ML Location Prediction for Venue Networks
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Solution Overview
Problem
Existing wireless communication systems, such as 5G NR and LTE, do not provide detailed location information, services, or directions specific to emergencies or traffic patterns within campus or venue networks, which can hinder users' ability to seek shelter or share their location with first responders during emergencies.
Innovation Solution
A system comprising at least one baseband unit (BBU) entity, one or more radio units, and one or more antennas, communicatively coupled to implement a base station for wireless communication. This system includes a machine learning computing system that receives time and location data to predict density in various location areas within the cell, determining a target location based on this data, and sending this target location to user equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a centralized or cloud radio access network (C-RAN) is implemented with baseband units and radio units, then wireless communication service is provided to user equipment, but the system does not provide detailed location information, services, or directions specific to emergencies or traffic patterns within campus or venue networks
Solution Approach 1:
The system segments the cell into multiple location areas and uses machine learning to predict density for each location area independently, enabling detailed location-specific information and emergency services for different zones within the network coverage area
Solution Approach 2:
A machine learning computing system is introduced as an intermediary component that receives time and location data, predicts density patterns, and determines target locations, bridging the gap between basic wireless communication and advanced location-based emergency services
2Loss of information
If machine learning computing system is added to predict density and determine target locations, then detailed location information and emergency services are provided, but system complexity increases
Solution Approach 1:
The machine learning computing system performs multiple functions including receiving time and location data, predicting density for multiple location areas, determining target locations, and sending recommendations, allowing a single added component to provide comprehensive location-based services without requiring multiple separate systems
3Reliability
If machine learning is used to predict density for multiple location areas, then overcrowding at safe zones is reduced, but computational resources and processing time increase
Solution Approach 1:
The system predicts density for multiple location areas rather than just a single target location, and provides recommendations based on predicted density patterns, allowing users to make informed decisions about destination selection while distributing load across multiple potential destinations rather than concentrating all traffic to one safe zone
Data Source
AI summary
Systems and methods for providing machine learning based location and directions for venue and campus network are provided. In one example, a system includes a BBU entity and RU(s) communicatively coupled to the BBU entity. The system further includes antenna(s) communicatively coupled to the RU(s), and each respective RU is communicatively coupled to a respective subset of the antenna(s). The BBU entity, the RU(s), and the antenna(s) are configured to implement a base station for wirelessly communicating with UEs in a cell. The system further includes a machine learning computing system configured to receive time and location data and determine a predicted density for location areas in the cell based on the time and location data. The system is configured to determine a target location based on the predicted density for the location areas in the cell and send the target location to a first UE in the cell