Channel temporal correlation-based optimization method for imperfect underwater acoustic channel state information

Radosevic, 2014, Adaptive OFDM modulation for underwater acoustic communications: Design considerations and experimental results, IEEE J. Ocean Eng., 39, 357, 10.1109/JOE.2013.2253212 Wan, 2015, Adaptive modulation and coding for underwater acoustic OFDM, IEEE J. Ocean Eng., 40, 327, 10.1109/JOE.2014.2323365 K. Pelekanakis, L. Cazzanti, On adaptive modulation for low SNR underwater acoustic communications, in: Proceedings of the Conf Oceans IEEE, Vol. 1, 2018, pp. 1–6. G. Ayela, M. Nicot, X. Lurton, New innovative multimodulation acoustic communication system, in: Proceedings of the Conf Oceans IEEE, Vol. 1, 1994, pp. l/292–l/295. Q. Fu, A. Song, Adaptive modulation for underwater acoustic communications based on reinforcement learning, in: Proceedings of the Conf Oceans IEEE, Vol. 1, 2018, pp. 1–8. Zhou, 2004, Adaptive modulation for multi-antenna transmissions with channel mean feedback, IEEE Trans. Wirel. Commun., 3, 1626, 10.1109/TWC.2004.833411 Zhang, 2016, Adaptive OFDMA with partial CSI for downlink underwater acoustic communications, J. Commun. Netw., 18, 387, 10.1109/JCN.2016.000054 Qiao, 2019, Adaptive downlink OFDMA system with low-overhead and limited feedback in time-varying underwater acoustic channel, IEEE Access, 7, 12729, 10.1109/ACCESS.2019.2892812 Lin, 2015, Prediction based sparse channel estimation for underwater acoustic OFDM, Appl. Acoust., 96, 94, 10.1016/j.apacoust.2015.03.018 M. Biagi, S. Rinauro, R. Cusani, Channel estimation or prediction for UWA, in: Proceedings of the Conf Oceans IEEE, Vol. 1, 2013, pp. 1–7. Sun, 2018, Online modeling and prediction of the large-scale temporal variation in underwater acoustic communication channels, IEEE Access, 6, 73984, 10.1109/ACCESS.2018.2882890 Y. Li, B. Li, Y. Zhang, A channel state information feedback and prediction scheme for time-varying underwater acoustic channels, 2018, pp. 1–6, Zhang, 2020, Efficient estimation and prediction for sparse time-varying underwater acoustic channels, IEEE J. Ocean Eng., 45, 1112, 10.1109/JOE.2019.2911446 Liu, 2021, Channel state information prediction for adaptive underwater acoustic downlink OFDMA system: Deep neural networks based approach, IEEE Trans. Veh. Technol., 70, 9063, 10.1109/TVT.2021.3099797 Zhang, 2022, Deep reinforcement learning-based adaptive modulation for underwater acoustic communication with outdated channel state information, Remote Sens., 14, 3947, 10.3390/rs14163947 S. Van Vaerenbergh, I. Santamaría, A comparative study of kernel adaptive filtering algorithms, in: Proc. 2013 IEEE Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), 2013, pp. 181–186. Engel, 2004, The kernel recursive least squares algorithm, IEEE Trans. Signal Process., 52, 2275, 10.1109/TSP.2004.830985 Berger, 2010, Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing, IEEE Trans. Signal Process., 58, 1708, 10.1109/TSP.2009.2038424 Qiao, 2019, A low-complexity orthogonal matching pursuit based channel estimation method for time-varying underwater acoustic ofdm systems, Appl. Acoust., 148, 246, 10.1016/j.apacoust.2018.12.026 Kuhne, 2008, Throughput analysis of multi-user OFDMA-systems using imperfect CQI feedback and diversity techniques, IEEE J. Sel. Area Commun., 26, 1440, 10.1109/JSAC.2008.081010 Akoum, 2010, Limited feedback for temporally correlated MIMO channels with other cell interference, IEEE Trans. Signal Process., 58, 5219, 10.1109/TSP.2010.2055860 Bergstra, 2012, Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281 Pedregosa, 2011, Scikit-learn: Machine learning in python, JMLR, 12, 2825