Detection of OFDM modulations based on the characterization in the phase diagram domain

Signal modulation identification is of high interest for applications in military communications, but is not limited only to this specific field. Some possible applications are related to spectrum surveillance, electronic warfare, quality services, and cognitive radio. Distinguishing between multi-carrier signals, such as orthogonal frequency division multiplexing (OFDM) signals, and single-carrier signals is very important in several applications. Conventional methods face a stalemate in which the classification accuracy process is limited, and, therefore, new descriptors are needed to complement the existing methods. Another drawback is that some features cannot be extracted using conventional feature extraction techniques in practical OFDM systems. This paper introduces a new signal detection algorithm based on the phase diagram characterization. First, the proposed algorithm is described and implemented for simulated signals in MATLAB. Second, the algorithm performance is verified in an experimental scenario by using long-term evolution OFDM signals over a software-defined radio (SDR) frequency testbed. Our findings suggest that the algorithm provides good detection performance in realistic noisy environments

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

International audienceThis paper studies the secrecy coding analysis achieved by the self-jamming technique in thepresence of an eavesdropper by considering a short-packet Full-Duplex (FD) transmission developedbased 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 thetransmitter and broadcast a self-jamming or jamming signal to the others. Unlike other conventionaltechniques without feedback, the blind or semi-blind algorithm applied at the legitimate receiver cansimultaneously 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 eavesdroppercase, the blind channel estimation with a feedback scheme is applied, where the temporary estimationof the intended channel and the decoded message are fed back to improve both the channel estimationand the decoding processes. Only the blind algorithm needs to be implemented in the case of apassive eavesdropper because it achieves sufficient performances and does not require adding pilotsymbols as the semi-blind algorithm. In the case of an active eavesdropper, based on its robustnessin the low region of the Signal-to-Noise Ratio (SNR), the semi-blind algorithm is considered bytrading four pilot symbols and only requiring the feedback for channel estimation processes inorder to overcome the increase in noise in the legitimate receiver. The results show that the blindor 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 blindor semi-blind algorithms are less sensitive to high SI and self-jamming interference power levelsimposed by secured FD transmission than the conventional algorithms without feedback

Comparison of Adaptive Filtering Strategies for Self-Interference Cancellation in LTE Communication Systems

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UWB Sensing for UAV and Human Comparative Movement Characterization

Nowadays, unmanned aerial vehicles/drones are involved in a continuously growing number of security incidents. Therefore, the research interest in drone versus human movement detection and characterization is justified by the fact that such devices represent a potential threat for indoor/office intrusion, while normally, a human presence is allowed after passing several security points. Our paper comparatively characterizes the movement of a drone and a human in an indoor environment. The movement map was obtained using advanced signal processing methods such as wavelet transform and the phase diagram concept, and applied to the signal acquired from UWB sensors

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

International audienceFull-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

Detection of OFDM modulations based on the characterization in the phase diagram domain

International audienceSignal modulation identification is of high interest for applications in military communications, but is not limited only to this specific field. Some possible applications are related to spectrum surveillance, electronic warfare, quality services, and cognitive radio. Distinguishing between multi-carrier signals, such as orthogonal frequency division multiplexing (OFDM) signals, and single-carrier signals is very important in several applications. Conventional methods face a stalemate in which the classification accuracy process is limited, and, therefore, new descriptors are needed to complement the existing methods. Another drawback is that some features cannot be extracted using conventional feature extraction techniques in practical OFDM systems. This paper introduces a new signal detection algorithm based on the phase diagram characterization. First, the proposed algorithm is described and implemented for simulated signals in MATLAB. Second, the algorithm performance is verified in an experimental scenario by using long-term evolution OFDM signals over a software-defined radio (SDR) frequency testbed. Our findings suggest that the algorithm provides good detection performance in realistic noisy environments

USRP Experimental Approach for Digital Self-Interference Cancellation in Full-Duplex Communications

International audienceIn-band full-duplex (FD) operation mode represents a key solution in achieving the performance required by the next-generation communications system, especially in terms of spectral efficiency. Therefore, the research interest in the development of enhanced self-interference cancellation (SIC) techniques has grown exponentially. In this paper, we propose an experimental approach relying on software-defined radio (SDR) technology and GNU Radio, which aims at evaluating the performance of the newly proposed SIC solutions under realistic conditions. Additionally, the interference cancellation capacity of a digital SIC method based on recursive adaptive filtering is tested in a point-to-point configuration

New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB Sensing

International audienceIn the last years, the commercial drone/unmanned aerial vehicles market has grown due to their technological performances (provided by the multiple onboard available sensors), low price, and ease of use. Being very attractive for an increasing number of applications, their presence represents a major issue for public or classified areas with a special status, because of the rising number of incidents. Our paper proposes a new approach for the drone movement detection and characterization based on the ultra-wide band (UWB) sensing system and advanced signal processing methods. This approach characterizes the movement of the drone using classical methods such as correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot analysis. The obtained results are compared in terms of movement map accuracy and required computation time in order to offer a future starting point for the drone intrusion detection