Deep Learning Based Mapping Diversity
Abstract
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.
Keywords
Deep learning , Mapping diversity , Autoencoder , Fading channel
References
- 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.