AI-based end-to-end wireless communication system and method

Abstract

The invention belongs to the technical field of wireless communication networks, and discloses an AI-based end-to-end wireless communication system and method. The method comprises the steps: communication system end-to-end granularity strategy matching for each module and the selecting of each communication system end-to-end strategy granularity parameter. The invention proposes an optimal strategy intelligent combination method for the end-to-end efficient transmission of a communication system, and the method gives consideration to a plurality of indexes of all modules according to the end-to-end transmission demands of a user, and achieves the resource configuration. Therefore, the method avoids a dilemma that the sequence of the strategy matching may affect the result, irons out the defect that the independent optimization of one module in the prior art causes a condition that the optimization of all modules is not equal to the global optimization, and achieves a purpose that thecommunication system globally performs the adaptive optimal strategy selection according to the user demands and the channel environment. To sum up, the method reduces the strategy mismatching betweenall modules, improves the end-to-end system performances, and reduces the error rate.

Description

technical field [0001] The invention belongs to the technical field of wireless communication networks, and in particular relates to an AI-based end-to-end wireless communication system and method. Background technique [0002] At present, the existing technology in the industry is as follows: source coding is coding for the purpose of improving the efficiency in the transmission process. Communication resources are precious, and the higher the efficiency of source coding, the better, that is, to use the least number of bits in the same amount of information. Channel coding is coding for the purpose of improving reliability in the transmission process, and reduces the probability of errors by introducing redundancy into the transmitted data. At the same time, when the source data rate is fixed, the bandwidth requirement is increased. In the encoding algorithm, the encoding rate, code distance and code weight are all very important parameters. At the same time, issues such ...

AI Technical Summary

Problems solved by technology

In the 5G communication process, the source structure is complex and the channel perception topology changes drastically. Therefore, problems such as source and channel coding, transmission and code rate restoration difficulties are imminent.

[0003] To sum up, the problems existing in the existing technology are: in the existing scheduling, deep learning is mostly used to assist or replace a certain module strategy algorithm, and deep learning is rarely used to manage each module strategy

[0005] Due to the high complexity of deep learning algorithms and the low delay required in communication, the original instruments are not enough to meet the needs

However, existing communication systems usually process each module independently, and each module performs a specific function, but the optimum of each module is not equal to the global optimum

Images