Spatial Link Performance Mapping for 5G mmWave Stability
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Solution Overview
Problem
Wireless networks, particularly those utilizing 5G millimeter wave (mmWave) spectrum, are prone to significant fluctuations in link quality due to physical obstructions, leading to decreased bandwidth and user experience variability.
Innovation Solution
Implementing link performance prediction (LPP) technology that uses machine learning and spatial mapping to proactively predict link quality changes, enabling applications to adapt to variations before they occur, by characterizing the surrounding environment of mmWave cells and tracking user equipment (UE) movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If 5G millimeter wave spectrum is used to increase overall capacity, then network capacity is improved, but link quality becomes extremely volatile and prone to blockage
Solution Approach 1:
The system performs preliminary actions by predicting future link quality fluctuations using machine learning models before they occur. The LPP service analyzes historical and real-time data to forecast link quality changes, allowing the network to proactively adjust parameters and maintain stable communication despite using volatile mmWave spectrum for high capacity.
2Reliability
If link quality is monitored in real-time to detect fluctuations, then link quality stability is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary LPP service that acts as a mediator between the radio access network and applications. This service consolidates the complexity of real-time link quality monitoring and prediction into a dedicated component, allowing the core network to maintain reliability without each individual device becoming more complex. The LPP service translates complex radio conditions into actionable predictions for applications.
3Measurement precision
If machine learning models are deployed for link performance prediction, then prediction accuracy is improved, but computational resources and energy consumption increase
Solution Approach 1:
The LPP service operates autonomously, collecting and analyzing data from multiple sources including radio measurements, location information, and environmental data. The system self-manages the computational workload by implementing efficient machine learning models that can run on network infrastructure rather than requiring high energy consumption on mobile devices. The service automatically updates prediction models based on incoming data without requiring intensive manual processing.
Data Source
AI summary
In one embodiment, a current path of a mobile device is determined based on radio signals between the mobile device and a base station, which indicates a sequence of positions of the mobile device over a current time window. A future path of the mobile device is then predicted based on the current path, which indicates a sequence of predicted future positions of the mobile device over a future time window. A link performance prediction (LPP) is then generated for the mobile device based on the future path of the mobile device and a base station coverage map. The base station coverage map indicates a radio signal quality across a base station coverage area, which is represented as a three-dimensional (3D) coordinate space. Moreover, the LPP indicates a predicted performance of a radio link between the mobile device and the base station during the future time window.