Golay Layer for PAPR Control in AI-Based OFDM Systems
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Solution Overview
Problem
Traditional end-to-end learning methods, such as auto-encoder orthogonal frequency division multiplexing (AE-OFDM), do not provide a permanent solution for peak-to-average power ratio (PAPR) without rigorous training/optimization, increasing training complexity in AI-based communication systems.
Innovation Solution
A new layer, the Golay layer, is introduced in AI-based communication systems to limit instantaneous peak power by manipulating complementary sequences through neural networks, stabilizing both mean and peak power using algebraic representations, and combining with other layers like the clipping and Polar-to-Cartesian layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional end-to-end learning methods (AE-OFDM) are used to control PAPR, then instantaneous peak power can be limited, but training complexity increases significantly
Solution Approach 1:
The patent segments the PAPR control function into a dedicated Golay layer that operates independently within the neural network architecture. This layer specifically processes complementary sequences to control peak power, separating this function from the general end-to-end learning process, thereby reducing overall training complexity while maintaining PAPR control effectiveness
Solution Approach 2:
The patent applies preliminary action by pre-processing information bits through the Golay layer before they enter the main neural network processing. The complementary sequences are generated and parameters are controlled in advance, ensuring PAPR constraints are met before the signal undergoes complex neural network transformation, thus avoiding the need for rigorous post-training optimization
2Manufacturing precision
If rigorous training/optimization procedures are applied to AE-OFDM, then PAPR control improves, but training time and computational resources increase
Solution Approach 1:
The patent extracts the PAPR control functionality from the general end-to-end learning process and implements it through the dedicated Golay layer with complementary sequences. This extraction allows PAPR control to be achieved with simpler, more efficient training procedures compared to optimizing the entire AE-OFDM system, reducing both training time and computational resource requirements
3Reliability
If complementary sequences are manipulated through neural networks, then instantaneous peak power is limited, but system complexity increases
Solution Approach 1:
The patent implements dynamics by making the Golay layer adaptable within the neural network framework. The layer can dynamically adjust its processing based on input conditions while maintaining the structural efficiency of complementary sequence manipulation, achieving peak power limitation without permanently increasing system complexity
Solution Approach 2:
The Golay layer is designed with multi-functionality, serving both as a PAPR control mechanism and as an integral part of the end-to-end learning system. This universal design allows the same structure to perform multiple functions, reducing the need for additional separate components and thereby minimizing system complexity
Data Source
AI summary
A new layer tailored for Artificial Intelligence-based communication systems to limit the instantaneous peak power for the signals that relies on manipulation of complementary sequences through neural networks. Disclosed is a method for providing non-linear distortion in end-to-end learning communication systems, the communication system comprising a transmitter and a receiver. The method includes mapping transmitted information bits to an input of a first neural network; controlling, by an output of the neural network, parameters of a complementary sequence (CS) encoder, producing an encoded CS; transmitting the encoded CS through an orthogonal frequency division multiplexing (OFDM) signal; processing, by Discrete Fourier Transform (DFT), the encoded CS, to produce a received information signal in a frequency domain; and processing, by a second neural network, the received information signal.