Adaptive Neighbor List Mapping for Wireless Network RF Optimization
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Solution Overview
Problem
Existing wireless network technologies face challenges in efficiently mapping neighbor lists while conserving client device battery power and minimizing disruption, especially at the edge of a cell where signal quality is non-optimal, due to varying modes of scanning that can be resource-intensive or outdated.
Innovation Solution
Implementing a process for client devices to periodically provide their view of the network, using a Digital Network Architecture Center (DNAC) to manage requests and build comprehensive maps that balance power conservation and minimal disruption, incorporating adaptive scanning modes and compressive sensing techniques to optimize data feedback and location determination based on signal strength regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If client devices perform frequent network scanning to provide accurate neighbor list views, then mapping precision is improved, but battery power consumption increases
Solution Approach 1:
The system implements periodic scanning requests where the AP requests neighbor list information from client devices at predetermined time intervals rather than continuously. This periodic approach maintains mapping accuracy while significantly reducing battery power consumption compared to continuous scanning.
Solution Approach 2:
The system performs preliminary actions by having client devices provide their neighbor list views in advance during periodic intervals. The AP then uses compressive sensing techniques to process this preliminary data and construct accurate network maps before actual troubleshooting or optimization is needed, reducing the need for intensive real-time scanning.
2Measurement precision
If client devices perform comprehensive network scanning to ensure accurate location determination, then mapping precision is improved, but disruption to client device operation increases
Solution Approach 1:
The system applies compressive sensing techniques that allow accurate location determination using partial scanning data rather than requiring complete comprehensive scans. The AP can construct accurate network maps from incomplete or reduced neighbor list information, maintaining location accuracy while minimizing disruption to client device operations.
3Loss of information
If the AP requests neighbor list information frequently from client devices, then network map accuracy is improved, but client device battery power is depleted faster
Solution Approach 1:
The system implements a feedback mechanism where client devices provide neighbor list information to the AP at periodic intervals. The AP uses compressive sensing to process this feedback data and construct accurate network maps. The periodic feedback approach ensures information accuracy while the efficient processing algorithm minimizes the energy burden on client devices.
Data Source
AI summary
Neighbor list adaptive mapping may be provided. A request to a client device to provide a client device view of a network may be sent periodically at a time interval. A length of the time interval may be dependent on a condition at the client device and identity data may be associated with the client device. In response to sending the request to the client device, data corresponding to the client device view of the network may be received. Then, in response to receiving the data, a map of the network may be updated based on the received data corresponding to the client device view of the network. The map may be associated with the identity data associated with the client device.