Dynamic Ad-Hoc Network Coverage Prediction
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Solution Overview
Problem
Networks face gaps in coverage due to the increasing geographical range of deployment, leading to degraded service quality and potential loss of connectivity, especially as users move into areas with reduced signal strength or obstructions.
Innovation Solution
The system generates ad-hoc networks dynamically based on predictions of user activity and coverage gaps, using data analysis and machine learning to identify areas of low coverage and allocate resources to extend network reach, employing communication devices as repeaters or range-extenders to maintain connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If additional resources are deployed to expand geographical coverage, then the scope of coverage is improved, but the geographical range per resource decreases
Solution Approach 1:
The patent implements dynamic ad-hoc networks that can be generated, modified, or terminated based on real-time predictions of user activity and coverage conditions. This allows the network to adapt its structure dynamically rather than maintaining a static infrastructure, resolving the contradiction by making coverage expansion flexible and demand-driven rather than requiring continuous resource deployment
Solution Approach 2:
The system performs preliminary actions by predicting future user activity and coverage gaps before they occur. Machine learning models analyze historical data to anticipate where coverage will be needed, allowing the system to proactively generate ad-hoc networks in advance, thus expanding coverage without reactive resource deployment
2Reliability
If ad-hoc networks are generated to extend coverage to gap areas, then network connectivity is improved, but system complexity increases
Solution Approach 1:
Communication devices in the ad-hoc network autonomously perform functions such as signal relaying, network formation, and coordination without requiring centralized control for each action. The devices self-organize into mesh networks, automatically routing signals and adapting to network conditions, which maintains connectivity while reducing the operational complexity of managing these distributed networks
Solution Approach 2:
The patent introduces machine learning models and prediction systems as intermediaries between the network infrastructure and user devices. These intermediaries analyze patterns and make intelligent decisions about where and when to generate ad-hoc networks, simplifying the overall system management by centralizing the decision-making logic while distributing the execution across autonomous devices
Data Source
AI summary
Aspects of the subject disclosure may include, for example, generating, at a first point in time, a first prediction regarding a likelihood that a communication device will attempt to connect to a service of a network at a second point in time that is subsequent to the first point in time, generating, at a third point in time that is prior to the second point in time, a second prediction regarding a scope of coverage of the network at the second point in time, and generating, by the processing system, a first ad-hoc network, modifying, by the processing system, a parameter of a second ad-hoc network, or a combination thereof, in accordance with the first prediction and the second prediction to extend the scope of coverage. Other embodiments are disclosed.