Compressive Spectrum Sensing Using a Bandpass Sampling Architecture

Abstract

Abstract-Fast and reliable detection of available channels (i.e., those temporarily unoccupied by primary users) is a fundamental problem in the context of emerging cognitive radio networks, without an adequate solution. The (mean) time to detect idle channels is governed by the front-end bandwidth to be searched for a given resolution bandwidth. Homodyne receiver architectures with a wideband RF front-end followed by suitable channelization and digital signal processing algorithms, are consistent with speedier detection, but also imply the need for very high speed analog-to-digital converters (ADCs) that are impractical and/or costly. On the other hand, traditional heterodyne receiver architectures consist of analog band-select filtering followed by down-conversion that require much lower rate ADCs, but at the expense of significant scanning operation steps that constitute a roadblock to lowering the scan duration. In summary, neither architecture provides a satisfactory solution to the goal of (near) real-time wideband spectrum sensing.In this work, we propose a new compressive spectrum sensing architecture based on the principle of under-sampling (or bandpass sampling) that provides a middle ground between the above choices, i.e., our approach requires modest ADC sampling rates and yet achieves fast spectrum scanning. Compared to other compressive spectrum sensing architectures, the proposed method does not require a high-speed Nyquist rate analog component. A performance model for the scanning duration is developed based on the mean time to detect all idle channels. Numerical results show that this scheme provides significantly faster idle channel detection than the conventional serial search scheme with a heterodyne architecture.

Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion

“…In principle, this A2I conversion strategy undersamples the input signal with multiple branches (i.e., a bank of parallel bandpass sampling stages) with sampling rates that differ from one branch to the other. There exist two main instances of this concept, namely multi-rate sampling (MRS) that uses a fixed set of sampling frequencies for each branch [8], [31], [32] and the Nyquist-folding receiver (NYFR) that modulates the sampling frequencies [41]. Both approaches rely on the fact that the signal of interest is aliased at a particular frequency when undersampled at a given rate on a given branch, but the same signal may experience aliasing at a different frequency when sampled at a different rate on another branch.…”

Hardware-Algorithms Co-Design and Implementation of an Analog-to-Information Converter for Biosignals Based on Compressed Sensing

“…As far as integrated prototypes are concerned, a few CSbased solutions have been proposed so far in the literature [16]- [21]. Most of them are based on the Random Modulation Pre-Integration (RMPI) architecture, which was shown to be the most versatile in terms of capability to acquire signals that are sparse regardless of their basis [8].…”

Analog‐to‐Digital Conversion for Cognitive Radio: Subsampling, Interleaving, and Compressive Sensing

“…Due to the underutilization of the radio spectrum, its occupancy can be usually considered sparse. Under this assumption, CS technique is a feasible alternative to many cognitive radio applications, where a reaction in real time is necessary, such as dynamic spectrum sensing [19,20], interferer's mitigation [21], power spectrum estimation [22], or sparsity order estimation [23]. All these applications are focused on identifying spectrum opportunities over a wideband spectrum rapidly and accurately, so that we can share and exploit these limited radio resources in real time.…”

“…A homodyne receiver with a wideband front end is able to implement rapid detections, but it implies the use of high-speed ADCs, which make these solutions impractical. A heterodyne receiver, where the signal channels are selected individually for downconversion to baseband, can be implemented by using a lower data-rate ADC, but they are impractical for spectrum sensing applications because the time to detect idle channels is higher than the homodyne case [19], due to the fact that heterodyne receivers consume time for switching to a new channel, apart from the time required by the detection algorithm, once the channel is selected. As a compromise solution, subsampling-based receivers enable us to use a low-cost ADC and provide a fast spectrum scanning [19].…”

“…However, the performance of these sub-Nyquist schemes depends on the sparsity level, that is, the employed reconstruction algorithms will only work accurately if most of the channels in the spectrum are idle, leading in many cases to higher computational complexity and longer signal-processing times. Furthermore, although the ADCs in Figure 13 work at a sub-Nyquist rate f s , the detector requires analog components capable to work at Nyquist rate: for the nonuniform sampler, a Nyquist rate clock is required for the shifters (ϕ i in Figure 13a); for the random pre-integrator, a Nyquist rate random generator (p i (t)i nFigure 13b before the integrator h(t)) is required as well [19]. Besides the high-rate requirement for high bandwidth, a higher frequency of the digital parts in a mixed-signal system involves a higher injected noise into the analog parts.…”