In order to enhance the reliability of digital transmissions, error correcting codes are used in every digital communication system. To meet the new constraints of data rate or reliability, new coding schemes are currently being developed. Therefore, digital communication systems are in perpetual evolution and it is becoming very difficult to remain compatible with all standards used. A cognitive radio system seems to provide an interesting solution to this problem: the conception of an intelligent receiver able to adapt itself to a specific transmission context. This article presents a new algorithm dedicated to the blind recognition of convolutional encoders in the general k/n rate case. After a brief recall of convolutional code and dual code properties, a new iterative method dedicated to the blind estimation of convolutional encoders in a noisy context is developed. Finally, case studies are presented to illustrate the performances of our blind identification method.

The use of channel code is mandatory in current digital communication systems. It allows to access the information on the receiver side despite the presence of noise. In this paper, we are interested in the blind identification of the parameters of an error correcting code from a received noisy data stream. Literature provides a large amount of contributions for this problem in the hard-decision case but few in the soft-decision case. It is well known that soft-decision methods allow significant gain in decoding techniques. Thence, we propose an algorithm which is able to identify the length of a code through a classification process from the bits likelihood values. It highlights a difference of behavior between an i.i.d. sequence and an encoded one. This method does not rely on any a priori knowledge about the encoder involved. Indeed, the distribution of $n$-length code words in an $n$-dimensional space depends on the encoder characteristics. Some areas of this $n$-dimensional space are left vacant because of the redundancy added by the encoder. Despite the presence of noise, it is still possible to detect this phenomenon. Furthermore, an adaptation of a collisions method based on the birthday paradox gives us access to an estimation of the code dimension. Finally, we investigate the performance of this estimation methods to show its efficiency.

This article presents a semianalytical approximation of symbol error rate (SER) for the well known long-range (LoRa) Internet of Things (IoT) modulation scheme in the following two scenarios: 1) in multipath frequency selective fading channel with additive white Gaussian noise (AWGN) and 2) in the presence of a second interfering LoRa user in the flat-fading AWGN channel. Performances for both coherent and noncoherent cases are derived by considering the common discrete Fourier transform (DFT)-based detector on the received LoRa waveform. By considering these two scenarios, the detector exhibits parasitic peaks that severely degrade the performance of the LoRa receiver. We propose in that sense a theoretical expression for this result, from which a unified framework based on peak detection probabilities allows us to derive SER, which is validated by Monte Carlo simulations. Fast computation of the derived closed-form SER allows to carry out deep performance analysis for these two scenarios.

In cognitive radio context, the parameters of coding schemes are unknown at the receiver. The design of an intelligent receiver is then essential to blindly identify these parameters from the received data. The blind identification of code word length has already been extensively studied in the case of binary error-correcting codes. Here, we are interested in non-binary codes where a noisy transmission environment is considered. To deal with the blind identification problem of code word length, we propose a technique based on the Gauss-Jordan elimination in GF(q) (Galois field), with q=2 ^m , where m is the number of bits per symbol. This proposed technique is based on the information provided by the arithmetic mean of the number of zeros in each column of these matrices. The robustness of our technique is studied for different code parameters and over different Galois fields.

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.

This article presents a novel long-range (LoRa) receiver operating in the frequency selective multipath channel (MPC). The dechirped received LoRa waveforms under MPC allows us to derive a simple and efficient LoRa receiver scheme by using a matched filter (MF) approach that aims to maximize the signal-to-noise ratio (SNR) at the symbol index frequency of the discrete Fourier transform (DFT) output. We show that the MF receiver can be seen as a RAKE structure where interference peaks related to multipath, exhibited at DFT output, are recombined in a constructive way. Detection performance is driven by channel energy and the benefit of this novel MF/RAKE receiver over original coherent and noncoherent receiver appears only for MPC that exhibits significant paths energy. These two MF and RAKE receivers have however different implementation complexities that are studied in detail. We provide in that sense recommendations on which receiver variant to use for real operations, depending on complexity constraints. Finally, the proposed MF/RAKE receiver outperforms previous results on time-delay estimation LoRa (TDEL) receiver over MPC, especially at low SNR and higher LoRa spreading factor (SF) parameter, at the cost of higher but reasonable complexity.