Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver

Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs with increased portability, higher levels of sensing capabilities, and more powerful autonomy. These features make them attractive for many recent applica-tions, potentially increasing the shortage of spectrum resources. In this paper, wideband spectrum sensing augmented technology is discussed for distributed UAV swarms to improve the utilization of spectrum. However, the sub-Nyquist sampling applied in existing schemes has high hardware complexity, power consumption, and low recovery efficiency for non-strictly sparse conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the distributed UAV swarms, which can theoretically achieve full-band spectrum detection and reception using a single analog-to-digital converter (ADC) at low speed for all circuit components. There is a focus on the sensing model of two multichannel scenarios for the distributed UAV swarms, one with a complete functional receiver for the UAV swarm with RIS, and another with a decentralized UAV swarm equipped with a complete functional receiver for each UAV element. The key issue is to consider whether the application of RIS technology will bring advantages to spectrum sensing and the data fusion problem of decentralized UAV swarms based on the NYFR architecture. Therefore, the property for multiple pulse reconstruction is analyzed through the Gershgorin circle theorem, especially for very short pulses. Further, the block sparse recovery property is analyzed for wide bandwidth signals. The proposed technology can improve the processing capability for multiple signals and wide bandwidth signals while reducing interference from folded noise and subsampled harmonics. Experiment results show augmented spectrum sensing efficiency under non-strictly sparse conditions

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201

Compressive Sensing of Analog Signals Using Discrete Prolate Spheroidal Sequences

Compressive sensing (CS) has recently emerged as a framework for efficiently capturing signals that are sparse or compressible in an appropriate basis. While often motivated as an alternative to Nyquist-rate sampling, there remains a gap between the discrete, finite-dimensional CS framework and the problem of acquiring a continuous-time signal. In this paper, we attempt to bridge this gap by exploiting the Discrete Prolate Spheroidal Sequences (DPSS's), a collection of functions that trace back to the seminal work by Slepian, Landau, and Pollack on the effects of time-limiting and bandlimiting operations. DPSS's form a highly efficient basis for sampled bandlimited functions; by modulating and merging DPSS bases, we obtain a dictionary that offers high-quality sparse approximations for most sampled multiband signals. This multiband modulated DPSS dictionary can be readily incorporated into the CS framework. We provide theoretical guarantees and practical insight into the use of this dictionary for recovery of sampled multiband signals from compressive measurements

Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown

A Cognitive Radio Compressive Sensing Framework

With the proliferation of wireless devices and services, allied with further significant predicted growth, there is an ever increasing demand for higher transmission rates. This is especially challenging given the limited availability of radio spectrum, and is further exacerbated by a rigid licensing regulatory regime. Spectrum however, is largely underutilized and this has prompted regulators to promote the concept of opportunistic spectrum access. This allows unlicensed secondary users to use bands which are licensed to primary users, but are currently unoccupied, so leading to more efficient spectrum utilization. A potentially attractive solution to this spectrum underutilisation problem is cognitive radio (CR) technology, which enables the identification and usage of vacant bands by continuously sensing the radio environment, though CR enforces stringent timing requirements and high sampling rates. Compressive sensing (CS) has emerged as a novel sampling paradigm, which provides the theoretical basis to resolve some of these issues, especially for signals exhibiting sparsity in some domain. For CR-related signals however, existing CS architectures such as the random demodulator and compressive multiplexer have limitations in regard to the signal types used, spectrum estimation methods applied, spectral band classification and a dependence on Fourier domain based sparsity. This thesis presents a new generic CS framework which addresses these issues by specifically embracing three original scientific contributions: i) seamless embedding of the concept of precolouring into existing CS architectures to enhance signal sparsity for CR-related digital modulation schemes; ii) integration of the multitaper spectral estimator to improve sparsity in CR narrowband modulation schemes; and iii) exploiting sparsity in an alternative, non-Fourier (Walsh-Hadamard) domain to expand the applicable CR-related modulation schemes. Critical analysis reveals the new CS framework provides a consistently superior and robust solution for the recovery of an extensive set of currently employed CR-type signals encountered in wireless communication standards. Significantly, the generic and portable nature of the framework affords the opportunity for further extensions into other CS architectures and sparsity domains

Wideband Spectrum Acquisition for UAV Swarm Using the Sparse Coding Fourier Transform

As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications. In this letter, the challenge of wideband spectrum acquisition for the UAV swarms is studied by proposing a processing method that features lower power consumption, higher compression rates, and a lower signal-to-noise ratio. Our system is equipped with multiple UAVs, each with a different sub-sampling rate. That allows for frequency backetization and estimation based on sparse Fourier transform theory. Unlike other techniques, the collisions and iterations caused by non-sparsity environ-ments are considered. We introduce sparse coding Fourier transform to address these issues. The key is to code the entire spectrum and decode it through spectrum correlation in the code. Simulation results show that our proposed method performs well in acquiring both narrowband and wideband signals simultaneously, compared to the other methods