Abstract
Nowadays, vast amounts of data transmission and data retrieval are crucial and are done using many ways. Orthogonal Frequency-Division Multiplexing (OFDM) is one of the efficient ways to transmit data with the help of orthogonal sub-carriers, which is used in applications such as WiFi, WiMax, and cellular communication. In this paper, instead of conventional detection techniques, machine learning (ML)-based methods are adopted to detect the symbols after data is being received through the Additive White Gaussian channel (AWGN). Detection is one of the areas in which the bit error rate (BER) performance of the OFDM system can be improved. Machine learning algorithms only depend on the training data to predict the outputs; hence, we can detect the symbol even without the use of cyclic prefix or channel estimation which can save a lot of time and data if the input data is large. A comparative study on the performance of receivers based on K-means clustering, k-nearest neighbors classifier, support vector machine, linear discriminant analysis, and quadratic discriminant analysis is done. The modulation techniques such as BPSK and QAM with various modulation orders ranging from 4 to 64 are used in this analysis. Performance comparison of aforementioned detection techniques using employing machine learning is done using BER vs signal-to-noise ratio (SNR) in the range of 0–30 dB.
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References
A.Y. Reddy, B.L. Reddy, A. S. Naga Veera Sai, MSE and BER analysis of text, audio and image transmission using ML based OFDM, IEEE International Conference for Innovation in Technology (INOCON). BANGLURU, 1–3 (2020). https://doi.org/10.1109/INOCON50539.2020.9298204
D. Chen, Y. Tian, D. Qu, T. Jiang, OQAM-OFDM for wireless communications in future Internet of Things: a survey on key technologies and challenges. IEEE Internet Things J. 5(5), 3788–3809 (2018). https://doi.org/10.1109/JIOT.2018.2869677
C.A. Origanti, C. Naveen, M.S. Sai Bhargav, MSE and BER analysis of OFDM in Rician channel, 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184) (2020), pp. 877–881. https://doi.org/10.1109/ICOEI48184.2020.9142941.
J.S. Raj, Machine learning implementation in cognitive radio networks with game-theory technique. J. IRO J. Sustain. Wireless Syst. (2), 68–75 (2020)
B. Tegin, T.M. Duman, Machine Learning at Wireless Edge with OFDM and Low Resolution ADC and DAC (2020) . arXiv preprint arXiv:2010.00350
V.K.R. Devana, A.M. Rao (2020) A compact 3.1-18.8 GHz triple band notched UWB antenna for mobileUWB applications. IRO J. Sustain. Wireless Syst. 2(1), 1–12
Y. Kim, H. Lee, J. Ahn, J. Chung, Selection of CDMA and OFDM using machine learning in underwater wireless networks. ICT Express. 5 (2019). https://doi.org/10.1016/j.icte.2019.09.002
Y. Zhang, J. Li, Y. Zakharov, X. Li, J. Li, Deep learning based underwater acoustic OFDM communications. Appl. Acoustics 154, 53–58 (2019)
H. Ye, G. Y. Li, B. Juang, Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wireless Commun. Lett. 7(1), 114–117 (2018). https://doi.org/10.1109/LWC.2017.2757490
Z. Zhao, M.C. Vuran, F. Guo, S. Scott, Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks (2018). arXiv preprint arXiv:1810.07181
K. Anuraj, S.S. Poorna, S. Jeyasree, V. Sreekumar, C.A. Origanti, Comparative study of spatial modulation and OFDM using QAM symbol mapping, 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India (2019), pp. 1246-1249. https://doi.org/10.1109/ICCS45141.2019.9065516
X. Zhang, Y. Su, G. Tao, Signal detection technology research of MIMO-OFDM system, in 2010 3rd International Congress on Image and Signal Processing, Yantai, China (2010), pp. 3031–3034, https://doi.org/10.1109/CISP.2010.5648241
C.A. origanti, K. Anuraj, S. S. Poorna, M. Gokul Krishnan, K. Greeshmanth, Performance comparison of orthogonal frequency division multiplexing in White Gaussian and Rayleigh channels, in 2020 Fourth International Conference on Inventive Systems and Control (ICISC), Coimbatore, India (2020), pp. 712–715. https://doi.org/10.1109/ICISC47916.2020.9171194
I. Mesecan, I.ö. Bucak, Searching the effects of image scaling for underground object detection using KMeans and KNN, in 2014 European Modelling Symposium, Pisa (2014), pp. 180–184, https://doi.org/10.1109/EMS.2014.64
J.P. Zhang, Z.W. Li, J. Yang, A parallel SVM training algorithm on large-scale classification problems, in 2005 International Conference on Machine Learning and Cybernetics, vol. 3 (2005) pp. 1637–1641. IEEE
M.M. Amiri D. Gündüz, Federated learning over wireless fading channels, in IEEE Transactions on Wireless Communications, vol. 19, no. 5 (2020), pp. 3546–3557. https://doi.org/10.1109/TWC.2020.2974748
X. Zhang, Y. Su, G. Tao, Signal detection technology research of MIMO-OFDM system, in 2010 3rd International Congress on Image and Signal Processing, IEEE, vol. 7 (2010), pp. 3031–3034
K. Puntsri, E. Khansalee, Experimental comparison of OFDM SC-FDM and PAM for low speed optical wireless communication systems, in 2019 7th International Electrical Engineering Congress (iEECON) (2019), pp. 1-4. https://doi.org/10.1109/iEECON45304.2019.8938969
A. Agarwal, B.S. Kumar, K. Agarwal, BER performance analysis of image transmission using OFDM technique in different channel conditions using various modulation techniques, in Computational Intelligence in Data Mining. Springer, Singapore, pp. 1–8
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Seeram, S.S.S.G., Reddy, A.Y., Basil, N.J., Suman, A.V.S., Anuraj, K., Poorna, S.S. (2022). Performance Comparison of Machine Learning Algorithms in Symbol Detection Using OFDM. In: Ranganathan, G., Fernando, X., Shi, F. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 311. Springer, Singapore. https://doi.org/10.1007/978-981-16-5529-6_36
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