Review on Channel Estimation in OFDM
Keywords:
OFDM, MIMO, Space Time Trellis Code, Frequency Index Modulation, Compressed Sensing (CS), Channel Estimation.Abstract
OFDM is a wireless connectivity technique that sends multiple data streams over a particular channel while efficiently handling inter-symbol interference and boosting the frequency and bandwidth available. Since the antenna is used for signal transmission, predicting the noise present in a noisy channel is essential. In noisy channels, the evaluation method for estimating channel can be used to explore the impact of noise on transmitted signal. Orthogonal frequency division multiplexing (OFDM) is important in wireless communication for its elevated transmission rate. Thus this paper is based on the analysis of Orthogonal frequency division multiplexing and modulation techniques in multiple input multiple output (MIMO) user.
References
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
Zheng, B., & Zhang, R. (2020). Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization. IEEE Wireless Communications Letters, 9(4), 518–522. https://doi.org/10.1109/LWC.2019.2961357
Jaradat, A. M., Hamamreh, J. M., & Arslan, H. (2020). OFDM with hybrid number and index modulation. IEEE Access, 8, 55042–55053. https://doi.org/10.1109/ACCESS.2020.2982088
Yang, Z., Wu, B., Zheng, K., Wang, X., & Lei, L. (2016). A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access, 4, 3273–3287. https://doi.org/10.1109/ACCESS.2016.2573314
Jaradat, A. M., Hamamreh, J. M., & Arslan, H. (2019). Modulation Options for OFDM-Based Waveforms: Classification, Comparison, and Future Directions. IEEE Access, 7(1), 17263–17278. https://doi.org/10.1109/ACCESS.2019.2895958
Felix, A., Cammerer, S., Dorner, S., Hoydis, J., & Ten Brink, S. (2018). OFDM-Autoencoder for End-to-End Learning of Communications Systems. IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, 2018-June. https://doi.org/10.1109/SPAWC.2018.8445920
Wen, M., Li, Q., Basar, E., & Zhang, W. (2018). Generalized multiple-mode OFDM with index modulation. IEEE Transactions on Wireless Communications, 17(10), 6531–6543. https://doi.org/10.1109/TWC.2018.2860954
Jawhar, Y. A., Audah, L., Taher, M. A., Ramli, K. N., Shah, N. S. M., Musa, M., & Ahmed, M. S. (2019). A Review of Partial Transmit Sequence for PAPR Reduction in the OFDM Systems. IEEE Access, 7(1), 18021–18041. https://doi.org/10.1109/ACCESS.2019.2894527
Baquero Barneto, C., Riihonen, T., Turunen, M., Anttila, L., Fleischer, M., Stadius, K., Ryynänen, J., & Valkama, M. (2019). Full-Duplex OFDM Radar with LTE and 5G NR Waveforms: Challenges, Solutions, and Measurements. IEEE Transactions on Microwave Theory and Techniques, 67(10), 4042–4054. https://doi.org/10.1109/TMTT.2019.2930510
Balevi, E., & Andrews, J. G. (2019). One-Bit OFDM Receivers via Deep Learning. IEEE Transactions on Communications, 67(6), 4326–4336. https://doi.org/10.1109/TCOMM.2019.2903811
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Copyright (c) 2021 Vimlesh Gour, Mr. Kamal Niwaria, Dr. Bharti Chourasia

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