Cooperative Spectrum Sensing with Sub-Nyquist Sampling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current spectrum sensing systems in cognitive radio networks face challenges due to the high cost and complexity of implementing high-speed ADCs for Nyquist sampling, and the irregular occupancy of frequency bands by primary users, which complicates the detection process and introduces aliasing effects.
Innovation Solution
A cooperative spectrum sensing system using sub-Nyquist sampling, where secondary user terminals perform energy detection and calculate correct and false alarm probabilities using specific equations, and a fusion center aggregates results to determine the occupancy of frequency bands, allowing for efficient detection with a lower number of samples and reduced complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Nyquist sampling is used for broadband spectrum sensing, then sampling accuracy is improved, but system cost and complexity increase due to requirement of high-speed ADC
Solution Approach 1:
The patent changes the sampling rate parameter from Nyquist rate to sub-Nyquist rate, enabling spectrum sensing without high-speed ADC. By operating below the traditional Nyquist sampling rate and using compressed sensing algorithms, the system achieves accurate spectrum detection while using lower-cost, lower-complexity hardware components.
2Reliability
If high-speed ADC is implemented for Nyquist sampling, then spectrum sensing performance is improved, but system cost increases
Solution Approach 1:
The patent replaces expensive high-speed ADC hardware with cheaper low-speed ADCs by using compressed sensing techniques. The system achieves reliable spectrum sensing performance through intelligent signal processing algorithms rather than relying on expensive hardware, making the system more economically viable for practical deployment.
3Device complexity
If sub-Nyquist sampling is used, then system cost and complexity are reduced, but aliasing effect occurs complicating detection
Solution Approach 1:
The patent introduces compressed sensing algorithms as an intermediary processing step between sub-Nyquist sampling and spectrum detection. These algorithms act as a mediator that reconstructs the original spectrum signal from the undersampled data, eliminating aliasing effects and enabling accurate detection despite the reduced sampling rate.
4Productivity
If sub-Nyquist sampling is used, then number of samples is reduced, but detection accuracy may be compromised
Solution Approach 1:
The patent applies preliminary action by using compressed sensing algorithms to pre-process and reconstruct signals before final detection. This preliminary signal reconstruction ensures that even though fewer samples are taken, the essential spectral information is preserved and accurately recovered, maintaining detection accuracy while improving sampling efficiency.
Data Source
AI summary
Disclosed is a cooperative spectrum sensing system using sub-Nyquist sampling, which include: a plurality of secondary user terminals for detecting a frequency band occupied by a primary user terminal; and a fusion center, wherein each of the secondary user terminals may include: a receiving unit for receiving signals from the primary user terminal; a sampling unit for performing the sub-Nyquist sampling for the received signals at a predetermined down-sampling rate; an energy-detecting unit for detecting the frequency band occupied by the primary user terminal by detecting energy for the sampled signals; and a calculating unit for calculating a correct detection probability and a false alarm probability for the frequency band occupied by the primary user terminal by using spectrum of the frequency band, and for transmitting results of calculation to the fusion center.