A tensor-based approach for frequency-selective MIMO channel equalization
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Artificial Intelligence,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing,Software
Link
https://link.springer.com/content/pdf/10.1007/s11045-023-00884-w.pdf
Reference45 articles.
1. Abe, T., & Matsumoto, T. (2003). Space-time turbo equalization in frequency-selective MIMO channels. IEEE Transactions on Vehicular Technology, 52(3), 469–475. https://doi.org/10.1109/TVT.2003.813227
2. Amin, M. B., Zirwas, W., & Haardt, M. (2017). HOSVD-based denoising for improved channel prediction of weak massive MIMO channels. In IEEE vehicular technology conference (Vol. 2017-June). https://doi.org/10.1109/VTCSpring.2017.8108241
3. Araújo, D. C., & De Almeida, A. L. F. (2018a). Tensor-Based compressed estimation of frequency-selective mmWave MIMO channels. In 2017 IEEE 7th international workshop on computational advances in multi-sensor adaptive processing, CAMSAP 2017 (Vol. 2017-Decem, pp. 1–5). IEEE. https://doi.org/10.1109/CAMSAP.2017.8313186
4. Araujo, D. C., De Almeida, A. L. F., Da Costa, J. P. C. L., & De Sousa, R. T. (2019). Tensor-based channel estimation for massive MIMO-OFDM systems. IEEE Access, 7, 42133–42147. https://doi.org/10.1109/ACCESS.2019.2908207
5. Choi, J. W., Shim, B., Singer, A. C., & Cho, N. I. (2010). Low-complexity decoding via reduced dimension maximum-likelihood search. IEEE Transactions on Signal Processing, 58(3), 1780–1793. https://doi.org/10.1109/TSP.2009.2036482
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