Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms.

Wideband spectrum sensing is a challenging problem in the framework of cognitive radio and spectrum surveillance, mainly because of the high sampling rates required by standard approaches. In this paper, a compressed sensing approach was considered to solve this problem, relying on a sub-Nyquist or Xsampling scheme, known as a modulated wideband converter. First, the data reduction at its output is performed in order to enable a highly effective processing scheme for spectrum reconstruction. The impact of this data transformation on the behavior of the most popular sparse reconstruction algorithms is then analyzed. A new mathematical approach is proposed to demonstrate that greedy reconstruction algorithms, such as Orthogonal Matching Pursuit, are invariant with respect to the proposed data reduction. Relying on the same formalism, a data reduction invariant version of the LASSO (least absolute shrinkage and selection operator) reconstruction algorithm was also introduced. It is finally demonstrated that the proposed algorithm provides good reconstruction results in a wideband spectrum sensing scenario, using both synthetic and measured data.

Widely-Linear Digital Self-Interference Cancellation in Full-Duplex USRP Transceiver.

Full-duplex (FD) communication systems allow for increased spectral efficiency but require effective self-interference cancellation (SIC) techniques to enable the proper reception of the signal of interest. The underlying idea of digital SIC is to estimate the self-interference (SI) channel based on the received signal and the known transmitted waveform. This is a challenging task since the SI channel involves, especially for mass-market FD transceivers, many nonlinear distortions produced by the impairments of the analog components from the receiving and transmitting chains. Hence, this paper first analyzes the power of the SI components under practical conditions and focuses on the most significant one, which is proven to be produced by the I/Q mixer imbalance. Then, a widely-linear digital SIC approach is adopted, which simultaneously deals with the direct SI and its image component caused by the I/Q imbalance. Finally, the performances achieved by linear and widely-linear SIC approaches are evaluated and compared using an experimental FD platform relying on software-defined radio technology and GNU Radio. Moreover, the considered experimental framework allows us to set different image rejection ratios for the transmission path I/Q mixer and to study its influence on the SIC capability of the discussed approaches.

A Modulated Wideband Converter Model Based on Linear Algebra and Its Application to Fast Calibration.

In the context of cognitive radio, smart cities and Internet-of-Things, the need for advanced radio spectrum monitoring becomes crucial. However, surveillance of a wide frequency band without using extremely expensive high sampling rate devices is a challenging task. The recent development of compressed sampling approaches offers a promising solution to these problems. In this context, the Modulated Wideband Converter (MWC), a blind sub-Nyquist sampling system, is probably the most realistic approach and was successfully validated in real-world conditions. The MWC can be realized with existing analog components, and there exist calibration methods that are able to integrate the imperfections of the mixers, filters and ADCs, hence allowing its use in the real world. The MWC underlying model is based on signal processing concepts such as filtering, modulation, Fourier series decomposition, oversampling and undersampling, spectrum aliasing, and so on, as well as in-flow data processing. In this paper, we develop an MWC model that is entirely based on linear algebra, matrix theory and block processing. We show that this approach has many interests: straightforward translation of mathematical equations into simple and efficient software programming, suppression of some constraints of the initial model, and providing a basis for the development of an extremely fast system calibration method. With a typical MWC acquisition device, we obtained a speed-up of the calibration computation time by a factor greater than 20 compared with a previous implementation.

A Novel Scheme for Discrete and Secure LoRa Communications.

In this paper, we present a new LoRa transceiver scheme to ensure discrete communications secure from potential eavesdroppers by leveraging a simple and elegant spread spectrum philosophy. The scheme modifies both preamble and payload waveforms by adapting a current state-of-the-art LoRa synchronization front-end. This scheme can also be seen as a self-jamming approach. Furthermore, we introduce a new payload demodulation method that avoids the adverse effects of the traditional cross-correlation solution that would otherwise be used. Our simulation results show that the self-jamming scheme exhibits very good symbol error rate (SER) performance with a loss of just 0.5 dB for a frequency spread factor of up to 10.

Drone Detection and Classification Using Physical-Layer Protocol Statistical Fingerprint.

We propose a novel approach for drone detection and classification based on RF communication link analysis. Our approach analyses large signal record including several packets and can be decomposed of two successive steps: signal detection and drone classification. On one hand, the signal detection step is based on Power Spectral Entropy (PSE), a measure of the energy distribution uniformity in the frequency domain. It consists of detecting a structured signal such as a communication signal with a lower PSE than a noise one. On the other hand, the classification step is based on a so-called physical-layer protocol statistical fingerprint (PLSPF). This method extracts the packets at the physical layer using hysteresis thresholding, then computes statistical features for classification based on extracted packets. It consists of performing traffic analysis of communication link between the drone and its controller. Conversely to classic drone traffic analysis working at data link layer (or at upper layers), it performs traffic analysis directly from the corresponding I/Q signal, i.e., at the physical layer. The approach shows interesting properties such as scale invariance, frequency invariance, and noise robustness. Furthermore, the classification method allows us to distinguish WiFi drones from other WiFi devices due to underlying requirement of drone communications such as good reactivity in control. Finally, we propose different experiments to highlight theses properties and performances. The physical-layer protocol statistical fingerprint exploiting communication specificities could also be used in addition of RF fingerprinting method to perform authentication of devices at the physical-layer.

Hiding Messages in Secure Connection Transmissions with Full-Duplex Overt Receiver.

This paper considers hiding messages in overt transmissions with a full-duplex receiver, which emits artificial noise to secure its transmission connection while a transmitter opportunistically sends a covert message to a covert user. The warden's uncertainties in decoding the overt message and artificial-noise-received power are exploited to hide messages. Then, the covert throughput accompanied with the warden's average detection error probability are determined. The results show that increasing the transmit power of artificial noise or improving secure connection at the overt user will improve the covert performance. The results also show that the covert performance is improved when the self-interference cancellation is improved at the full-duplex receiver or when the warden is located close to the full-duplex receiver, indicating the positive impact of the overt performance on the covert performance.

Secrecy Coding Analysis of Short-Packet Full-Duplex Transmissions with Joint Iterative Channel Estimation and Decoding Processes.

This paper studies the secrecy coding analysis achieved by the self-jamming technique in the presence of an eavesdropper by considering a short-packet Full-Duplex (FD) transmission developed based on iterative blind or semi-blind channel estimation and advanced decoding algorithms. Indeed, the legitimate receiver and eavesdropper can simultaneously receive the intended signal from the transmitter and broadcast a self-jamming or jamming signal to the others. Unlike other conventional techniques without feedback, the blind or semi-blind algorithm applied at the legitimate receiver can simultaneously estimate, firstly, the Self-Interference (SI) channel to cancel the SI component and, secondly, estimate the propagation channel, then decode the intended messages by using 5G Quasi-Cyclic Low-Density Parity Check (QC-LDPC) codes. Taking into account the passive eavesdropper case, the blind channel estimation with a feedback scheme is applied, where the temporary estimation of the intended channel and the decoded message are fed back to improve both the channel estimation and the decoding processes. Only the blind algorithm needs to be implemented in the case of a passive eavesdropper because it achieves sufficient performances and does not require adding pilot symbols as the semi-blind algorithm. In the case of an active eavesdropper, based on its robustness in the low region of the Signal-to-Noise Ratio (SNR), the semi-blind algorithm is considered by trading four pilot symbols and only requiring the feedback for channel estimation processes in order to overcome the increase in noise in the legitimate receiver. The results show that the blind or semi-blind algorithms outperform the conventional algorithm in terms of Mean Square Error (MSE), Bit Error Rate (BER) and security gap (Sg). In addition, it has been shown that the blind or semi-blind algorithms are less sensitive to high SI and self-jamming interference power levels imposed by secured FD transmission than the conventional algorithms without feedback.

RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context.

In IoT networks, authentication of nodes is primordial and RF fingerprinting is one of the candidates as a non-cryptographic method. RF fingerprinting is a physical-layer security method consisting of authenticated wireless devices using their components' impairments. In this paper, we propose the RF eigenfingerprints method, inspired by face recognition works called eigenfaces. Our method automatically learns important features using singular value decomposition (SVD), selects important ones using Ljung-Box test, and performs authentication based on a statistical model. We also propose simulation, real-world experiment, and FPGA implementation to highlight the performance of the method. Particularly, we propose a novel RF fingerprinting impairments model for simulation. The end of the paper is dedicated to a discussion about good properties of RF fingerprinting in IoT context, giving our method as an example. Indeed, RF eigenfingerprint has interesting properties such as good scalability, low complexity, and high explainability, making it a good candidate for implementation in IoT context.

Joint Semi-Blind Self-Interference Cancellation and Equalisation Processes in 5G QC-LDPC-Encoded Short-Packet Full-Duplex Transmissions.

The paper proposes a joint semi-blind algorithm for simultaneously cancelling the self-interference component and estimating the propagation channel in 5G Quasi-Cyclic Low-Density Parity-Check (QC-LDPC)-encoded short-packet Full-Duplex (FD) transmissions. To avoid the effect of channel estimation processes when using short-packet transmissions, this semi-blind algorithm was developed by taking into account only a small number (four at least) pilot symbols, which was integrated with the intended information sequence and used for the feedback loop of the estimation of the channels. The results showed that this semi-blind algorithm not only achieved nearly optimal performance, but also significantly reduced the processing time and computational complexity. This semi-blind algorithm can also improve the performances of the Mean-Squared Error (MSE) and Bit Error Rate (BER). The results of this study highlight the potential efficiency of this joint semi-blind iterative algorithm for 5G and Beyond and/or practical IoT transmission scenarios.

Stage- and sex-specific heat tolerance in the yellow dung fly Scathophaga stercoraria.

Thermal tolerance varies at all hierarchical levels of biological organization: among species, populations, individuals, and even within individuals. Age- or developmental stage- and sex-specific thermal effects have received relatively little attention in the literature, despite being crucial for understanding thermal adaptation in nature and responses to global warming. We document stage- and sex- specific heat tolerance in the yellow dung fly Scathophaga stercoraria (Diptera: Scathophagidae), a species common throughout the northern hemisphere that generally favours cool climates. Exposure of eggs to temperatures up to 32°C did not affect larval hatching rate, but subsequent egg-to-adult survival at a benign temperature was reduced. Permanent transfer from benign (18°C) to hot temperatures (up to 31°C) at different larval and pupal stages strongly decreased egg-to-adult survival, though survival continuously improved the later the transfer occurred. Temporary transfer for only two days increased mortality more weakly, survival being lowest when temperature stress was imposed early during the larval or pupal stages. Adult flies provided with sugar and water tolerated 31°C longer than previously thought (5 days in males to 9 days in females). Eggs were thus less susceptible to thermal stress than larvae, pupae or adults, in agreement with the hypothesis that more mobile stages require less physiological protection against heat because they can behaviourally thermoregulate. The probability of mating, of laying a clutch, and hatching success were generally independently reduced by exposure of females or males to warm temperatures (24°C) during the juvenile or adult stages, with some interactions evident. High temperature stress thus affects survival differentially depending on when it occurs during the juvenile or the pre-reproductive adult life stage, and affects reproductive success via the mating behaviour of both sexes, female physiology in terms of oviposition, and fertility via sperm and/or egg quality. Our results illustrate that temperature stress, even when moderate and temporary, during early development can have profound lethal and non-lethal fitness-consequences later in life.