Abstract: Provided are a time domain channel prediction method and a time domain channel prediction system for an OFDM wireless communication system, which relate to the technical field of adaptive transmission in wireless communication. Frequency domain channel information is converted into time domain tap information by inverse Fourier transform. With respect to each time domain tap information, tap information prediction based on an extreme learning machine is realized, and finally predicted tap information is converted into frequency domain channel information by Fourier transform. To improve a generalization ability of a channel predictor, an output weight of the extreme learning machine is punished by a combination of l2 regularization and l1/2 regularization. The disclosure may provide satisfactory prediction performance and may output a sparse output weight, which reduces the requirement for memory storage. The disclosure ensures adaptive transmission and adaptive coding of wireless communication.

Abstract: A method and a system for selecting important delay taps of channel impulse response are provided, which belong to the field of adaptive transmission technology of wireless communication. The method performs local predictability estimation on all delay taps in the channel impulse response in wireless communication, thereby selecting the important delay taps in the channel impulse response. The disclosure uses recurrence rates of the delay taps to estimate local predictability of the delay taps. Noise delay taps are distinguished from the important delay taps through the local predictability of the delay taps, which improves the identification rate of the important delay taps of the channel impulse response in wireless communication. The disclosure may implement the selection of the important delay taps of the channel impulse response, which provides guarantee for implementing adaptive transmission and adaptive coding of wireless communication.

Abstract: A method and a system for selecting important delay taps of channel impulse response are provided, which belong to the field of adaptive transmission technology of wireless communication. The method performs local predictability estimation on all delay taps in the channel impulse response in wireless communication, thereby selecting the important delay taps in the channel impulse response. The disclosure uses recurrence rates of the delay taps to estimate local predictability of the delay taps. Noise delay taps are distinguished from the important delay taps through the local predictability of the delay taps, which improves the identification rate of the important delay taps of the channel impulse response in wireless communication. The disclosure may implement the selection of the important delay taps of the channel impulse response, which provides guarantee for implementing adaptive transmission and adaptive coding of wireless communication.

Abstract: Provided are a time domain channel prediction method and a time domain channel prediction system for an OFDM wireless communication system, which relate to the technical field of adaptive transmission in wireless communication. Frequency domain channel information is converted into time domain tap information by inverse Fourier transform. With respect to each time domain tap information, tap information prediction based on an extreme learning machine is realized, and finally predicted tap information is converted into frequency domain channel information by Fourier transform. To improve a generalization ability of a channel predictor, an output weight of the extreme learning machine is punished by a combination of l2 regularization and l1/2 regularization. The disclosure may provide satisfactory prediction performance and may output a sparse output weight, which reduces the requirement for memory storage. The disclosure ensures adaptive transmission and adaptive coding of wireless communication.

Abstract: The disclosure discloses a channel prediction method and system for a MIMO wireless communication system. The method includes the following steps: obtaining frequency domain channel information of each antenna pair of the MIMO wireless communication system through channel estimation; processing, by inverse Fourier transform, frequency domain channel information of each antenna pair to obtain information of each effective delay path of the MIMO wireless communication system; training the width learning system; utilizing the trained width learning system to predict each effective delay path of each antenna pair, so as to obtain the information of the next moment of each effective delay path of each antenna pair; after summarizing the information of the next moment of each effective delay path of each antenna pair obtained through prediction, the Fourier transform is utilized to convert the information into predicted frequency domain channel information.

Abstract: A channel prediction system and a channel prediction method for an OFDM wireless communication system include a standard echo state network and a two-layer adaptive elastic network. In the method, with respect to each subcarrier of a pilot OFDM symbol, an echo state network is trained by using frequency domain channel information of each subcarrier obtained by channel estimation. The trained echo state network may realize short-term prediction of the frequency domain channel information. To overcome a likely ill-conditioned solution of an output weight in an echo state network, the output weight in the echo state network is estimated by using a two-layer adaptive elastic network.

Abstract: A channel prediction system and a channel prediction method for an OFDM wireless communication system include a standard echo state network and a two-layer adaptive elastic network. In the method, with respect to each subcarrier of a pilot OFDM symbol, an echo state network is trained by using frequency domain channel information of each subcarrier obtained by channel estimation. The trained echo state network may realize short-term prediction of the frequency domain channel information. To overcome a likely ill-conditioned solution of an output weight in an echo state network, the output weight in the echo state network is estimated by using a two-layer adaptive elastic network.