Joint channel state detection and decoding algorithm based on classification learning

Abstract

The invention discloses a joint channel state detection and decoding algorithm based on classification learning and belongs to the technical field of on-the-move satellite communication. An intermittent channel state transfer model is established by utilizing intermittent channel structure information; through a sum-product algorithm, by means of transmission among factor graph structure channel observation function nodes, channel state variable nodes, received symbol variable nodes, symbol mapping function nodes, mixed mapping nodes and coding constraint nodes, and through weighted average of neighbor channel state node information, current channel state node information is obtained; and through continuous iterative computation, a channel state distribution and decoding result is obtained. Compared with a channel state detection and decoding method based on the channel state transfer model, the algorithm has the obvious advantages of being low in complexity and self-adaptive to channel change and the like.

Description

technical field [0001] The invention belongs to the technical field of satellite communication in motion, and in particular relates to a joint channel state detection and decoding algorithm based on classification learning. Background technique [0002] In recent years, the field of On-the-Move in satellite communication has gradually received attention. Satellite communication technology in motion refers to the technology of maintaining uninterrupted satellite communication during the movement of vehicles, aircraft and other carriers. During the moving process of the carrier, the wireless signal will be randomly blocked or even randomly interrupted due to factors such as obstruction by obstacles. At the same time, in the helicopter satellite communication, because the installation position of the helicopter antenna is limited, the signal will be blocked by the helicopter rotor. Therefore, it is particularly critical to solve the channel intermittent problem in the mobile ...

AI Technical Summary

Problems solved by technology

In terms of channel detection, the traditional channel state detection method is a channel state information detection algorithm based on signal energy. In the case of low signal-to-noise ratio, this algorithm cannot take into account the false detection probability and detection accuracy.

And in practical applications, the constantly changing signal-to-noise ratio of the received signal will further degrade the actual performance

At the same time, compared with the traditional channel state information detection algorithm based on signal energy, the joint channel state detection method based on the state transition model, which uses the intermittent channel structure characteristics, uses the channel transition probability function to update the channel state variable node information, Good detection results can be obtained, but the state transition probability of the channel needs to be known in advance. In practical applications, due to channel changes, it is difficult to obtain accurate parameters, which makes the detection effect decline.

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