MLAP Prediction Model for Proactive Vehicle Handoffs
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Solution Overview
Problem
Existing wireless communication systems, particularly in mobile environments like vehicles, face challenges in maintaining stable connections due to weak signals or interference, leading to delays in handoff processes and potential loss of connectivity.
Innovation Solution
The implementation of a machine learning access point (MLAP) prediction model that uses data collected from vehicles and access points to predict the best access points for vehicles to connect to as they move, thereby facilitating proactive handoffs and reducing connectivity issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a moving device waits until it detects loss of communication with the current AP before connecting to another AP, then the connection stability is improved by avoiding premature handoffs, but the handoff time increases and connectivity reliability deteriorates
Solution Approach 1:
The patent applies preliminary action by having the mobile device proactively initiate handoff procedures before actual connection loss occurs. The device uses predictive models to forecast when signal degradation will become problematic and triggers handoff in advance, thereby avoiding the delay inherent in reactive approaches while maintaining connection stability.
Solution Approach 2:
The patent implements feedback mechanisms where the mobile device continuously monitors signal strength, handoff history, and network conditions. This feedback loop enables the device to learn from previous handoff experiences and adjust future handoff timing, resolving the contradiction between premature handoffs and necessary connection stability.
2Device complexity
If the new AP does not have information about the previously serving AP, then the network simplicity is maintained, but the service quality deteriorates due to delays in obtaining service information
Solution Approach 1:
The patent applies preliminary action by having the serving AP prepare and transmit service information to the target AP before the mobile device actually connects. This advance information transfer eliminates the delay the new AP would otherwise experience in obtaining service entitlement information, improving service quality without requiring complex real-time queries during handoff.
Solution Approach 2:
The patent uses the mobile device itself as an intermediary that facilitates information transfer between APs. The device's connection establishment actuates a information transfer mechanism where service details are routed from the serving AP through the mobile device to the target AP, ensuring seamless information exchange while maintaining network simplicity.
3Ease of operation
If the mobile device selects the AP with the highest signal strength, then the ease of operation is improved by automatic selection, but the connectivity reliability deteriorates due to signal interference and weak connections
Solution Approach 1:
The patent implements feedback by having the mobile device continuously monitor connection quality metrics beyond simple signal strength, including packet loss rates, throughput, and historical handoff performance. This multi-parameter feedback enables the device to make more reliable AP selection decisions while maintaining the ease of automatic operation.
Solution Approach 2:
The patent applies parameter changes by expanding the selection criteria from a single signal strength parameter to multiple quality indicators. The device dynamically adjusts handoff triggers based on real-time connection conditions, transitioning from static signal-based selection to dynamic multi-parameter evaluation, thereby improving connectivity reliability without sacrificing operational simplicity.
Data Source
AI summary
Data is collected at access points about mobile communications systems, e.g., vehicles, use of access points and the quality of the connections that are provided. The collected information is provided to a machine learning access point (MLAP) model training device that generates one or more MLAP prediction models with each model corresponding to a geographic area in which access points are located. Vehicles provide location, acceleration and/or other information, such as a user entered destination. A vehicle request for an AP recommendation includes this information which allows the AP prediction engine to predict a path of travel. The MLAP prediction engine uses the stored models and/or information about AP loading, to predict what AP a vehicle requesting an AP recommendation should attach to next. The predicted AP is returned as a handoff recommendation along with the location and/or time the vehicle should initiate the handoff to the recommended AP.