Compressed-Sampling Circuits for Interferer Identification
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Solution Overview
Problem
The increasing demand for access to the electromagnetic spectrum by various devices, such as personal health monitoring devices and smart cars, poses a challenge as the current paradigm of pre-allocating spectrum may not guarantee access due to spectrum congestion, necessitating the development of cognitive radio-based dynamic shared spectrum access systems for efficient spectrum sensing and utilization.
Innovation Solution
The implementation of compressed-sampling circuits that include a low noise amplifier, passive mixer, local oscillator, analog-to-digital converter, and digital baseband circuit, configured to identify interferers using a compression-sampling digital signal processor, enabling energy-efficient and rapid wideband signal detection by modulating the local oscillator with pseudo-random sequences to detect frequency locations of interferers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectrum sensing methods are used to detect interferers in a crowded electromagnetic spectrum, then the system can identify frequency locations of interferers, but the process consumes excessive energy and time, reducing productivity and increasing use of energy
Solution Approach 1:
The wideband spectrum is divided into multiple sub-bands, and the sensing process is segmented into sequential stages. The LNA processes the full bandwidth signal, which is then divided and processed by multiple parallel ADCs and digital signal processing chains. This segmentation allows parallel processing of different frequency portions, significantly improving sensing speed while maintaining detection accuracy through coordinated processing of all segments.
Solution Approach 2:
The system employs periodic sampling of the spectrum using compressed sensing techniques. Instead of continuously monitoring all frequencies, the system takes periodic measurements at strategically selected time instances and frequency points. This periodic action reduces the total measurement time and energy consumption while still capturing interferer presence through the periodic nature of the sampling pattern.
2Measurement precision
If wideband spectrum monitoring is implemented to detect all potential interferers, then the system can identify frequency locations across the entire spectrum, but the energy consumption increases significantly
Solution Approach 1:
The system performs partial spectrum sensing by focusing measurements on specific frequency regions and time intervals rather than continuously monitoring the entire bandwidth. Compressed sensing algorithms enable the system to reconstruct the complete spectrum state from a reduced set of measurements, achieving full spectrum coverage with less than 100% of the measurements that would be required for exhaustive monitoring, thereby reducing energy consumption.
Solution Approach 2:
The wideband spectrum monitoring is segmented into multiple narrower bandwidth processing channels. Each channel processes a portion of the total bandwidth with lower power consumption. The LNA amplifies the full bandwidth signal, but subsequent processing stages divide and conquer the bandwidth, allowing energy-efficient processing of segmented spectral portions that are then combined to achieve complete spectrum coverage.
3Productivity
If rapid spectrum sensing is performed to quickly identify available frequency gaps, then the productivity and response time improve, but the measurement precision and reliability of interferer detection deteriorates
Solution Approach 1:
The system performs preliminary coarse detection to quickly identify potential interferer locations, then applies refined processing only to those specific regions. The LNA and initial filtering stages prepare the signal by removing obvious out-of-band components and amplifying the bandwidth of interest, creating a pre-processed signal that enables faster subsequent processing without sacrificing detection reliability for the critical frequency regions.
Solution Approach 2:
Compressed sensing algorithms serve as an intermediary between rapid sampling and reliable detection. The algorithms process the rapidly acquired undersampled signal data, using mathematical reconstruction techniques to recover accurate interferer presence information. This intermediary processing layer enables the system to achieve both rapid sensing speed and high detection reliability by bridging the gap between fast acquisition and accurate measurement.
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
These circuits effectively identify interferers, enabling efficient dynamic shared spectrum access, thereby addressing the challenge of spectrum congestion and ensuring access to a shared pool of frequencies, facilitating the coexistence of diverse devices in a crowded electromagnetic environment.
Implementation Method 1
a low noise amplifier (LNA)... The LNA has an input that receives a radio frequency (RF) signal
Implementation Method 2
a passive mixer... The passive mixer has a first input coupled to an output of the LNA. The LO source has an output coupled to a second input of the passive mixer
Implementation Method 3
In a first mode, the LO source outputs a modulated LO signal that is formed by modulating a local oscillator signal with a pseudo-random sequence
Implementation Method 4
a low pass filter... The low pass filter has an input coupled to an output of the passive mixer
Implementation Method 5
an analog-to-digital converter (ADC)... The ADC has an input coupled to an output of the low pass filter
Implementation Method 6
a compression-sampling digital signal processor (DSP)... The compression-sampling DSP is configured to output identifiers of frequency locations of interferers
Data Source
AI summary
Circuits for identifying interferers using compressed-sampling, comprising: a low noise amplifier (LNA); a passive mixer having a first input coupled to an output of the LNA; a local oscillator (LO) source having an output coupled to a second input of the passive mixer; a low pass filter having an input coupled to an output of the passive mixer; an analog-to-digital converter (ADC) having an input coupled to the output of the low pass filter; a digital baseband (DBB) circuit having an input coupled to an output of the ADC; and a compression-sampling digital-signal-processor (DSP) having an input coupled to the output of the DBB circuit, wherein the compression-sampling DSP is configured to output identifiers of frequency locations of interferers, wherein, in a first mode, the LO source outputs a modulated LO signal that is formed by modulating an LO signal with a pseudo-random sequence.