A deep learning long short-term memory model for reduction of inter-carrier interference in orthogonal frequency division multiplexing systems
Wireless network performance depends extensively on the carrier interference. Inter-carrier interference (ICI) can be caused by several factors, including carrier frequency offsets, Doppler spread due to channel time variation, and sampling frequency offsets, which degrade the performance of an orthogonal frequency division multiplexing (OFDM) system. Hence, reducing ICI is a major task in communication systems. The lower the ICI, the higher the performance of the OFDM system. Moreover, the application of deep learning has demonstrated significant improvements in communication reliability and reduced the computational complexity of 5G and subsequent networks. Deep learning combined with ICI self-cancellation techniques yields promising results. In this paper, a hybrid framework leveraging mirror-based techniques is developed to enhance data robustness and mitigate interference while using deep learning models for adaptive, real-time ICI prediction and suppression. We incorporated multiple symbol rates and multi-carrier vector transmission at the physical layer to improve signal resilience against channel imperfections. These techniques were used in conjunction with deep learning models such as long short-term memory (LSTM) to predict ICI patterns and dynamically adjust mirror-based symbol-repetition parameters to optimize signal quality. The LSTM was incorporated with an attention mechanism. Improved interference cancellation was observed through the combined strength of symbol repetition and adaptive neural network-based interference prediction. The performance of the proposed work was evaluated using two modulation techniques: quadrature phase-shift keying and binary phase-shift keying. The results for bit error rate and carrier-to-interference ratio were best with ICI self-cancellation combined with LTSM. In summary, the proposed approach improves OFDM system performance by eliminating ICI and enhancing signal quality.
Bai, S., Kilby, J., & Prasad, K. (2026). Deep Learning-Based Channel Estimation Techniques Using IEEE 802.11p Protocol, Limitations of IEEE 802.11p and Future Directions of IEEE 802.11bd: A Review. Sensors, 26(5), 1658. https://doi.org/10.3390/s26051658
Basholli, F., Hayal, M. R., Elsayed, E. E., & Juraev, D. A. (2025). Deep Learning for Skin Disease Classification: A Comparative Study of CNN and CNN-LSTM Architectures. Journal of Computing and Data Technology, 1(1), 40–49. https://doi.org/10.71426/jcdt.v1.i1.pp40-49
Bazzi, A., Slock, D. T. M., & Meilhac, L. (2015). Efficient Maximum Likelihood Joint Estimation of Angles and Times of Arrival of Multiple Paths. In: 2015 IEEE GlobeCom Workshops (GC Workshops) (pp. 1–7). IEEE. 2015 IEEE GlobeCom Workshops (GC Workshops). https://doi.org/10.1109/glocomw.2015.7414203
Dintakurthy, Y., Innmuri, R. K., Vanteru, A., & Thotakuri, A. (2025). Emerging Applications of Artificial Intelligence in Edge Computing: A Comprehensive Review. Journal of Modern Technology, 175–185. https://doi.org/10.71426/jmt.v1.i2.pp175-185
Essai Ali, M. H. (2020). Deep learning‐based pilot‐assisted channel state estimator for OFDM systems. IET Communications, 15(2), 257–264. https://doi.org/10.1049/cmu2.12051
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232. https://doi.org/10.1109/tnnls.2016.2582924
Huang, H., Guo, S., Gui, G., Yang, Z., Zhang, J., Sari, H., & Adachi, F. (2020). Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions. IEEE Wireless Communications, 27(1), 214–222. https://doi.org/10.1109/mwc.2019.1900027
Jdid, B., Hassan, K., Dayoub, I., Lim, W. H., & Mokayef, M. (2021). Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey. IEEE Access, 9, 57851–57873. https://doi.org/10.1109/access.2021.3071801
Krishnama Raju, A., Gupta, S., & Jaiswal, A. (2022). An Efficient Deep Neural Networks-Based Channel Estimation and Signal Detection in OFDM Systems. In: Lecture Notes in Networks and Systems (pp. 603–613). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-6246-1_51
Khan, I., Zafar, M. H., Ashraf, M., & Kim, S. (2018). Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems. Electronics, 7(12), 382. https://doi.org/10.3390/electronics7120382
Le, H. A., Van Chien, T., Nguyen, T. H., Choo, H., & Nguyen, V. D. (2021). Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems. Sensors, 21(14), 4861. https://doi.org/10.3390/s21144861
Li, L., Chen, H., Chang, H. H., & Liu, L. (2020). Deep Residual Learning Meets OFDM Channel Estimation. IEEE Wireless Communications Letters, 9(5), 615–618. https://doi.org/10.1109/lwc.2019.2962796
Logins, A., He, J., & Paramonov, K. (2022). Block-Structured Deep Learning-Based OFDM Channel Equalization. IEEE Communications Letters, 26(2), 321–324. https://doi.org/10.1109/lcomm.2021.3133018
Ly, A., & Yao, Y. D. (2021). A Review of Deep Learning in 5G Research: Channel Coding, Massive MIMO, Multiple Access, Resource Allocation, and Network Security. IEEE Open Journal of the Communications Society, 2, 396–408. https://doi.org/10.1109/ojcoms.2021.3058353
Mei, K., Liu, J., Zhang, X., Cao, K., Rajatheva, N., & Wei, J. (2021). A Low Complexity Learning-Based Channel Estimation for OFDM Systems With Online Training. IEEE Transactions on Communications, 69(10), 6722–6733. https://doi.org/10.1109/tcomm.2021.3095198
Miao, P., Chen, G., Cumanan, K., Yao, Y., & Chambers, J. A. (2022). Deep Hybrid Neural Network-Based Channel Equalization in Visible Light Communication. IEEE Communications Letters, 26(7), 1593–1597. https://doi.org/10.1109/lcomm.2022.3172219
Mthethwa, B., & Xu, H. (2020). Deep Learning-Based Wireless Channel Estimation for MIMO Uncoded Space-Time Labeling Diversity. IEEE Access, 8, 224608–224620. https://doi.org/10.1109/access.2020.3044097
Nair, A. K., & Menon, V. (2022). Joint Channel Estimation and Symbol Detection in MIMO-OFDM Systems: A Deep Learning Approach using Bi-LSTM. In: Proceedings of 2022 14th International Conference on Communication Systems & Networks (COMSNETS) (pp. 406–411). IEEE. https://doi.org/10.1109/comsnets53615.2022.9668456
Reddy, S. R. S., Akshaya, G. N., Koteswari, O. L., Sreeja, T., & Edara, V. S. (2025). Leveraging Sentiment Analysis in the Digital Era: Uncovering Insights from Unstructured Data for Enhanced Customer Engagement. Journal of Modern Technology, 02(01), 212–219. https://doi.org/10.71426/jmt.v2.i1.pp212-219
Shamasundar, B., & Chockalingam, A. (2019). A DNN Architecture for the Detection of Generalized Spatial Modulation Signals (Version 2). arXiv. https://doi.org/10.48550/ARXIV.1910.01948
Soma, A. k. (2024). Hybrid RNN-GRU-LSTM Model for Accurate Detection of DDoS Attacks on IDS Dataset. Journal of Modern Technology, 2(1), 283–291. https://doi.org/10.71426/jmt.v2.i1.pp283-291
Wang, S., Yao, R., Tsiftsis, T. A., Miridakis, N. I., & Qi, N. (2020). Signal Detection in Uplink Time-Varying OFDM Systems Using RNN With Bidirectional LSTM. IEEE Wireless Communications Letters, 9(11), 1947–1951. https://doi.org/10.1109/lwc.2020.3009170
Ye, H., Li, G. Y., & Juang, B. H. (2018). Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems. IEEE Wireless Communications Letters, 7(1), 114– 117. https://doi.org/10.1109/lwc.2017.2757490