Abstract
Highdata rate cognitive radio (CR) systems require high speed Analog-to-Digital Converters (ADC). This requirement imposes many restrictions on the realization of the CR systems. The necessity of high sampling rate can be significantly alleviated by utilizing analog to information converter (AIC). AIC is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal has a sparse representation in some dictionary, which can be recovered from a small number of linear projections of that signal. This paper proposes an efficient spectrum sensing technique based on energy detection, compression sensing, and de-noising techniques. De-noising filters are utilized to enhance the traditional Energy Detector performance through Signal-to-Noise (SNR) boosting. On the other hand, the ordinary sampling provides an ideal performance at a given conditions. A near optimal performance can be achieved by applying compression sensing. Compression sensing allows signal to be sampled at sampling rates much lower than the Nyquist rate. The system performance and ADC speed can be easily controlled by adjusting the compression ratio. In addition, a proposed energy detector technique is introduced by using an optimum compression ratio. The optimum compression ratio is determined using a Genetic Algorithm (GA) optimization tool. Simulation results revealed that the proposed techniques enhanced system performance.
Highlights
There is a large demand on high data rate wireless services over spectrum-based communications
This paper proposes an efficient spectrum sensing technique based on energy detection, compression sensing, and de-noising techniques
It is worth noting that for compression ratios greater than 60%, the curves using Compressive Sensing (CS) become very close to the Receiver Operating Characteristics (ROC) curves using the ordinary sampling
Summary
There is a large demand on high data rate wireless services over spectrum-based communications. Energy detection is the most widely used technique for spectrum sensing because of its low computational and implementation complexities as mentioned in [1]. Sensing of a wideband spectrum is a very challenging problem due to its high sampling rate requirements. This led to a complex and an expensive hardware problem. CS is introduced to reduce the number of samples required to acquire the spectrum, by exploiting the unique sparsity in the wide-band spectrum. A new high performance spectrum sensing technique is introduced. It is based on ED, CS, and De-noising techniques.
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