OFDM Signal Generation Using ANN-Based BER and PAPR Optimization
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Solution Overview
Problem
Existing wireless communication systems face challenges in optimizing both bit error rate (BER) and peak-to-average power ratio (PAPR) simultaneously, particularly in integrating artificial intelligence for enhanced signal processing, leading to potential performance degradation and inefficiencies.
Innovation Solution
A method and device utilizing artificial neural networks (ANNs) to generate and process wireless signals in the frequency domain, incorporating PAPR margin information to optimize both BER and PAPR through end-to-end learning, ensuring efficient signal transmission and reception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional channel encoder/decoder are used in wireless communication systems, then system reliability is maintained, but design efficiency and optimization capability are limited
Solution Approach 1:
The patent replaces traditional mechanical channel encoder/decoder systems with an artificial neural network-based end-to-end autoencoder system. The neural network learns optimal encoding and decoding transformations through training, substituting conventional error correction coding schemes with data-driven intelligent processing that achieves both high design efficiency and reliable communication performance
Solution Approach 2:
The patent transforms the fixed parameter-based traditional encoding/decoding approach into a flexible parameter-optimized neural network system. Through training processes, the neural network automatically adjusts its internal parameters (weights and biases) to optimize performance, enabling adaptive optimization that traditional fixed-parameter systems cannot achieve
2Reliability
If AI models are integrated into communication systems for enhanced performance, then communication performance is improved, but system complexity and computational burden increase
Solution Approach 1:
The patent merges the channel encoder and decoder into a unified end-to-end autoencoder neural network system. This integration combines previously separate encoding and decoding functions into a single trained model, reducing system complexity while maintaining or improving communication performance through coordinated optimization of both functions simultaneously
Solution Approach 2:
The patent creates a universal neural network-based autoencoder that can handle multiple communication tasks and channel conditions. The single trained model performs both encoding and decoding operations, providing multi-functionality that reduces the need for separate specialized components and simplifies system architecture
3Reliability
If PAPR reduction techniques are applied in OFDM systems, then signal quality is improved, but transmission efficiency and power utilization deteriorate
Solution Approach 1:
The patent applies preliminary PAPR reduction processing within the neural network encoding stage, before signal transmission. The autoencoder learns to generate encoded signals with reduced PAPR characteristics during training, proactively preventing high PAPR issues rather than correcting them afterward, thereby maintaining both signal quality and transmission efficiency
Solution Approach 2:
The patent changes the signal parameters through neural network transformation to achieve optimal PAPR characteristics. The learned encoding transformations modify signal parameters to reduce peak power while maintaining average power efficiency, resolving the trade-off between signal quality and transmission efficiency
Data Source
AI summary
The present invention relates to a wireless communication system and, particularly, to a method and an apparatus therefor, the method comprising the steps of: inputting, into an artificial neural network, (1) a data modulation symbol, (2) information for generating a reference signal sequence, and (3) PAPR margin information, so as to output a complex sequence from the artificial neural network in a frequency domain; generating an OFDM symbol by applying the inverse Fourier transform to the complex sequence; and transmitting the OFDM symbol.