Physics-based propagation models for channels involving mixed paths

Luebbers, R.; Schuster, J.; Fast, Stephen A.

doi:10.1109/milcom.2003.1290432

Cited by 1 publication

(1 citation statement)

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“…To address this challenging problem, a basic approach is to model the physical characteristics of a channel during transmission and then use the established physical model to predict future channels. For example, in [3][4][5], the fading channel is assumed to be the overlap of a limited set of complex sinusoidal waves whose parameters, namely amplitude, arrival and departure angles, Doppler shift and number of scattering sources, are weak compared to the channel volatility and may be accurately predicted. In this way, the physical characteristics of the channel transmission can be accounted for, and the prediction effect is better when the modeling is accurate.…”

Section: Introductionmentioning

confidence: 99%

Mixed RNN‐DNN based channel prediction for massive MIMO‐OFDM systems

Ge,

Shi,

Niu

et al. 2023

IET Communications

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Channel state information (CSI), which is crucial for resource allocation and system performance in time division duplex (TDD) massive multiple‐input multiple‐output (MIMO) systems, is difficult to predict because of the channel's time‐varying nature. To overcome this limitation, a scheme for channel prediction combined with deep learning (DL) is proposed. The system uses a deep neural network (DNN) to interpolate channel estimates from a few received pilot signals and a recurrent neural network (RNN) to train through the current time and the recent historical channel estimates to predict the CSI while the channel is constantly varying. In the end, a mixed neural network of RNN and DNN, is called MRDNN. In addition, the proposed DL‐based method does not rely on the relevant feature information about the channel, such as internal characteristics and parameters of the channel itself or channel statistical information, which improves its effectiveness in practical applications. The results of the simulation show that the MRDNN‐based method is better than the existing methods, like traditional AR method and NL Kalman method, and also can be effective in improving the quality of channel prediction and the performance of the system under the dynamic change scenario of low mobility.

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Section: Introductionmentioning

confidence: 99%