AI Encoding Decoding for 5G PAPR Reduction
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Solution Overview
Problem
Current wireless communication systems, particularly in 5G, face challenges in reducing peak-to-average power ratio (PAPR) during data transmission and reception, which affects efficiency and latency, especially in AI-enhanced encoding and decoding processes.
Innovation Solution
Implementing artificial intelligence (AI) through deep neural networks (DNNs) for encoding and decoding processes in user equipment (UE) and base stations (BS), allowing for dynamic adjustment of AI parameters such as number of layers, nodes, and connection methods to optimize data transmission and reception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI encoding and decoding processes are implemented in wireless communication systems, then data transmission efficiency is improved, but peak-to-average power ratio increases
Solution Approach 1:
The patent applies dynamics by making the AI model configuration adjustable and adaptable. The base station and user equipment can dynamically select different numbers of layers, nodes, and connection methods in the neural network based on channel conditions and transmission requirements. This dynamic adjustment allows the system to optimize between transmission efficiency and power consumption by selecting appropriate AI model complexities for different scenarios.
Solution Approach 2:
The patent implements parameter changes by modifying AI model parameters (number of layers, nodes, connection methods) to control the peak-to-average power ratio. By changing these parameters, the system can adjust the computational complexity of AI encoding/decoding processes, thereby managing power consumption while maintaining data transmission efficiency. The base station transmits AI parameters to user equipment to control these changes.
2Loss of time
If AI parameters are adjusted to optimize encoding and decoding, then latency is reduced, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the AI processing complexity between the base station and user equipment. The base station handles AI parameter generation and transmission, while user equipment performs AI-based encoding and decoding. This segmentation allows latency optimization through coordinated AI processing while distributing device complexity across multiple components rather than concentrating it in a single device.
Solution Approach 2:
The patent uses AI parameters as an intermediary between the base station and user equipment. These parameters convey the necessary configuration information for AI models without requiring direct complex model exchange. The intermediary AI parameters enable efficient encoding/decoding with reduced latency while keeping the actual AI model complexity managed and controlled through parameter transmission rather than full model deployment.
Data Source
AI summary
Provided is a method for transmitting or receiving data, by a user equipment (UE), to or from a base station (BS). The method includes transmitting, by the UE, capability information of the UE to the BS, wherein the capability information includes information related to artificial intelligence (AI) calculation for the data transmission or reception, receiving, by the UE, at least one of a plurality of AI parameters from the BS, and applying the at least one AI parameter to an encoding process for the data transmission or a decoding process for the data reception, wherein the encoding process or the decoding process is performed by information on a network structure in the at least one AI parameter, and wherein the at least one AI parameter comprises a plurality of information for performing the encoding process or the decoding process by the network structure.