KEYWORDS: Signal detection, Modulation, Signal processing, Receivers, Signal to noise ratio, Clocks, Compressed sensing, Environmental sensing, Telecommunications, Reconstruction algorithms
The need to efficiently and effectively monitor the frequency spectrum for identification of unoccupied bands is
essential in communication systems such as Cognitive Radio (CR), battlefield communications, etc. The Nyquist Folding
Analog-to-Information Receiver (NYFR) which is based on the theory of Compressed Sensing has been proposed
recently to address this problem in a sparse environment. Although, typical CS techniques, involve random projections
followed by a computationally intensive signal reconstruction process, the methods used in NYFR does not requires the
laborious l1 minimization algorithm. The NYFR performs analog compression via a non-uniform sampling process that
induces a chirp-like modulation on each received signal. Signal parameters can simply be determined by using timefrequency
analysis techniques without full signal reconstruction. This paper revisits the detection problem of using
NYFR for information recovery for appropriate frequency detection when the original signal in the presence of both the
additive white Gaussian noise and Rice multipath fading. An automatic detection algorithm was also developed to
determine the detected frequency parameters without looking at the FFT spectrogram plot.
Compressive sensing (CS) relies on the fact that CS sampled signals are much closer to their information rate rather than
the signal bandwidth. This attribute helps to provide the much needed benefits of reduced storage or transmission
bandwidth for the next-generation broadband wireless communications and to overcome the hardware limitations for
wideband spectrum sensing in dynamic spectrum access. In order to opportunistically reuse holes in the spectrum, it is
essential to have a spectral detection and estimation technique that is capable of sensing and identifying available
frequency bands. Conventional methods of detection are saddled with the high sampling rate requirement of Nyquist
rate, however timing requirements limits the number of samples that can be taken from the signals. In a situation
whereby the signal spectrum in open-access networks is sparse in nature, this work develops a detection mechanism for
identifying spectrum holes using compressive sensing based algorithm technique. Different compressive sensing
reconstruction algorithms are investigated and FFT spectrogram with an edge detection algorithm is used to identify the
holes in the spectrum. A quick wideband spectrum sensing can be achieved using the compressive sensing technique and
a more refined sensing can be used by any of the other available methods such as energy detection. The proposed model
is evaluated in different fading propagation environments, taking into account of both additive and multiplicative noise.
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