The blind recognition of communication parameters is a key research issue for commercial and military communication systems. In this paper, we investigate the problem of the blind recognition of linear space-time block codes (STBC). To characterize the space time coding, we propose to compute a time-lag correlation of the received samples. Provided the number of transmitters, the noise variance and the symbol timing are well estimated, we show that the theoretical values of the correlation norm only depend on the STBC and are affected by neither the channel nor the symbol modulation. The automatic recognition of the STBC is realized by selecting the STBC which minimizes the distance between the theoretical values and the experimental ones. Simulations show that our method performs well even for low signal to noise ratio (0 dB).

Blind sequences estimation methods, which work without prior knowledge about the transmitter, have been proposed in M.K. Tsatsanis and G.B. Giannakis (1997), G. Burel and C. Boulder (2001), and G. Burel and C. Boulder (2001); but they are restricted to a single-user context. Here, we extend the approach originally proposed in G. Burel and C. Boulder (2001) to the multi-user context. We have developed a fast and efficient blind synchronization criterion based on the behavior of the FROBENIUS square norm of a submatrix that scans the intercepted signal correlation matrix. By using eigenanalysis techniques and theoretical linear algebra results, we prove that the maxima of this criterion correspond to the synchronization times with transmitters. Then, eigenanalysis techniques allow to estimate the spreading sequences. Eigenanalysis was associated with additional processing modules aimed at solving dephasing problems due to the transmission channel.

In image processing, image segmentation is the process of partitioning a digital image into multiple image segments. Among state‐of‐the‐art methods, Markov random fields can be used to model dependencies between pixels and achieve a segmentation by minimising an associated cost function. Currently, finding the optimal set of segments for a given image modelled as a Markov random fields appears to be NP‐hard. The authors aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of synthetic aperture radar images. For that purpose, the authors propose a hybrid quantum annealing classical optimisation expectation maximisation algorithm to obtain optimal sets of segments. After proposing suitable formulations, the authors discuss the performances and the scalability of authors’ approach on the D‐Wave quantum computer. The authors also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimisation problems.

The Modulated Wideband Converter (MWC) is a blind sub-Nyquist sampling system used especially to monitor wideband spectrum. This system can be realized by existing analog components. From a theoretical point of view, all analog components are assumed ideal. However, this hypothesis is false in practice. Indeed, some imperfections are introduced by analog components such as nonlinearities of mixers, phase/attenuation/selectivity of low-pass filters and desynchronization between modulating waveforms. Consequently, it is necessary to correctly estimate the sensing matrix to ensure correct spectrum reconstruction performance. Conventional calibration methods are based on the measurements of different single tones. However, these approaches need to record several measurements with single-tone inputs. To avoid this problem, this paper presents a new hardware calibration method of MWC by using a single measurement of a white noise signal for radio frequency spectrum monitoring. A preprocessing method is performed to resynchronize input, output and modulating waveforms signals with each other. Our hardware calibration method is applied to our prototype of Compressed Sensing (CS) scheme in order to estimate the corrected sensing matrix. To prove the efficiency of our hardware calibration, reconstruction performances were evaluated with respect to the probability of correct reconstruction and false alarm criteria. These results are compared with those obtained with the theoretical MWC scheme, with our prototype without calibration and with reference calibration.

In this paper, the blind synchronization approach based on the Frobenius square norm behaviour (FSNB) is extended to the case of several users in multirate CDMA transmissions. It consists in estimating the FSNB criterion maxima (synchronization peaks) positions, and it allows one to estimate desynchronization and transmissions delay times. We prove that, the extented-FSNB criterion is a powerful tool for blind synchronization. We also detail the synchronization peaks masking which degrades its performances especially at very low SNRs, and show how to proceed in this case. Simulation results illustrate sequences identification and symbols recovering, after synchronizing using the extended-FSNB.

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.