Wireless Power Transmitter Channel Learning via Eigenvalue Decomposition
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Solution Overview
Problem
Wireless power networks face challenges in efficiently determining spatial channels between transmitter and receiver antennas, leading to suboptimal power transmission efficiency due to the complexity of channel learning and the need for additional pilot signals.
Innovation Solution
A method involving the transmission of pilot signals, feedback signal reception, derivation of channel matrices, and estimation of spatial channel signatures using the dominant eigenvalue and eigenvector, which reduces computational complexity and allows for scalable channel learning without extra training pilot signals, and optimizing power transmission time into timeslots to maximize delivered power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional channel learning methods are used to determine spatial channels between transmitter and receiver antennas, then channel accuracy can be maintained, but computational complexity increases and additional pilot signals are required
Solution Approach 1:
The patent extracts only the essential channel information (spatial channel signatures) needed for power transmission optimization, rather than computing complete channel matrices. By using feedback signals containing received power indications and applying eigenvalue decomposition to extract dominant spatial characteristics, the system achieves accurate channel determination with reduced computational burden, eliminating the need for complex traditional channel learning algorithms
Solution Approach 2:
The feedback signals originally designed for simple power indication are made multi-functional by using them as the basis for deriving spatial channel signatures. The same feedback mechanism serves both power measurement and channel estimation purposes, eliminating the need for separate pilot signal transmissions while maintaining channel accuracy across multiple receivers
2Measurement precision
If traditional channel learning methods are used to determine spatial channels between transmitter and receiver antennas, then channel accuracy can be maintained, but the system becomes less scalable to multiple receivers
Solution Approach 1:
The feedback signal mechanism is designed to serve multiple receivers simultaneously with the same pilot signal transmission. Each receiver's feedback containing received power indications is processed to derive its own spatial channel signature, allowing the system to scale to any number of receivers without requiring additional training resources per receiver
Solution Approach 2:
The channel learning process is segmented into independent per-receiver processing steps. The transmitter broadcasts a single pilot signal, and each receiver independently processes its own feedback to derive its channel signature. This segmentation allows parallel processing for multiple receivers, enabling linear scaling without increasing overall system complexity
3Measurement precision
If additional pilot signals are transmitted for channel learning, then channel estimation accuracy improves, but transmission time and energy consumption increase
Solution Approach 1:
The feedback signals originally intended solely for power measurement are repurposed to carry channel estimation information. By using the received power indications from normal feedback channels to derive spatial channel signatures through eigenvalue decomposition, the system eliminates the need for separate pilot signal transmissions, saving time and energy while maintaining estimation accuracy
Solution Approach 2:
The system uses its own existing feedback mechanism to serve the additional function of channel estimation. The feedback signals that must be transmitted anyway for power control contain sufficient information for channel signature derivation, making the system self-sufficient and eliminating the need for external pilot signal resources
Data Source
AI summary
The present disclosure relates to channel learning and power transmission in wireless power networks. A method of estimating channels between a transmitter and a plurality of receivers in a wireless power network is described. The transmitter comprises an array of wireless power transmission antennas. The method comprises: transmitting a pilot signal from the wireless power transmission antennas of the transmitter; receiving feedback signals from each receiver of the plurality of receivers, the feedback signals comprising received signal power indications for each respective receiver; calculate channel matrices for channels between the wireless transmission antennas and wireless power reception antennas of each respective receiver by minimizing an objective function of a channel matrix and the received signal power indications for the respective receiver; and estimating spatial channel signatures of the reception antennas on the array of wireless power transmission antennas from the dominant eigenvalue and corresponding eigenvector of the respective channel matrix.