Backscatter Placement Calibration for 3D RF Energy Heatmaps
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Solution Overview
Problem
Backscatter Devices (BKDs) face challenges in receiving maximum ambient energy for optimal performance, as their deployment location significantly influences their ability to harness and utilize ambient energy, especially for active BKDs that require energy storage before use.
Innovation Solution
A method for BKD placement and calibration that involves identifying and mapping ambient energy sources in a 3D space, predicting energy availability using survey devices, and recommending optimal placement locations based on energy heatmaps to maximize energy reception, with the option to adjust or add energy sources to meet energy budgets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If BKDs are deployed without placement calibration, then deployment is simple and quick, but ambient energy reception is insufficient and performance is suboptimal
Solution Approach 1:
The system performs preliminary mapping of ambient energy sources and characterization of energy availability at different locations before actual BKD deployment. This advance preparation creates an energy heatmap that guides optimal placement, ensuring BKDs are positioned in locations with sufficient ambient energy without requiring complex real-time adjustments during deployment
Solution Approach 2:
The patent introduces an intermediary calibration system consisting of survey devices and a controller that mediates between the BKDs and ambient energy sources. This intermediary layer performs measurements, creates energy maps, and provides placement recommendations, simplifying the deployment process while ensuring optimal energy reception
2Measurement precision
If survey devices are placed throughout the 3D space to map ambient energy, then energy availability is accurately measured, but device deployment complexity and time increase
Solution Approach 1:
The 3D deployment space is segmented into multiple sub-spaces, and survey devices are strategically placed in selected sub-spaces rather than uniformly throughout the entire space. This segmentation approach captures sufficient ambient energy variation data while reducing the total number of measurement points required, thereby decreasing deployment time while maintaining measurement precision
Solution Approach 2:
The system performs measurements at a sufficient number of strategic locations rather than attempting to measure every possible point in the 3D space. By selecting representative sub-spaces and positions, the system achieves adequate measurement precision for optimal BKD placement without the excessive time cost of exhaustive sampling
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach ensures that BKDs receive the maximum possible ambient energy, enhancing their performance and the reliability of backscattered frames being received by network devices, while maintaining or optimizing WiFi coverage.
Implementation Method 1
Backscatter Devices (BKDs) use ambient energy, for example, Radio Frequency (RF) signals, to transmit data without a power source such as a battery or a connection to electricity
Data Source
AI summary
Backscatter Device (BKD) placement and placement calibration may be provided. A plurality of ambient energy sources of a Three-Dimensional (3D) space may be caused to transmit charging frames for Backscatter Devices (BKDs). Each of the charging frames may include a payload having a Media Access Control (MAC) address of transmitting ambient energy source. An amount of ambient energy received from the charging frames of the plurality of ambient energy sources and each contributing source may be received from survey devices placed at positions along a sub-space of the 3D space. The amount of ambient energy available from each contributing source at each positions along the sub-space per predetermined time period may be predicted based on the amount of ambient energy received from the plurality of ambient energy sources.