Compressive Signal Sampling for Low-Power Wireless Detection
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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 high-frequency applications, and the need for advanced equipment like LTE systems that demand improved signal detection and estimation methods.
Innovation Solution
The implementation of compressive sampling techniques in sensor-based wireless communication systems, which exploit the sparseness of input signals to reduce the number of samples needed for reliable representation, using a sensing matrix and sparse representation matrix to compressively sample signals and recover information signals efficiently.
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 compressive sampling rate (M << 2B). By exploiting the sparsity parameter k of the signal in a transformed domain, the system recovers signals at rates far below the Nyquist rate, directly reducing power consumption while maintaining recovery accuracy through optimized recovery algorithms
Solution Approach 2:
The patent extracts and exploits the sparsity property of signals in specific domains (time, frequency, or transformed domains). By identifying and utilizing the k sparse non-zero coefficients, the system removes the need for full Nyquist-rate sampling, achieving energy-efficient signal recovery with M measurement samples where M is much smaller than the Nyquist rate
2Measurement precision
If Nyquist-rate sampling is used to ensure exact signal recovery, then measurement precision is improved, but device cost worsens due to requiring expensive high-quality components
Solution Approach 1:
The patent changes the sampling rate parameter from Nyquist rate to compressive sampling rate, enabling the use of lower-cost components. The system achieves accurate signal recovery with M measurements where M << 2B, eliminating the need for expensive high-quality components required by traditional Nyquist-rate sampling systems
Solution Approach 2:
The patent enables the use of cheaper sampling components by exploiting signal sparsity. Instead of requiring expensive, high-precision components for Nyquist-rate sampling, the system uses simpler, lower-cost components that perform compressive sampling at reduced rates, achieving cost-effective signal recovery
3Use of energy by moving object
If compressive sampling is used to reduce power consumption, then use of energy is improved, but measurement precision worsens due to sampling below Nyquist rate
Solution Approach 1:
The patent changes the sampling approach from time-domain Nyquist sampling to compressive sampling in transformed domains. By exploiting sparsity in time, frequency, or wavelet domains, the system achieves accurate signal recovery with M measurements where M << 2B, maintaining precision while reducing power consumption
Solution Approach 2:
The patent introduces sparsity-exploiting transformation domains as intermediaries between the sampled signal and the original signal. By transforming the signal into domains where it is sparse (time, frequency, or wavelet domains), the system enables accurate recovery from fewer measurements, bridging the gap between low sampling rates and high recovery accuracy
4Measurement precision
If high sampling rates are used to support high-frequency signals, then measurement precision is improved, but use of energy worsens due to substantial power requirements
Solution Approach 1:
The patent changes the sampling rate parameter from high-frequency Nyquist rate (2B) to compressed sampling rate (M). By exploiting the sparsity structure of high-frequency signals in transformed domains, the system achieves accurate detection with fewer measurements, dramatically reducing power consumption while maintaining detection precision
Data Source
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.