Location-Based Base Station Banning for Mobile Wireless Backhaul
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Solution Overview
Problem
Mobile wireless backhauling is challenging, particularly in fast-moving systems like trains, due to dynamic network conditions and environmental RF factors, leading to packet losses and reduced throughput, as traditional cellular technologies like LTE are not practical for such scenarios.
Innovation Solution
A device predicts a drop in received signal strength or throughput based on location data and prevents communication with a base station during a predicted key-hole phenomenon, using machine learning to determine ban parameters and switch to alternative base stations for stable connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mobile wireless backhauling is implemented in fast-moving systems, then connectivity is provided to onboard devices, but packet losses and reduced throughput occur due to dynamic network conditions and signal key-hole effects
Solution Approach 1:
The system performs preliminary actions by predicting signal key-hole effects before they occur. The machine learning model analyzes historical location data and signal strength patterns to forecast upcoming signal drops, allowing the system to proactively switch to alternative base stations before connectivity degradation happens, thus maintaining both reliability and throughput
Solution Approach 2:
The system implements dynamic base station selection and banning mechanisms. Instead of static connectivity approaches, the system continuously adapts base station associations based on real-time location data, predicted signal conditions, and dynamic banning parameters that adjust based on mobile system velocity and environmental factors
2Adaptability or versatility
If traditional cellular technologies like LTE are used, then standard communication protocols are available, but they are not practical for fast-moving mobile systems due to signal key-hole effects
Solution Approach 1:
The system changes operational parameters by implementing location-based base station banning with velocity-dependent thresholds. The banning parameters, including distance thresholds and duration settings, are dynamically adjusted based on the mobile system's velocity and predicted signal conditions, allowing the system to adapt LTE technology for fast-moving applications by modifying how base station associations are managed
3Reliability
If base station banning is implemented to mitigate signal key-hole effects, then connectivity reliability is improved, but device complexity increases due to machine learning prediction and dynamic parameter adjustment
Solution Approach 1:
The system implements self-service through autonomous machine learning models that automatically learn optimal banning parameters from historical data without requiring manual configuration. The system self-adjusts banning thresholds and durations based on patterns it discovers in location-signal strength correlations, reducing the operational complexity while maintaining reliability improvements
Data Source
AI summary
In one embodiment, a device obtains location data indicative of a location of a mobile system relative to a base station of a wireless network. The device predicts, based on the location data, a drop in received signal strength indicator as the mobile system approaches the base station. The device determines, based on the drop in received signal strength indicator or throughput that will occur as the mobile system approaches the base station. The device prevents the mobile system from communicating with the base station during the ban.