Compressive Sampling for Sparse Uplink Signal 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 traditional Nyquist-rate sampling, especially in high-frequency applications, and the need for advanced equipment like LTE systems that demand more efficient 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 low-cost, low-power sensors and a sensing matrix to compressively sample uplink signals and generate sensed signals that can be processed by base stations for signal reconstruction.
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 sampling to compressive sampling, reducing the sampling rate below the Nyquist rate. This is enabled by exploiting the sparsity of the signal in a transform domain, allowing accurate reconstruction with fewer samples. The sensing matrix is designed to capture sufficient information at this reduced sampling rate, resolving the contradiction between measurement precision and energy consumption.
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 parameter from Nyquist-rate to compressive sampling rate, which allows the use of lower-cost components. The compressive sampling framework with sensing matrix enables accurate signal reconstruction without requiring high-quality analog components that would be necessary for high-rate sampling, thus reducing device cost while maintaining measurement precision.
3Use of energy by moving object
If compressive sampling is used to reduce sampling rate and power consumption, then use of energy is improved, but measurement precision worsens due to sampling below Nyquist rate
Solution Approach 1:
The patent applies preliminary transformation of the signal into a sparse representation domain before sampling. By expressing the signal as a sparse combination of basis functions (e.g., wavelets, Fourier basis), the system can capture the essential information with fewer samples. The sensing matrix is designed to work with this sparse representation, ensuring that compressive sampling below Nyquist rate still achieves accurate signal recovery.
4Reliability
If traditional sampling methods are used for signal detection, then reliability is improved, but device complexity worsens due to requiring advanced equipment like LTE systems
Solution Approach 1:
The patent extracts and exploits the sparsity property of the signal, separating this key characteristic from the traditional sampling approach. By identifying and utilizing the sparse structure in the signal representation, the system achieves reliable detection with simpler hardware. The sensing matrix is specifically designed to capture the sparse components, allowing reliable signal detection without requiring complex advanced equipment like traditional LTE systems.
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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.