Fronthaul Spectrum Channel Configuration Using ML Frequency Prediction
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Solution Overview
Problem
Fifth Generation (5G) wireless networks face challenges in managing signal strength and spectral efficiency in fronthaul optical networks due to varying optical fiber characteristics and on-demand traffic demands, leading to signal drops and inefficiencies in spectrum channel utilization.
Innovation Solution
A software-defined access optical controller (SDAOC) uses machine learning to predict optimal spectrum channels by learning the physical characteristics of the fronthaul optical network, selecting frequencies that support spectral efficiency needs, and recommending channels to maximize utilization while minimizing operating expenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional spectrum channel allocation methods are used in fronthaul optical networks, then device complexity is reduced, but spectral efficiency and signal strength reliability deteriorate due to varying optical fiber characteristics and on-demand traffic demands
Solution Approach 1:
The system employs machine learning models that automatically learn optical fiber characteristics and predict optimal spectrum channels without manual configuration. The model self-adapts to varying fiber properties and traffic patterns, eliminating the need for complex manual spectrum management while maintaining high signal reliability
Solution Approach 2:
The system dynamically adjusts spectrum channel parameters (frequency, bandwidth) based on real-time optical fiber characteristics and traffic demands. The machine learning model continuously optimizes these parameters to maintain signal strength reliability despite variations in fiber properties
2Productivity
If machine learning models are used to predict optimal spectrum channels, then spectral efficiency and signal reliability improve, but device complexity and computational requirements increase
Solution Approach 1:
The machine learning model is trained in advance on historical optical fiber characteristics and traffic patterns to build a predictive framework. This preliminary training enables the system to quickly predict optimal spectrum channels during operation without requiring complex real-time computations
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges the optical fiber physical layer and the spectrum channel allocation layer. This intermediary model translates complex optical characteristics into simplified predictions for spectrum optimization, reducing computational complexity
3Reliability
If spectrum channels are optimized for on-demand traffic demands, then data transmission reliability improves, but loss of time in channel configuration and adaptation increases
Solution Approach 1:
The system performs preliminary learning of optical fiber characteristics during idle or low-traffic periods, building a predictive model in advance. When on-demand traffic arrives, the pre-trained model can quickly predict optimal spectrum channels without requiring time-consuming real-time analysis
Solution Approach 2:
The system continuously monitors transmission quality and feeds this information back to the machine learning model. This feedback mechanism enables the model to adapt to changing fiber characteristics and traffic patterns over time, maintaining reliability without requiring manual reconfiguration
Data Source
AI summary
A method for configuring a spectrum channel between a centralized unit and a distributed unit of a fronthaul optical network of a wireless communications core network includes obtaining a set of parameters describing settings of the centralized unit and the distributed unit, executing a machine learning model trained to predict an optimal frequency of a spectrum channel for carrying data between the centralized unit and the distributed unit when the settings of the centralized unit and the distributed unit are configured in accordance with the set of parameters, identifying a spectrum channel of a path between the centralized unit and the distributed unit that supports the optimal frequency and sending commands to the centralized unit and the distributed unit that cause the settings of the centralized unit and the distributed unit to be configured to use the spectrum channel for carrying data between the centralized unit and the distributed unit.