wireless communications. Development and Analysis of Deep Learning Architectures, 223-266.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
- Kim, H., Oh, S., & Viswanath, P. (2020). Physical layer communication via deep learning. IEEE Journal on Selected Areas in Information Theory, 1(1), 5-18.
- Metzner, J. J. (1977). Improved Sequential Signaling and Decision Techniques for Nonbinary Block Codes. IEEE Transactions on Communications, 25(5), 561–563.
- O’Shea, T., & Hoydis, J. (2017). An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking, 3(4), 563-575.
- O'Shea, T. J., Karra, K., & Clancy, T. C. (2016). Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention. In 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 223-228).
- Proakis, J. G. (2008). Digital communications. McGraw-Hill, Higher Education.
- Qin, Z., Ye, H., Li, G. Y., & Juang, B. H. F. (2019). Deep learning in physical layer communications. IEEE Wireless Communications, 26(2), 93-99.
- Samra, H., Ding, Z., & Hahn, P. M. (2005). Symbol mapping diversity design for multiple packet transmissions. IEEETransactions on Communications, 53(5), 810–817.
- Simeone, O. (2018). A very brief introduction to machine learning with applications to communication systems. IEEE Transactions on Cognitive Communications and Networking, 4(4), 648-664.
- Wang, T., Wen, C. K., Wang, H., Gao, F., Jiang, T., & Jin, S. (2017). Deep learning for wireless physical layer: Opportunities and challenges. China Communications, 14(11), 92-111.
- Wengerter, C., Golitschek Edler Von Elbwart, A., Seidel, E., Velev, G., ve Schmitt, M. P. (2002). Advanced hybrid ARQ technique employing a signal constellation rearrangement. IEEE Vehicular Technology Conference, 56(4), 2002–2006.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Radyo Frekansı Mühendisliği |
| Bölüm | Articles |
| Yazarlar | |
| Proje Numarası | MF-22003 |
| Erken Görünüm Tarihi | 3 Mart 2025 |
| Yayımlanma Tarihi | 15 Mart 2025 |
| Gönderilme Tarihi | 23 Ağustos 2023 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 17 Sayı: 1 |
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