Learning Assisted Estimation for Time- Varying Channels
Abstract
Channel estimation is a critical module to determine the performance of wireless receivers. For some communication systems, the channels are time-varying and without well-justified models, e.g., underwater acoustic channels, high mobility channels, and mm Wave channels. These channels are usually hard to use finite parameters to estimate and track. Channel estimation in these cases may significantly affect the symbol detection performance. In this paper, we develop learning assisted (LA) channel estimation algorithms. We use CNN and DNN based channel estimators to track channel variations. We demonstrate that the estimators can be dynamically updated using pilots through incremental learning. Different from the existing channel estimators, our algorithms combine learning techniques with preamble training symbols and pilots, and thus can track channel variations on-line and fit better for the current cellular systems, vehicular communications, and underwater acoustic systems. Simulation results validate the effectiveness of our algorithms.
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Intelligent media/contents prediction has applications for enabling low latency communication in remote regions, including intelligent caching and transfer caching at nodes in aeroplanes and ships for mesh-networks based airborne internet and oceanic broadband.
“Intelligent media/contents prediction has various other applications for enabling low latency communication in remote regions; e.g., intelligent caching and transfer caching at the nodes level in aeroplanes and ships for mesh-networks based airborne internet and oceanic broadband applications [76], respectively.”
From Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) · cites this paper as [76]
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Microwave Mobile Communications
Jakes, W. C. (1994) · Institute of Electrical and Electronics Engineers eBooks · doi:10.1109/9780470545287 · Cited by 9,799 · see citing works
Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems
Ye, H., Li, G. Y., & Juang, B.-H. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2017.2757490 · Cited by 2,000 · see citing works
Self-citationAn analysis of pilot symbol assisted modulation for Rayleigh fading channels (mobile radio)
Cavers, J. K. (1991) · IEEE Transactions on Vehicular Technology · doi:10.1109/25.108378 · Cited by 1,340 · see citing works
CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach
Wang, X., Gao, L., Mao, S., & Pandey, S. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2545523 · Cited by 1,111 · see citing works
Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems
He, H., Wen, C.-K., Jin, S., & Li, G. Y. (2018) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2018.2832128 · Cited by 823 · see citing works
Deep learning for wireless physical layer: Opportunities and challenges
Wang, T., Wen, C.-K., Wang, H., Gao, F., Jiang, T., & Jin, S. (2017) · China Communications · doi:10.1109/cc.2017.8233654 · Cited by 606 · see citing works
Pilot-Assisted Time-Varying Channel Estimation for OFDM Systems
Tang, Z., Cannizzaro, R. C., Leus, G. J. T., & Banelli, P. (2007) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.893198 · Cited by 448 · see citing works
Optimal insertion of pilot symbols for transmissions over time-varying flat fading channels
Dong, M., Tong, L., & Sadler, B. M. (2004) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2004.826182 · Cited by 222 · see citing works
Measurements on time-variant communication channels
Kailath, T. (1962) · IEEE Transactions on Information Theory · doi:10.1109/tit.1962.1057748 · Cited by 141 · see citing works
Average-rate optimal PSAM transmissions over time-selective fading channels
Ohno, S., & Giannakis, G. B. (2002) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2002.804183 · Cited by 116 · see citing works
On the transfer function calculus for underspread LTV channels
Kozek, W. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.552218 · Cited by 79 · see citing works
Optimal training for block transmissions over doubly-selective fading channels
Ma, X., Giannakis, G. B., & Ohno, S. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5744900 · Cited by 68 · see citing works
Self-citationAdaptive channel estimator for an HF radio link
Clark, A. P., & Hariharan, S. (1989) · IEEE Transactions on Communications · doi:10.1109/26.35371 · Cited by 28 · see citing works
Wu, B., Yang, M., Li, K., Huang, Z., Siniscalchi, S. M., Wang, T., & Lee, C. (2017) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-017-0516-6 · Cited by 14 · see citing works
A performance analysis and design of equalization with pilot aided channel estimation
Zhang, Y., Fitz, M. P., & Gelfand, S. B. (2002) · IEEE Vehicular Technology Conference (VTC) · doi:10.1109/vetec.1997.600423 · Cited by 10 · see citing works
Flagged first, then by citations
3 of the 19 references couldn’t be matched to a known paper.
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Zappone, A., Renzo, M. D., & Debbah, M. A. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2924010 · Cited by 680 · see citing works
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658 · see citing works
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Yang, Y., Gao, F., Ma, X., & Zhang, S. (2019) · IEEE Access · doi:10.1109/access.2019.2901066 · Cited by 259 · see citing works
Self-citationDeep Learning-Based Channel Estimation Algorithm Over Time Selective Fading Channels
Bai, Q., Wang, J., Zhang, Y., & Song, J. (2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2943455 · Cited by 151 · see citing works
Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction
Yang, Y., Gao, F., Xing, C., An, J., & Alkhateeb, A. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3041383 · Cited by 64 · see citing works
Deep Learning Based Single Carrier Communications Over Time-Varying Underwater Acoustic Channel
Zhang, Y., Li, J., Zakharov, Y. V., Li, J., Li, Y., Lin, C., & Li, X. (2019) · IEEE Access · doi:10.1109/access.2019.2906424 · Cited by 59 · see citing works
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Gizzini, A. K., & Chafii, M. (2022) · IEEE Access · doi:10.1109/access.2022.3188111 · Cited by 55 · see citing works
Channel Estimation Enhancement With Generative Adversarial Networks
Hu, T., Huang, Y., Zhu, Q., & Wu, Q. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.3013257 · Cited by 50 · see citing works
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Yang, Y., Zhang, S., Gao, F., Ma, J., & Dobre, O. A. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2020.2990487 · Cited by 49 · see citing works
Jain, P., Gupta, A., & Kumar, N. (2022) · Physical Communication · doi:10.1016/j.phycom.2022.101917 · Cited by 49 · see citing works
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Essai, M. H., & Taha, I. B. M. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.682 · Cited by 39 · see citing works
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Zhang, J., Ma, X., Qi, J., & Jin, S. (2021) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2021.3051490 · Cited by 35 · see citing works
Self-citationDulaj, K., Alhammadi, A., Shayea, I. A. M., El‐Saleh, A. A., & Alnakhli, M. A. (2025) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2025.3564361 · Cited by 30 · see citing works
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Liu, S., Wang, T., & Wang, S. (2021) · Digital Communications and Networks · doi:10.1016/j.dcan.2021.09.014 · Cited by 26 · see citing works
Ge, Y., Zhang, W., Gao, F., Zhang, S., & Ma, X. (2020) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3005470 · Cited by 18 · see citing works
Self-citationUmar, A., Khalid, Z., Ali, M., Abazeed, M. B., Alqahtani, A., Ullah, R., & Safdar, H. (2023) · Applied Sciences · doi:10.3390/app132212384 · Cited by 16 · see citing works
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Liu, J., Ji, F., Zhao, H., Li, J., & Wen, M. (2021) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2021-fall52928.2021.9625222 · Cited by 17 · see citing works
Sowjanya, P. S., & Satyanarayana, P. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02010-1 · Cited by 15 · see citing works
RetractedAlghamdi, F., & Bajaber, F. (2025) · Sensors · doi:10.3390/s25072121 · Cited by 11 · see citing works
Zhou, M., Zhang, H., Lv, T., Huang, W. K., Duan, Y., & Gao, Y. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1331635 · Cited by 10 · see citing works
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Gizzini, A. K., Medjahdi, Y., Ghandour, A. J., & Clavier, L. (2025) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2025.3596548 · Cited by 9 · see citing works
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Essai, M. H., Alraddady, F. A., Al-Thunaibat, M. Y., & Elnazer, S. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.022246 · Cited by 7 · see citing works
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Wei, H., Wang, J., Chen, X., Peng, Q., Zha, Y., & Li, L. (2023) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2023.3313808 · Cited by 4 · see citing works
Awad, S. D., Sali, A. B., Al-Wani, M. M., Al-Saegh, A. M., Mandeep, J. S., & Abdullah, R. S. A. R. (2022) · Computer Networks · doi:10.1016/j.comnet.2023.110022 · Cited by 4 · see citing works
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Cao, Y. S., Sun, B., & Tsang, D. H. K. (2020) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops49005.2020.9145135 · Cited by 3 · see citing works
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Yang, G., He, G., Ruirong, C. H. E. N., Zhang, Z., Yang, Z., Liu, R., Tao, Y. A. N. G., Chen, D., Su, X., Sun, S., et al. (2024) · Scientia Sinica Informationis · doi:10.1360/ssi-2023-0331 · Cited by 3 · see citing works
Self-citationJoodaki, S., Turbić, K., Sezgin, A., & Gacanin, H. (2023) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc56721.2023.10293887 · Cited by 2 · see citing works
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Zhou, Y., Wang, Y., Gui, G., Gacanin, H., & Sari, H. (2020) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp49889.2020.9299767 · Cited by 2 · see citing works
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Hu, Z. (2023) · Electronics Letters · doi:10.1049/ell2.12965 · Cited by 1 · see citing works
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Gao, X., & Peng, T. (2023) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc57788.2023.10233635 · Cited by 1 · see citing works
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Karunakaran, S., Shanthind, D., Babu, V., & Aaqib, S. M. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.12.1132 · Cited by 1 · see citing works
RetractedYang, L., Ren, L., Shao, Y., & Chang, A. (2023) · Research Square · doi:10.21203/rs.3.rs-2785633/v1 · Cited by 1 · see citing works
Gamal, D., Elsayed, K., & Mehana, A. H. (2023) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4773
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- Authors
- Ma, X., Ye, H., & Li, Y.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- 2018 15th International Symposium on Wireless Communication Systems (ISWCS)
- Publisher
- IEEE
- Published
- 2018-08
- Pages
- 1-5
- OpenAlex
- W2897036932
- Cited by
- 53
- References
- 19
- Citations by year
- 2025: 4 · 2024: 6 · 2023: 7 · 2022: 8 · 2021: 14 · 2020: 9 · 2019: 5
- FWCI
- 11.17
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Symposium on Wireless Communication Systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
- Keywords
- channel estimation, time-varying channels, incremental learning, convolutional neural networks, deep neural network, underwater acoustic channel, vehicular communication, wireless receivers
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