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With the rapid development of high−speed transportation networks and the continuous advancement of sixth−generation (6G) mobile communication technologies, the demand for reliable wireless connectivity in high−mobility scenarios has grown significantly. However, the high Doppler shift induced by user mobility leads to rapidly time−varying channels, significantly degrading communication reliability and transmission quality. To address this challenge, this paper proposes an integrated sensing and communication (ISAC) approach, where the base station simultaneously communicates with high−speed users and receives echo signals to predict the Doppler shift. The predicted Doppler shift is then pre−compensated to reduce signal processing complexity at the receiver and improve communication quality. Specifically, this paper introduces an intelligent online Doppler shift prediction method based on long short−term memory (LSTM) networks for orthogonal frequency division multiplexing (OFDM) systems in high−speed mobility scenarios. In this method, the base station estimates the current Doppler shift based on received echo signals and e*mploys an LSTM model to predict Doppler shifts in real−time for the subsequent moment. To effectively handle dynamic environments, the proposed model utilizes an online updating strategy, where LSTM model parameters are updated in real−time after receiving echoes and estimating Doppler shift. To evaluate the performance of the proposed model, we compare the LSTM−based prediction results with those obtained using an unscented Kalman filter (UKF). Prediction accuracy is analyzed under varying conditions of mobile speeds and signal−to−noise ratios. Simulation results demonstrate that the proposed online LSTM prediction model exhibits superior accuracy and robustness in nonlinear Doppler shift prediction compared to the UKF model, providing an efficient and reliable solution for online Doppler shift prediction in highly dynamic communication environments.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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