Wireless Downlink Coding With UE Capability Signaling
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Solution Overview
Problem
Current wireless communication systems face challenges in optimizing channel codes for non-AWGN channels, leading to errors and resource wastage due to retransmissions, especially in ultra-high latency and ultra-reliable communication environments like 5G/6G.
Innovation Solution
A method using AI/ML to generate optimal codes based on channel information and capability information, where user equipment (UE) and base stations exchange decoding level and machine learning capability information to select or generate machine learning-based codes, optimizing block error rates and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional channel codes are used in non-AWGN channels, then device complexity is reduced, but communication reliability deteriorates due to errors and retransmissions
Solution Approach 1:
The patent applies parameter changes by using AI/ML to generate codes that adapt to specific channel conditions rather than using fixed traditional codes. The system changes the code parameters based on channel state information, enabling optimization for non-AWGN channels while maintaining manageable complexity through intelligent algorithm selection.
Solution Approach 2:
The patent replaces traditional mechanical/code-based error correction systems with AI/ML-based code generation. Instead of relying on predetermined code structures, the system uses machine learning models to generate optimized codes, substituting conventional coding mechanisms with intelligent adaptive systems.
2Reliability
If AI/ML-based codes are generated and used, then communication reliability improves, but loss of time increases due to code generation processing
Solution Approach 1:
The patent applies preliminary action by pre-generating and storing multiple candidate codes using AI/ML before actual communication occurs. When communication needs to occur, the system selects from pre-generated codes rather than generating new ones in real-time, thus reducing latency while maintaining the reliability benefits of AI-optimized codes.
Solution Approach 2:
The patent implements dynamics by creating a dynamic code selection mechanism that adapts to changing channel conditions. The system maintains a pool of pre-generated codes and dynamically selects the most appropriate code based on current channel state, balancing the trade-off between code optimization and generation time.
3Adaptability or versatility
If capability information exchange is implemented, then adaptability improves for code selection, but device complexity increases due to additional signaling
Solution Approach 1:
The patent applies the taking out principle by extracting only the essential capability information needed for code selection and transmitting only that specific data. Rather than exchanging comprehensive device specifications, the system identifies and transmits only the critical parameters related to code generation capabilities, reducing signaling overhead while maintaining adaptability.
Data Source
AI summary
A method of transmitting and receiving data in a wireless communication system and a device therefor are disclosed. Specifically, the method performed by a user equipment (UE) may comprise transmitting, to a base station, capability information including (i) first information indicating a decoding level of the UE and (ii) second information indicating whether a machine learning is possible, receiving decoding level information from the base station based on the first information, and receiving the downlink data from the base station based on the decoding level information and the second information.