AI-Driven Polar Coding for Adaptive Wireless Signal Transmission
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Solution Overview
Problem
Existing wireless communication systems face challenges in adapting to varying communication channel environments in real-time, particularly in enhancing communication capacity, reliability, and reducing latency for diverse services and user equipment.
Innovation Solution
A method and apparatus utilizing artificial intelligence to generate real-time channel environment-adaptive polar codes for signal transmission and reception between terminals and base stations, involving the determination and learning of polar codes based on reward information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed polar codes are used for signal transmission, then device complexity is reduced, but adaptability to varying channel environments deteriorates
Solution Approach 1:
The patent implements dynamic polar code generation by training neural networks offline to learn optimal code structures for different channel conditions. During real-time communication, the system dynamically selects and applies pre-trained codes based on current channel estimates, enabling adaptation without real-time computation complexity
Solution Approach 2:
The system performs preliminary training of neural networks offline to generate optimal polar codes for various channel conditions. This pre-computation stores code structures in advance, eliminating the need for complex real-time code generation while maintaining high adaptability when channel conditions change
2Reliability
If real-time AI-based polar code generation is implemented, then communication capacity and reliability are improved, but processing time and latency increase
Solution Approach 1:
The patent performs all AI-based polar code generation and optimization in advance during an offline training phase. Multiple neural networks are trained on diverse channel conditions to pre-generate optimal codes, so that during real-time communication, only lightweight code selection and application are needed, eliminating real-time processing delays
Solution Approach 2:
The system segments the code generation process into offline training phase and online application phase. The complex AI processing is separated from real-time communication, with only lightweight code selection remaining in the online phase, thus maintaining high reliability without increasing real-time latency
3Productivity
If channel environment adaptation is enhanced through AI learning, then communication efficiency is improved, but system complexity increases
Solution Approach 1:
The patent employs self-service mechanisms where the system automatically estimates channel conditions and selects appropriate pre-trained polar codes without requiring manual configuration or complex real-time optimization. The pre-trained neural networks autonomously determine optimal codes based on channel state information
Solution Approach 2:
Complex AI-based code optimization is performed in advance during offline training, creating a library of optimized codes for various channel conditions. This preliminary action eliminates the need for complex real-time processing, maintaining high communication efficiency while minimizing system complexity during operation
Data Source
AI summary
The present disclosure may provide a method for operating a terminal in a wireless communication system. Herein, the method for operating the terminal may include determining a first polar code through an artificial intelligence (AI), transmitting data encoded in the first polar code and an information subchannel index set for the first polar code to a base station, receiving reward information based on decoding of the data from the base station, determining a second polar code by performing learning through the AI based on the reward information, and transmitting data encoded in the determined second polar code and an information subchannel index set for the second polar code to the base station.