Method for forecasting signal-to-noise ratio of mobile communication signals based on channel scene classification

Abstract

The invention discloses a method for forecasting a signal-to-noise ratio of mobile communication signals based on channel scene classification. The method includes the following steps that first step, a signal-to-noise ratio of a moment is measured and compared with a moment forecast signal-to-noise ratio of moment forecast, and a deviation value between the moment measured signal-to noise ratio and the forecast signal-to-noise ratio is obtained; second step, at the moment, a current channel variation scene is judged as a channel slow variation scene, and a forecast scheme of the channel slow variation scene is adopted for forecasting a forecast signal-to-noise ratio of the moment; third step, at the moment, the current channel variation scene is judged as a channel moderate variation scene, a forecast scheme of the channel moderate variation scene is adopted for forecasting a forecast signal-to-noise ratio of the moment; fourth step, at the moment, the current channel variation scene is judged as a channel sudden variation scene, a forecast scheme of the channel sudden variation scene is adopted for forecasting a forecast signal-to-noise ratio of the moment; fifth step, a command of returning the first step is carried out. The method for forecasting the signal-to-noise ratio of mobile communication signals based on the channel scene classification can deal with the channel scenes very well in which a traditional forecasting technology is ineffective, can track variation of the signal-to-noise ratio rapidly and accurately and enables the performance of a whole system to be improved.

Description

technical field [0001] The invention relates to the field of mobile communication, in particular to a signal-to-noise ratio prediction method for mobile communication signals based on channel scene classification. Background technique [0002] The most important difference between wireless communication and wired communication is the random variability of the channel, which mainly refers to the openness of the channel transmission environment and the time-varying nature of channel parameters, which cause channel frequency selective fading and time-varying fading, and the movement of receivers and transmitters It also leads to the complexity of the receiving environment and the randomness of the receiving location. The traditional idea is to ensure that the system can maintain normal communication under relatively bad channel conditions. Generally, the reliability of communication is achieved by increasing the transmission power of the transmitter, reducing the modulation...

AI Technical Summary

Problems solved by technology

The traditional signal-to-noise ratio prediction method is only suitable for the situation where the channel state changes slowly. When the channel undergoes a large change or even a sudden change in a short period of time, the signal-to-noise ratio prediction deviation will be large, and the traditional prediction method will fail. As a result, the performance of adaptive communication systems using signal-to-noise ratio prediction technology is severely damaged

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