Recurrent Neural Network for I/Q Imbalance Compensation in Wireless Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wireless communication systems face challenges in efficiently compensating for in-band interference signals and power amplifier noise, particularly in 5G wireless communications, due to the complexity of implementing customized signal processing methods across various hardware platforms and the need for higher capacity and connectivity for machine learning and artificial intelligence applications.
Innovation Solution
The implementation of a recurrent neural network within wireless devices that utilizes a single receiver path for both feedback signal processing and wireless transmission, activating a switch to provide feedback during uplink time periods and receiving signals during downlink periods, allowing for efficient compensation of I/Q imbalance and nonlinear power amplifier noise using digital pre-mismatch and digital pre-distortion filters, and optimizing filter coefficients for improved noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a digital signal processor implements a specific portion of digital processing at a cellular base station using specially-designed hardware, then the signal processing performance is improved, but the device complexity increases
Solution Approach 1:
The patent applies universality by implementing multiple signal processing functions (filtering, interference cancellation, noise reduction) within a single reconfigurable hardware platform. The system can dynamically adapt to perform different processing tasks based on environmental parameters, eliminating the need for multiple specially-designed hardware components for different processing portions.
Solution Approach 2:
The patent employs dynamics by using reconfigurable hardware that can change its processing characteristics in real-time based on environmental conditions. The system dynamically adjusts filtering parameters, interference cancellation strategies, and noise reduction techniques according to the specific operational context, providing both versatility and optimized performance.
2Productivity
If wireless systems use traditional signal processing methods for 5G communications, then implementation is simpler, but the capacity and connectivity for machine learning and artificial intelligence applications is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-processing signals to extract features and prepare data for machine learning and AI algorithms. The system performs preliminary filtering, interference cancellation, and feature extraction that optimizes the input data for subsequent AI processing, enabling higher capacity and connectivity for ML/AI applications without requiring complete system redesign.
3Measurement precision
If machine learning systems use large volumes of data for training, then the accuracy is improved, but the memory capacity and data transmission requirements increase
Solution Approach 1:
The patent applies extraction by selectively identifying and extracting only the most relevant features and data elements needed for machine learning training. Rather than transmitting or storing complete raw datasets, the system extracts essential signal characteristics, interference patterns, and environmental parameters that are most valuable for improving ML accuracy, significantly reducing data volume requirements.
Data Source
AI summary
Examples described herein include methods, devices, and systems which compensates input data for I/Q imbalance or noise related thereto to generate compensated input data. In doing such the above compensation, during an uplink transmission time interval (TTI), a switch path is activated to provide converted input data to a receiver stage including a recurrent neural network (RNN). The RNN calculates an error representative of the noise based partly on the input signal to be transmitted and a feedback signal to generate filter coefficient data associated with the I/Q imbalance. The feedback signal is provided, after processing through the receiver, to the RNN. During an uplink TTI, the converted input data is transmitted as the RF wireless transmission via an RF antenna. During a downlink TTI, the switch path is deactivated and the receiver stage receives an additional RF wireless transmission to be processed in the receiver stage.