Multi-band Compressive Sensing for RF Telemetry Logging
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Solution Overview
Problem
Current wireless network systems face inefficiencies in gathering, storing, and transmitting RF statistics due to high data overhead, which reduces network capacity, increases power consumption, and incurs additional costs, especially in green networks where resource conservation is crucial.
Innovation Solution
Implementing a multiband compressive sensing framework that generates and combines compressive sensing schedules across multiple frequency bands to reduce the volume of data gathered and communicated, using a sensing matrix combiner to create a spatial distribution and scheduled time slots for access points, thereby minimizing resource usage and data loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional RF statistics gathering methods are used with dedicated receivers and sensors, then measurement accuracy is improved, but network capacity is reduced and device complexity increases
Solution Approach 1:
The patent applies multi-functionality by enabling access points to perform both their primary wireless communication function and RF statistics sensing function simultaneously. The access points use their existing transceivers to gather RF telemetry data without requiring dedicated sensing hardware, thus maintaining network capacity while achieving measurement objectives.
Solution Approach 2:
The system implements self-service by having access points autonomously gather and process RF statistics using their own internal resources (transceivers and processors) without external assistance. The access points self-configure sensing schedules and transmit compressed data directly to the network controller, eliminating the need for separate sensing nodes.
2Measurement precision
If redundant transceiver nodes are deployed for sensing, then measurement capability is improved, but network cost increases
Solution Approach 1:
The patent eliminates the need for redundant sensing nodes by making existing access points multi-functional. The same access points that provide wireless service also perform RF statistics measurement, avoiding additional hardware costs while maintaining comprehensive measurement coverage.
Solution Approach 2:
Access points use their own built-in transceivers and processors to perform sensing functions, eliminating the need for separate dedicated sensing hardware. This self-service approach reduces network cost by reusing existing infrastructure components.
3Measurement precision
If RF statistics data is communicated to fusion nodes and cloud entities, then diagnostic capability is improved, but data traffic increases and payload capacity is reduced
Solution Approach 1:
The patent extracts only the essential RF statistics information from the raw wireless data and transmits only this compressed summary to the network controller. By taking out only the necessary diagnostic information rather than transmitting all raw data, the system reduces data traffic volume while maintaining diagnostic capability.
Solution Approach 2:
The system changes the data representation parameters by compressing RF statistics into a reduced set of representative values. The access points transmit compressed telemetry data with fewer data points that capture the essential characteristics of RF conditions, thereby reducing data volume while preserving diagnostic information.
4Measurement precision
If comprehensive RF statistics are gathered and stored, then measurement accuracy is improved, but power consumption increases
Solution Approach 1:
The patent extracts only the essential RF statistics parameters needed for network diagnostics and excludes redundant data collection. By focusing measurement efforts on key metrics rather than comprehensive data gathering, the system achieves accurate measurements with reduced power consumption.
Solution Approach 2:
The system optimizes power consumption by changing measurement parameters to collect only necessary RF statistics. The access points adjust their sensing intensity and data transmission frequency based on network conditions, reducing energy usage while maintaining measurement accuracy for effective network management.
Data Source
AI summary
In one embodiment, an apparatus comprises a compressive sensing schedule generator configured to generate a plurality of compressive sensing schedules, wherein each of the plurality of compressive sensing schedules is for each of a plurality of frequency bands of a network, wherein the network comprises a plurality of access points and a plurality of clients, and a sensing matrix combiner configured to combine the plurality of compressive sensing schedules into a resulting schedule that comprises a spatial distribution and a scheduled time slot for each of the plurality of access points.