An 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, including coarse-grained strategy matching of each module of the communication system end-to-end and selection of fine-grained parameters of each strategy of the communication system end-to-end . The present invention proposes an optimal strategy intelligent combination method for end-to-end efficient transmission of a communication system, and comprehensively considers multiple indicators of each module according to user's end-to-end transmission requirements to realize resource allocation. Therefore, it avoids the dilemma that the order of strategy matching may affect its results, overcomes the disadvantage of optimizing a module alone in the existing technology so that "the optimum of each module is not equal to the global optimum", and realizes that the overall communication system is based on user needs. Environment and other adaptive optimal strategy selection. To sum up, the present invention can reduce policy mismatch among modules, improve end-to-end system performance, and reduce bit 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

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