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.

Automatic modulation classification (AMC) is an important process for future communication systems with prominent applications from spectrum management, and secure communication, to cognitive radio. The requirement for an efficient AMC classifier is due to its capability in blind modulation recognition, which is a difficult task in real scenarios where the limitations of traditional hardware and the complexity of channel impairments are involved. Therefore, this paper proposes a complete real-time AMC system based on software-defined radio and deep learning architecture. The system demodulation performance is verified through simulations and real channel impairment conditions to ensure reliability. With at most 6 times reduced number of parameters, two proposed models convolutional long short-term memory deep neural network and residual long short-term memory neural network also show a general improvement in classification accuracy compared with reference studies. The performance of these models at real-time AMC is tested with suitable processing time for practical applications.

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.

Compressed Sensing is considered as a practical signal processing technique that can recover a sparse signal sampled by a rate much lower than the Nyquist-Shannon rate. To successfully recover that signal, Compressed Sensing goes through three main stages: signal representation, measurement matrix formation to signal compression, and signal reconstruction. The two conditions that need to be ensured are the sparsity of signals and the restricted isometry property of the measurement matrix. The measurement matrix plays an important role in the successful reconstruction. Indeed, the design of a good measurement matrix will improve the reconstruction rate. This paper proposes a method for constructing the measurement matrix by using Coupled Quadratic Congruential Random Number Generator. By comparing the inequality of two quadratic congruential random number sequences, the random bits sequence +1 and -1 can be generated. The proposed method has advantages in efficient/easy hardware implementation. Then, the measurement matrix formed by this method will be proved to satisfy the Compressed Sensing reconstruction conditions. The simulation results also show the recovery rate of the formed measurement matrix based on the sparsity levels. At last, an implementation to a sub-Nyquist sampling scheme will be presented with the performance of reconstruction in some practical scenarios.

This paper proposes an image encryption-decryption scheme with two-layer security. In the first layer, an image is encrypted by a switched system, using the concept from control theory. Also, from this switched system, a random key matrix will be generated while encrypting in the first layer. This key matrix is used to diffuse the image pixels from the first layer, providing the second layer of security. Then, in the decryption stage, the random key matrix plays a role as a cryptographic key and the optimization method will be used to decrypt the second layer encrypted image. The flatness property of the switched system that makes the plain image to be recovered from the encrypted image of the first layer. This scheme is to enhance the security in image encryption and also fortifies the encryption process against sophisticated attacks while maintaining computational efficiency. Finally, the effectiveness of the proposed scheme is evaluated by simulation, then assessing it to different types of cryptanalytic analyses such as information entropy analysis, adjacent pixel correlation, differential attack analysis, key sensitivity analysis, and histogram analysis. The numerical values that can achieved by the proposed schemes are entropy 7.996, adjacent pixel correlation for horizontal −0.0012, vertical −0.0039, diagonal 0.0005; and differential attack analysis with Number of Pixels Change Rate 0.9963 and Unified Average Changing Intensity 0.3341.