Wireless Sensor Sampling Using Sparse Uplink Reconstruction
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Solution Overview
Problem
Wireless communication systems face challenges in efficiently sampling signals due to the high power and cost requirements of Nyquist-rate sampling, particularly in achieving robust, high-bandwidth, real-time communication with high-user density, where existing methods do not effectively exploit signal sparseness for reduced sampling rates.
Innovation Solution
The implementation of compressive sampling in sensor-based wireless communication systems, where sensors operate at a lower effective sampling rate by using a sensing matrix to compressively sample uplink signals, reducing the number of samples needed while maintaining signal integrity, and leveraging sparse signal properties to minimize power consumption and deployment costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Nyquist-rate sampling is used to ensure exact signal recovery, then measurement precision is improved, but use of energy and device cost worsen due to requiring high-quality components and substantial power
Solution Approach 1:
The patent changes the fundamental sampling parameter from Nyquist rate (2B) to a reduced rate based on signal sparseness k, where k << 2B. This parameter change allows sampling at a lower rate while maintaining signal recovery accuracy through compressive sensing algorithms that exploit the sparse representation of signals in certain bases.
Solution Approach 2:
The patent replaces the traditional mechanical/electronic sampling system requiring high-speed ADCs and high-quality components with a compressive sampling system that uses random or incoherent sampling matrices. This substitution allows lower-quality, lower-power components to achieve the same measurement precision through mathematical processing.
2Measurement precision
If Nyquist-rate sampling is used to capture high-frequency signals, then measurement precision is improved, but device complexity and cost worsen due to requiring expensive high-quality components
Solution Approach 1:
The patent changes the sampling rate parameter from the conservative Nyquist rate to a sparseness-based rate, fundamentally altering the sampling approach. This enables the use of simpler, lower-cost components that cannot support Nyquist-rate sampling, while still achieving accurate signal recovery through compressive sensing reconstruction algorithms.
Solution Approach 2:
The patent substitutes complex high-precision hardware sampling systems with a simplified system using random sampling matrices and mathematical reconstruction. This substitution eliminates the need for expensive high-quality components required by traditional Nyquist sampling, achieving the same measurement precision through algorithmic processing.
3Ease of operation
If traditional sampling methods are used without exploiting signal sparseness, then ease of operation is improved through simple uniform sampling, but productivity worsens due to inability to reduce sampling rate for high-bandwidth communication
Solution Approach 1:
The patent introduces sparseness-based sampling rate as a new parameter that adapts to signal characteristics. Instead of fixed uniform sampling, the system dynamically determines sampling rate based on the sparseness k of the signal in a given basis, enabling both high productivity through rate reduction and maintained operational simplicity through automated sparseness estimation.
Solution Approach 2:
The compressive sampling system performs self-adjustment by automatically estimating signal sparseness and adapting the sampling rate accordingly. This self-service capability eliminates the need for manual optimization while achieving high productivity through automated exploitation of signal structure, maintaining ease of operation without sacrificing performance.
Data Source
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AI summary
Methods, devices and systems for sensor-based wireless communication systems using compressive sampling are provided. In one embodiment, the method for sampling signals comprises receiving, over a wireless channel, a user equipment transmission based on an S-sparse combination of a set of vectors; down converting and discretizing the received transmission to create a discretized signal; correlating the discretized signal with a set of sense waveforms to create a set of samples, wherein a total number of samples in the set is equal to a total number of sense waveforms in the set, wherein the set of sense waveforms does not match the set of vectors, and wherein the total number of sense waveforms in the set of sense waveforms is fewer than a total number of vectors in the set of vectors; and transmitting at least one sample of the set of samples to a remote central processor.