Radio Mapping Architecture for Dynamic ML-Based RF Parameter Prediction
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Solution Overview
Problem
Current wireless network planning techniques are static and fail to adapt to dynamic radio environments, leading to sub-optimal spectral efficiency and resource utilization due to inadequate data for machine learning applications at the access and physical layers, particularly in Massive MIMO systems where accurate CSI is hard to obtain, resulting in poor channel estimation and performance issues.
Innovation Solution
A radio mapping architecture that includes a User Equipment (UE), a spectrum monitoring unit, and a server with a radio mapping database and Machine Learning (ML) module, which continuously monitors and updates RF parameters to predict dynamically changed parameters, refine the ML model, and optimize channel estimation and beamforming for improved spectral efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static network planning techniques are used to configure wireless networks, then network configuration is simplified and easier to implement, but spectral efficiency is reduced and resource utilization becomes sub-optimal due to inability to adapt to dynamic radio environments
Solution Approach 1:
The patent implements dynamic network configuration by deploying machine learning models that continuously learn from radio environment data and adapt network parameters in real-time. The system transitions from static configuration to dynamic adaptation by collecting measurement data from user equipment, training ML models to predict optimal parameters, and applying these predictions to adjust network configuration dynamically, thereby resolving the contradiction between ease of operation and spectral efficiency.
Solution Approach 2:
The patent establishes a feedback loop where measurement data from the radio environment is continuously collected, processed through machine learning models, and used to adjust network parameters. The system monitors actual performance, compares it with predictions, and refines its configurations iteratively. This feedback mechanism enables the system to maintain optimal spectral efficiency while keeping the configuration process automated and manageable.
2Reliability
If bandwidth allocated to reference signals is increased to improve robustness under worst-case scenarios, then demodulation reliability is improved, but unnecessary overheads increase when RF conditions are benign
Solution Approach 1:
The patent applies parameter changes by using machine learning models to dynamically adjust reference signal bandwidth allocation based on predicted radio conditions. Instead of using fixed worst-case configurations, the system learns from historical and real-time data to determine appropriate reference signal levels for current conditions. This allows the network to reduce reference signal overhead when conditions are good while maintaining sufficient robustness when conditions deteriorate, resolving the contradiction between reliability and energy loss.
3Ease of operation
If single point measurements are used for real-time channel parameter determination, then measurement simplicity is maintained, but time and space variations of radio environment are not captured resulting in poor estimation
Solution Approach 1:
The patent applies preliminary action by collecting and storing measurement data from multiple points and time instances before making channel parameter determinations. Instead of relying on single-point measurements, the system accumulates historical measurement data, uses this training data to build predictive models, and then applies these models to estimate current channel parameters. This preliminary data collection and model training enables accurate estimation without requiring complex real-time multi-point measurements, resolving the contradiction between measurement simplicity and precision.
4Productivity
If Machine Learning techniques are implemented at the access and physical layers, then accurate radio parameter prediction and adaptive optimization are achieved, but lack of large structured data sets and unstructured data availability prevents effective ML application
Solution Approach 1:
The patent introduces an intermediary layer that collects, structures, and prepares raw measurement data from the radio environment for machine learning processing. This intermediary data processing layer transforms unstructured measurement data into structured formats suitable for ML training, bridges the gap between available raw data and ML requirements, and enables effective ML application at the access and physical layers. This resolves the contradiction by creating the necessary data infrastructure without requiring changes to the underlying measurement processes.
Data Source
AI summary
A radio mapping architecture for applying machine learning techniques to mobile wireless radio access networks, including a base station, a user equipment (UE), and a network is provided. The radio mapping architecture includes a spectrum monitoring unit and a server and utilizes the UE. The server includes a radio mapping database and a Machine Learning module. The UE or the spectrum monitoring unit captures Radio parameters to derive an input schema for the radio mapping database. The spectrum monitoring unit extracts the Radio parameters that correspond to the base station and the UE and updates them in the radio mapping database periodically. The input schema for the radio mapping database is updated with the Radio parameters sensed by the spectrum monitoring unit and the UE.