Systems and methods for optimizing network topology based on access point localization
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Solution Overview
Problem
Conventional wireless networks in complex environments like Multi-Dwelling Units (MDUs) face challenges in maintaining consistent and high-quality connections for client devices as they move around, leading to service interruptions, degraded performance, and inefficient resource management due to reactive handoff mechanisms and interference from multiple access points.
Innovation Solution
A computerized framework that predicts client device movement, dynamically manages network resources, and executes seamless handoffs between access points by gathering building layout and AP locations, tracking device movement, and creating virtual tunnels to ensure uninterrupted connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive handoff mechanisms are used based on signal strength thresholds, then the network can handle basic device transitions, but service interruptions and connection losses occur during handoff
Solution Approach 1:
The system performs preliminary actions by predicting client device movement paths using machine learning models and pre-establishing handoff sequences before actual handoff is needed. This allows the network to prepare resource reservations and configure APs in advance, eliminating handoff delays and preventing service interruptions.
Solution Approach 2:
The system dynamically adjusts handoff parameters based on real-time client movement patterns and network conditions. Instead of using fixed signal strength thresholds, the system continuously updates prediction models and adapts handoff sequences to match actual client behavior, improving connection reliability while minimizing delays.
2Area of stationary object
If multiple access points are deployed in close proximity to cover complex environments, then coverage area is improved, but interference and overlapping coverage areas increase
Solution Approach 1:
The system applies local quality by assigning different roles and transmission parameters to individual APs based on their specific locations and predicted client traffic patterns. Each AP is optimized for its local coverage area with tailored power levels and channel assignments, reducing interference while maintaining comprehensive coverage in complex environments.
Solution Approach 2:
The system introduces a centralized controller as an intermediary that coordinates between multiple APs. This controller uses machine learning predictions to manage resource allocation, power levels, and channel assignments across the network, resolving interference issues while preserving extended coverage area.
3Reliability
If traditional re-authentication and re-association processes are used during handoff, then network security is maintained, but connection delays and performance degradation occur
Solution Approach 1:
The system performs preliminary authentication and association setup during the prediction phase, before the actual handoff occurs. Resource reservations and security credentials are prepared in advance based on predicted client movement, allowing seamless handoff without re-authentication delays while maintaining network security.
4Ease of manufacture
If signal strength thresholds are used for handoff decisions, then the handoff process is simple to implement, but the decisions do not correlate with optimal path of movement or network conditions
Solution Approach 1:
The system changes the parameters used for handoff decisions from simple signal strength thresholds to complex machine learning predictions that incorporate client movement patterns, historical data, and real-time network conditions. This transformation maintains implementation feasibility while dramatically improving handoff optimality and connection reliability.
Data Source
AI summary
Disclosed are systems and methods for optimizing wireless network topology are disclosed, particularly for use in complex environments, such as Multi-Dwelling Units (MDUs). The disclosed framework provides uninterrupted wireless connectivity and an enhanced user experience by dynamically managing network connections and executing seamless handoffs between APs. The framework manages connections to wireless access points (APs) based on the physical layout of the environment and/or user behavior. The framework effectuates receiving data on the network's physical structure and AP locations, tracking client device movement, and predicting the client device's path to proactively manage network resources. The framework can reserve resources at APs along the predicted path and establish a virtual tunnel to facilitate seamless handoff between APs as the client device moves. Adjustments to the network resources and virtual tunnel are made in response to changes in the client device's movement, which includes skipping APs in some embodiments.