Symbol Classification in Receiver of OFDM Carrier Signal with Deep Learning and Whale Optimization Algorithm

Yıl 2025, ERKEN GÖRÜNÜM, 1 - 1

Ali Hander , Bilgehan Erkal , Javad Rahebi

https://doi.org/10.2339/politeknik.1664072

Öz

Orthogonal Frequency Division Multiplexing (OFDM) remains a cornerstone in modern wireless communication systems, owing to its resilience to multipath fading and spectral efficiency. In OFDM systems, accurate symbol classification is paramount for successful data demodulation. This paper proposes a novel methodology for symbol classification in the receiver of an OFDM carrier signal, using a synergic combination of deep learning and feature selection with the Whale Optimization Algorithm (WOA). The deep learning component, embodied in a convolutional neural network (CNN), is adept at extracting intricate features from the received OFDM symbols, while the WOA facilitates efficient feature selection by optimizing a subset of attributes that contribute most to categorization accuracy. This dual approach not only enhances the discriminative power of the classification model but also reduces the computational complexity by focusing on the most relevant features. Experimental findings confirm the effectiveness of the proposed framework, demonstrating superior symbol classification performance compared to conventional methods. Moreover, the integration of feature selection with the WOA ensures the identification of an optimal subset of features, further improving classification accuracy and generalization capability. This study combines DL with metaheuristic feature selection to improve symbol classification in OFDM receivers, thereby making wireless communication systems more reliable and efficient.

Anahtar Kelimeler

Symbol Classification , OFDM , Deep learning , Wale optimization algorithm

Derin Öğrenme ve Balina Optimizasyon Algoritması ile OFDM Taşıyıcı Sinyal Alıcıda Sembol Sınıflandırma

Öz

Ortogonal Frekans Bölmeli Çoğullama (OFDM), çok yollu sönümlemeye karşı dayanıklılığı ve spektral verimliliği sayesinde modern kablosuz iletişim sistemlerinin temel taşlarından biri olmaya devam etmektedir. OFDM sistemlerinde, başarılı veri demodülasyonu için doğru sembol sınıflandırması çok önemlidir. Bu makale, Balina Optimizasyon Algoritması (WOA) ile derin öğrenme ve özellik seçiminin sinerjik bir kombinasyonunu kullanarak bir OFDM taşıyıcı sinyalinin alıcısında sembol sınıflandırması için yeni bir metodoloji önermektedir. Bir evrişimli sinir ağında (CNN) somutlaşan derin öğrenme bileşeni, alınan OFDM sembollerinden karmaşık özellikleri çıkarmada ustalaşırken, WOA, kategorizasyon doğruluğuna en çok katkıda bulunan bir alt özellik kümesini optimize ederek verimli özellik seçimini kolaylaştırır. Bu ikili yaklaşım sadece sınıflandırma modelinin ayırt edici gücünü artırmakla kalmaz, aynı zamanda en ilgili özelliklere odaklanarak hesaplama karmaşıklığını da azaltır. Deneysel bulgular, geleneksel yöntemlere kıyasla üstün sembol sınıflandırma performansı göstererek önerilen çerçevenin etkinliğini doğrulamaktadır. Ayrıca, özellik seçiminin WOA ile entegrasyonu, optimum özellik alt kümesinin belirlenmesini sağlayarak sınıflandırma doğruluğunu ve genelleme yeteneğini daha da geliştirmektedir. Bu çalışma, OFDM alıcılarında sembol sınıflandırmasını iyileştirmek için DL ile metasezgisel özellik seçimini birleştirerek kablosuz iletişim sistemlerinin daha güvenilir ve verimli olmasını sağlar.

Anahtar Kelimeler

Sembol Sınıflandırma , OFDM , Derin öğrenme , Wale optimizasyon algoritması

Kaynakça

• [1] A. Hamdan, “Multicarrier Communication over Fast Fading Mobile Channels: Interference Analysis, Equalization, and Channel Estimation.” Université Grenoble Alpes [2020-....], (2023).
• [2] I. Khan, M. Cheffena, and M. M. Hasan, “Data aided channel estimation for MIMO-OFDM wireless systems using reliable carriers,” IEEE Access, 3(11):47836–47847, (2023).
• [3] H. Li, S. Qiao, and Y. Sun, “A depth graph attention-based multi-channel transfer learning network for fluid classification from logging data,” Physics Fluids, 36(10):, (2024).
• [4] E. Yaghoubi, E. Yaghoubi, A. Khamees, and A. H. Vakili, “A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering,” Neural Computing and Applications 1–45, (2024).
• [5] S. R. Doha and A. Abdelhadi, “Deep Learning in Wireless Communication Receiver: A Survey,” arXiv Prepr. arXiv2501.17184, (2025).
• [6] N. L. Rane, M. Paramesha, S. P. Choudhary, and J. Rane, “Machine learning and deep learning for big data analytics: A review of methods and applications,” Partners Univers. International Innovation Journal, 2(3):172–197, (2024).
• [7] A. Kumar, S. Majhi, G. Gui, H.-C. Wu, and C. Yuen, “A survey of blind modulation classification techniques for OFDM signals,” Sensors, 22(3):1020, (2022).
• [8] H.-H. Tseng, Y.-F. Chen, and S.-M. Tseng, “Hybrid Beamforming and Resource Allocation Designs for mmWave Multi-User Massive MIMO-OFDM Systems on Uplink,” IEEE Access, 3(11):133070–133085, (2023).
• [9] S. Singh, S. Kumar, S. Majhi, U. Satija, and C. Yuen, “Blind Carrier Frequency Offset Estimation Techniques for Next-Generation Multicarrier Communication Systems: Challenges, Comparative Analysis, and Future Prospects,” IEEE Communication Survey Tutorials, (2024).
• [10] B. M. R. Manasa and P. Venugopal, “A systematic literature review on channel estimation in MIMO-OFDM system: Performance analysis and future direction,” Journal of Optic Communication, 45(3):589–614, (2024).
• [11] S. B. Meshram and S. V Rathkanthiwar, “An Overview: Peak-to-Average Power Ratio Reduction in OFDM System Using Block Coding Technique,” International Journal Engineering Innovation Research, 2(1):63, (2013).
• [12] N. Q. M. Adnan, A. A. A. Wahab, S. Muniandy, S. S. N. Alhady, and W. A. F. W. Othman, “Partial Transmit Sequence (PTS) Optimization Using Improved Harmony Search (IHS) Algorithm for PAPR Reduction in OFDM,” in Symposium on Intelligent Manufacturing and Mechatronics, 260–274, (2021).
• [13] L. Lanante, C. Ghosh, and S. Roy, “Hybrid OFDMA random access with resource unit sensing for next-gen 802.11 ax WLANs,” IEEE Transaction Mobile Computer, 20(12):3338–3350, (2020).
• [14] S. Weinstein and P. Ebert, “Data transmission by frequency-division multiplexing using the discrete Fourier transform,” IEEE Trans. Commun. Technol., 19(5):628–634, (1971).
• [15] X. Zhang, Z. Luo, W. Xiao, and L. Feng, “Deep Learning-Based Modulation Recognition for MIMO Systems: Fundamental, Methods, Challenges,” IEEE Access, (2024).
• [16] A. M. Alsefri, “DEVICE-TO-DEVICE CONTINUOUS AUTHENTICATION USING MACHINE LEARNING FOR THE INTERNET OF THINGS,” (2023).
• [17] W. Liu, Z. Guo, F. Jiang, G. Liu, D. Wang, and Z. Ni, “Improved WOA and its application in feature selection,” PLoS One, 17(5):e0267041, (2022).
• [18] R. Saiyyed, M. Sindhwani, N. K. Mishra, H. Pahuja, S. Sachdeva, and M. K. Shukla, “Synergizing intelligent signal processing with wavelength-division multiplexing for enhanced efficiency and speed in photonic network communications,” J. Opt. Commun. 3(10):, (2024).
• [19] C. Edwin Singh and S. M. Celestin Vigila, “WOA-DNN for Intelligent Intrusion Detection and Classification in MANET Services.,” Intell. Autom. Soft Comput., 35(2):, (2023).
• [20] E. Yaghoubi, E. Yaghoubi, Z. Yusupov, and M. R. Maghami, “A Real-Time and Online Dynamic Reconfiguration against Cyber-Attacks to Enhance Security and Cost-Efficiency in Smart Power Microgrids Using Deep Learning,” Technologies, 12(10):197, (2024).
• [21] C. Silpa, A. Vani, and K. R. Naidu, “Optimized deep learning based hypernet convolution neural network and long short term memory for joint pilot design and channel estimation in MIMO‐OFDM model,” Trans. Emerg. Telecommun. Technol., 35(1):e4925, (2024).
• [22] L. Li, “Online Machine Learning for Wireless Communications: Channel Estimation, Receive Processing, and Resource Allocation.” Virginia Polytechnic Institute and State University, (2023).
• [23] A. M. Jaradat, J. M. Hamamreh, and H. Arslan, “Modulation options for OFDM-based waveforms: Classification, comparison, and future directions,” IEEE Access, 3(7):17263–17278, (2019).
• [24] H. Dahrouj et al., “An overview of machine learning-based techniques for solving optimization problems in communications and signal processing,” IEEE Access, 4(9):74908–74938, (2021).
• [25] A. Shemyakin and A. Kniazev, Introduction to Bayesian estimation and copula models of dependence. John Wiley & Sons, (2017).
• [26] M. M. Zayed, S. Mohsen, A. Alghuried, H. Hijry, and M. Shokair, “IoUT-Oriented an Efficient CNN Model for Modulation Schemes Recognition in Optical Wireless Communication Systems,” IEEE Access, (2024).
• [27] E. Yaghoubi, E. Yaghoubi, A. Khamees, D. Razmi, and T. Lu, “A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior,” Eng. Appl. Artif. Intell., 3(135):108789, (2024).
• [28] W. Zhang, K. Xue, A. Yao, and Y. Sun, “CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network,” Electronics, 13(17):3408, (2024).
• [29] W. Hsieh et al., “Deep Learning, Machine Learning--Digital Signal and Image Processing: From Theory to Application,” arXiv Prepr. arXiv2410.20304, (2024).
• [30] A. Kumar, K. K. Srinivas, and S. Majhi, “Automatic modulation classification for adaptive OFDM systems using convolutional neural networks with residual learning,” IEEE Access, 2(11):61013–61024, (2023).
• [31] Z. Zhang, C. Wang, C. Gan, S. Sun, and M. Wang, “Automatic modulation classification using convolutional neural network with features fusion of SPWVD and BJD,” IEEE Trans. Signal Inf. Process. over Networks, 5(3):469–478, (2019).
• [32] M. Amiriebrahimabadi and N. Mansouri, “A comprehensive survey of feature selection techniques based on whale optimization algorithm,” Multimed. Tools Appl., 83(16):47775–47846, (2024).
• [33] J. LIU, Y. XIAN, and X. U. WANG, “Improved Deep Neural Network for OFDM Signal Recognition Using Hybrid Grey Wolf Optimization”.
• [34] Y. Zhang, D. Liu, J. Liu, Y. Xian, and X. Wang, “Improved deep neural network for OFDM signal recognition using hybrid grey wolf optimization,” IEEE Access, 4(8)133622–133632, (2020).
• [35] B. Wei, W. Zhang, X. Xia, Y. Zhang, F. Yu, and Z. Zhu, “Efficient feature selection algorithm based on particle swarm optimization with learning memory,” IEEE Access, 4(7):166066–166078, (2019).
• [36] A. Ahmad et al., “Toward modeling and optimization of features selection in Big Data based social Internet of Things,” Futur. Gener. Comput. Syst., 4(82):715–726, (2018).
• [37] H. Dong, T. Li, R. Ding, and J. Sun, “A novel hybrid genetic algorithm with granular information for feature selection and optimization,” Appl. Soft Comput., 5(65):33–46, (2018).
• [38] B. Xue, M. Zhang, and W. N. Browne, “Particle swarm optimization for feature selection in classification: A multi-objective approach,” IEEE Trans. Cybern., 43(6):1656–1671, (2012).
• [39] Y. Liu, G. Wang, H. Chen, H. Dong, X. Zhu, and S. Wang, “An improved particle swarm optimization for feature selection,” J. Bionic Eng., 8(2):191–200, (2011).
• [40] P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geosci. Remote Sens. Lett., 12(2):309–313, (2014).
• [41] S. Oreski and G. Oreski, “Genetic algorithm-based heuristic for feature selection in credit risk assessment,” Expert Syst. Appl., 41(4):2052–2064, (2014).
• [42] X. Yi and C. Zhong, “Deep learning for joint channel estimation and signal detection in OFDM systems,” IEEE Commun. Lett., 24(12):2780–2784, (2020).
• [43] N. A. Amran, M. D. Soltani, M. Yaghoobi, and M. Safari, “Deep learning based signal detection for OFDM VLC systems,” in 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 1–6, (2020).
• [44] A. Emir, F. Kara, H. Kaya, and X. Li, “Deep learning-based flexible joint channel estimation and signal detection of multi-user OFDM-NOMA,” Phys. Commun., 4(8):101443, (2021).
• [45] Y. Zhu, B. Wang, J. Li, Y. Zhang, and F. Xie, “Y-shaped net-based signal detection for OFDM-IM systems,” IEEE Commun. Lett., 26(11):2661–2664, (2022).
• [46] X. Zhou, J. Zhang, C.-K. Wen, J. Zhang, and S. Jin, “Model-driven deep learning-based signal detector for CP-free MIMO-OFDM systems,” in 2021 IEEE international conference on communications workshops (ICC workshops), 1–6., (2021).
• [47] J.-H. Ro, S.-J. Yu, Y.-H. You, S. K. Hong, and H.-K. Song, “An adaptive QR-based energy efficient signal detection scheme in MIMO-OFDM systems,” Comput. Commun., 14(9):225–231, (2020).
• [48] S. Wang, R. Yao, T. A. Tsiftsis, N. I. Miridakis, and N. Qi, “Signal detection in uplink time-varying OFDM systems using RNN with bidirectional LSTM,” IEEE Wirel. Commun. Lett., 9(11):1947–1951, (2020).
• [49] X. Chen, M. Liu, G. Gui, B. Adebisi, H. Gacanin, and H. Sari, “Complex deep neural network based intelligent signal detection methods for OFDM-IM systems,” in 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 90–94, (2021).
• [50] F. Shad, T. D. Todd, V. Kezys, and J. Litva, “Dynamic slot allocation (DSA) in indoor SDMA/TDMA using a smart antenna basestation,” IEEE/ACM Trans. Netw., 9(1):69–81, (2001).
• [51] X. Fang, “More realistic analysis for blocking probability in SDMA systems,” IEE Proceedings-Communications, 149(3):152–156, (2002).
• [52] C. M. Walke and T. J. Oechtering, “Analytical expression for uplink C/I-distribution in interference-limited cellular radio systems,” Electron. Lett., 38(14):743–744, (2002).
• [53] S. Thoen, L. Deneire, L. Van der Perre, M. Engels, and H. De Man, “Constrained least squares detector for OFDM/SDMA-based wireless networks,” IEEE Trans. Wirel. Commun., 2(1):129–140, (2003).
• [54] L. Hanzo, B. Choi, and T. Keller, OFDM and MC-CDMA for broadband multi-user communications, WLANs and broadcasting. John Wiley & Sons, (2005).
• [55] Q. H. Spencer, A. L. Swindlehurst, and M. Haardt, “Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels,” IEEE Trans. signal Process., 52(2):461–471, (2004).
• [56] X. Dai, “Carrier frequency offset estimation for OFDM/SDMA systems using consecutive pilots,” IEE Proceedings-Communications, 152(5):624–632, (2005).
• [57] J. Joung and A. H. Sayed, “User selection methods for multiuser two-way relay communications using space division multiple access,” IEEE Trans. Wirel. Commun., 9(7):2130–2136, (2010).
• [58] G. S. Dahman, R. H. M. Hafez, and R. J. C. Bultitude, “Angle-of-departure-aided opportunistic space-division multiple access for MIMO applications,” IEEE Trans. Wirel. Commun., 9(4):1303–1307, (2010).
• [59] C. He, X. Liang, B. Zhou, J. Geng, and R. Jin, “Space-division multiple access based on time-modulated array,” IEEE Antennas Wirel. Propag. Lett., 4(14):610–613, (2014).
• [60] S. Su, C. He, and L. Xu, “Quasi-Reflective Chaotic Mutant Whale Swarm Optimization Fused with Operators of Fish Aggregating Device,” Symmetry (Basel)., 14(4):829, (2022).