Federated Learning for Wireless Signal Quality and Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional wireless communication systems face challenges in processing higher frequency ranges, such as 5G frequencies above 6 GHz, due to increased distortion and complexity, which complicates information recovery and increases hardware costs.
Innovation Solution
Implementing federated learning for deep neural networks (DNNs) in wireless communication systems, where a network entity directs user equipment (UEs) to form DNNs using initial ML configurations, aggregates updated ML information, and determines a common ML configuration for UEs with shared characteristics to improve wireless communication processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If higher frequency ranges (above 6 GHz) are used to increase data capacity, then data capacity is improved, but signal quality deteriorates due to increased susceptibility to multipath fading, scattering, atmospheric absorption, and interference
Solution Approach 1:
The patent replaces conventional signal processing methods with machine learning-based approaches. Deep neural networks are trained to predict and compensate for channel effects such as multipath fading, scattering, and atmospheric absorption. The ML model processes received signals and predicts transmitted signals, enabling the system to maintain signal quality despite the challenges of higher frequency ranges.
Solution Approach 2:
The patent dynamically adjusts system parameters based on channel conditions. The ML model continuously learns from incoming signals and adapts its parameters (weights and biases) to optimize performance. This allows the system to compensate for varying channel characteristics caused by multipath effects, scattering, and atmospheric conditions, thereby maintaining reliable communication at higher frequencies.
2Productivity
If hardware capable of transmitting and receiving higher frequencies is incorporated, then data capacity is improved, but device complexity and cost increase
Solution Approach 1:
The patent substitutes complex hardware solutions with software-based machine learning processing. Instead of requiring sophisticated hardware designed specifically for higher frequency ranges, the system uses standard hardware equipped with ML algorithms that can process and compensate for frequency-related challenges through software intelligence.
Solution Approach 2:
The ML-based processing system provides multiple functions using a single integrated approach. The same deep neural network infrastructure handles various tasks including signal prediction, channel compensation, and interference mitigation, reducing the need for specialized hardware components for each function.
3Device complexity
If conventional signal processing methods are used at higher frequencies, then device simplicity is maintained, but information recovery becomes more difficult due to increased distortion
Solution Approach 1:
The patent replaces conventional signal processing algorithms with machine learning-based approaches. Deep neural networks are employed to predict transmitted signals from received signals, automatically learning complex patterns and distortions caused by multipath fading, scattering, and atmospheric effects without requiring manual algorithm design.
Solution Approach 2:
The ML model performs self-learning and self-optimization by continuously training on received signals. The system automatically adapts to changing channel conditions and distortion patterns without requiring external intervention or complex manual configuration, making information recovery easier despite increased distortion at higher frequencies.
Data Source
AI summary
Aspects describe federated learning for deep neural networks, DNNs, in a wireless communication system. A network entity directs (610) each user equipment, UE, in a set of UEs to form, using an initial machine-learning (ML) configuration, a respective deep neural network, DNN, that processes wireless network communications. The network entity requests (620), from each UE in the set of UEs, respective updated ML information generated by the respective UE using a training procedure and local input data. The network entity then receives (640), from at least some UEs in the set of UEs, the respective updated ML information determined by the respective UE. The network entity identifies (645) a subset of UEs in the set of UEs and determines (650) a common ML configuration for the subset of UEs. The network entity then directs (655) each UE in the subset of UEs to form an updated DNN using the common ML configuration.