Skip to main content
SearchLoginLogin or Signup

Convolutional Neural Network Based Equalizer for Improving the Reliability Performance of OFDM with Subcarrier Power Modulation

The authors propose the use of Convolutional Neural Networks (CNN) based equalizer for orthogonal frequency division multiplexing with subcarrier power modulation (OFDM-SPM) in order to significantly improve its reliability performance.

Published onJul 15, 2021
Convolutional Neural Network Based Equalizer for Improving the Reliability Performance of OFDM with Subcarrier Power Modulation
·

ABSTRACT

As the demand for higher data rates has rapidly been increasing day after day, researchers around the world have given serious attention and made significant efforts towards exploring new techniques that can improve the spectral efficiency of future wireless systems. Among these methods, the modulation technique named as orthogonal frequency division multiplexing with subcarrier power modulation (OFDM-SPM) is considered as a key potential candidate transmission method, which has the potential to effectively improve the per-user spectral efficiency of wireless networks. However, the reliability performance efficiency of OFDM-SPM is not that high, where it was found that the additional data stream conveyed by sub-carriers’ power has higher bit error rate (BER) performance compared to the data stream conveyed by conventional modulation schemes. To improve the reliability performance of  OFDM-SPM furthermore, in this paper, we propose the use of Convolutional Neural Networks (CNN) based equalizer for OFDM-SPM. Simulation results show that Convolutional Neural Networks (CNN) based equalizer can improve the reliability performance of OFDM-SPM by 5-10 dB compared to the conventional OFDM-SPM scheme that does not use CNN.

INDEX TERMS: CNN, DL, AI, ML, OFDM, OFDM-SPM, Rayleigh fading, SNR, STN, BER, Training Network.

DOWNLOAD ARTICLE PDF

References

[1] A. Jaradat, J. M. Hamamreh, H. Arslan, “Modulation Options for OFDM-Based Waveforms: Classification, Comparison, and Future Directions, ”IEEE Access, 7(1), 17263-17278, Feb. 2019.

[2] A. M. Jaradat, J. M. Hamamreh and H. Arslan, "OFDM With Hybrid Number and Index Modulation," in IEEE Access, vol. 8, pp. 55042-55053,2020.

[3] J. M. Hamamreh, Z. E. Ankarali, and H. Arslan, “CP-Less OFDM with Alignment Signals for Enhancing Spectral Efficiency, Reducing Latency, and Improving PHY Security of 5G and Beyond Services,” in IEEE Access, vol. 6, pp. 63649-63663, 2018.

[4] J. M. Hamamreh, E. Basar, and H. Arslan, “OFDM-subcarrier indexselection for enhancing security and reliability of 5G URLLC services,”IEEE Access, vol. 5, pp. 25 863–25 875, 2017.

[5] E. Ba ̧sar, “OFDM With Index Modulation Using Coordinate Interleaving, ”in IEEE Wireless Communications Letters, vol. 4, no. 4, pp. 381-384, Aug.2015, doi: 10.1109/LWC.2015.2423282.

[6] A. Hajar, J. M. Hamamreh, M. Abewa and Y. Belallou, "A Spectrally Efficient OFDM-Based Modulation Scheme for Future Wireless Systems, "2019 Scientific Meeting on Electrical-Electronics Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey, 2019, pp. 1-4, doi:10.1109/EBBT.2019.8742049.

[7] Y. Belallou, J. M. Hamamreh and A. Hajar, “OFDM-Subcarrier Power Modulation with two-dimensional signal constellations,” 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), Izmir,Turkey, 2019, pp. 1-6, doi: 10.1109/ASYU48272.2019.8946346.

[8] Abewa, M., Hamamreh, J. M. (2020). Non-coherent OFDM-SubcarrierPower Modulation for Low Complexity and High Throughput IoT Applications. RS Open Journal on Innovative Communication Technologies.

[9] Hajar, A., Hamamreh, J. M. (2020). The Generalization of Orthogonal Frequency Division Multiplexing With Subcarrier Power Modulation to Quadrature Signal Constellations. RS Open Journal on Innovative Communication Technologies.

[10] J. M. Hamamreh, Abdulwahab Hajar, and Mohamedou Abewa, "Orthog-onal Frequency Division Multiplexing With Subcarrier Power Modulationfor Doubling the Spectral Efficiency of 6G and Beyond Networks." inTransactions on Emerging Telecommunications Technologies, 2020.

[11] Y. Yıldırım, S. Özer and H. A. Çırpan, "Deep Receiver Design for Multi-carrier Waveforms Using CNNs," IEEE 43rd International Conference on Telecommunications and Signal Processing (TSP), Milan, Italy, 2020, pp.31-36, doi: 10.1109/TSP49548.2020.9163562.

[12] W. Xu, Z. Zhong, Y. Be’ery, X. You and C. Zhang, "Joint Neural Network Equalizer and Decoder," IEEE 15th International Symposium on Wireless Communication Systems (ISWCS), Lisbon, Portugal, 2018, pp. 1-5, doi:10.1109/ISWCS.2018.8491056.

[13] S. Hong et al., "Convolutional Neural Network Aided Signal Modulation Recognition in OFDM Systems," IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-5, doi:10.1109/VTC2020-Spring48590.2020.9128455.

[14] M. Mirmohammadsadeghi, S. S. Hanna and D. Cabric, "Modulation classification using convolutional neural networks and spatial transformer networks," IEEE 51st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2017, pp. 936-939, doi: 10.1109/AC-SSC.2017.8335486.

[15] M. K. M. Fadul, D. R. Reising and M. Sartipi, "Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning," in IEEE Access, vol. 9, pp. 17100-17113, 2021.

[16] W. S. Costa, J. L. A. Samatelo, H. R. O. Rocha, M. E. V. Segatto and J. A. L. Silva, "Direct Equalization with Convolutional Neural Networks in OFDM based VLC Systems," 2019 IEEE Latin-American Conference on Communications (LATINCOM), Salvador, Brazil, 2019, pp. 1-6, doi:10.1109/LATINCOM48065.2019.8938004.

[17] X. Glorotand Y. Bengio. "Understanding the difficulty of training deep feed forward neural networks." In Proceedings of the thirteenth international conference on artificial intelligence and statistics (Series: Proceedings of Machine Learning Research), pp. 249-256. 2010.

[18] C. M. Bishop, “Pattern recognition and machine learning”. Springer, NY, 2006.

[19] Jiang, Peiwen , Wang, Tianqi , Han, Bin , Gao, Xuanxuan , Zhang, Jing, Wen, Chao-Kai , Jin, Shi , Li, Geoffrey. (2018). Artificial Intelligence-aided OFDM Receiver: Design and Experimental Results.

[20] Abuqamar, A., Hamamreh, J. M. (2021). Back Propagation Artificial Neural Network for Improving the Performance of STBC-based OFDM with Subcarrier Power Modulation . RS Open Journal on Innovative Communication Technologies, 2(4). https://doi.org/10.46470/03d8ffbd.7aff4a62

[21] Hijazi, M., Hamamreh, J. M. (2021). Signal Space Diversity for Improvingthe Reliability Performance of OFDM with Subcarrier Power Modula-tion. RS Open Journal on Innovative Communication Technologies, 2(3).https://doi.org/10.46470/03d8ffbd.7f32914f

[22] Abuqamar, A., Hamamreh, J. M., Abewa, M. (2021). STBC-assisted OFDM with Subcarrier Power Modulation. RS Open Journal on Innovative Communication Technologies, 2(4). https://doi.org/10.46470/03d8ffbd.275ae770

[23] Abewa, M., Hamamreh, J. M. (2021). NC-OFDM-SPM: A Two-Dimensional Non-Coherent Modulation Scheme for Achieving the Coherent Performance of OFDM along with Sending an Additional Data-stream. RS Open Journal on Innovative Communication Technologies, 2(3). https://doi.org/10.46470/03d8ffbd.a97a5236

Comments
0
comment
No comments here
Why not start the discussion?