This paper investigates the physical layer security of a wireless network assisted by a Reconfigurable Intelligent Surface ((RIS)) in the presence of full-duplex active eavesdropping. In this scenario, the (RIS) cooperates with the Base Station (BS) to transfer information to the intended user while an active attacker attempts to intercept the information through a wiretap channel. In addition, the attacker sends jamming signals to obstruct with the legitimate user’s signal reception and increase the eavesdropping rate. Our objective is to maximize the secrecy rate by jointly optimizing the active and passive beamformers at the BS and (RIS), respectively. To solve the resulting non-convex optimization problem, we propose a solution that decomposes it into two disjoint beamforming design sub-problems solved iteratively using Alternating Optimization (AO) techniques. Numerical analysis is conducted to evaluate the effects of varying the number of active attacking antennas and elements of the (RIS) on the secrecy performance of the considered systems under the presence of jamming signals sent by the attacker. The results demonstrate the importance of considering the impact of jamming signals on physical layer security in (RIS)-aided wireless networks. Overall, our work contributes to the growing body of literature on (RIS)-aided wireless networks and highlights the need to address the effects of jamming and active eavesdropping signals in such systems.

This paper investigates beamforming design for Simultaneous transmission and reflection Reconfigurable Intelligent Surface (Star (RIS)) assisted secure wireless communication with three operating protocols (energy splitting (ES), mode switching (MS), and time switching (TS)) with the assumption of the availability of imperfect CSI of two eavesdroppers at BS. We maximize the sum rate of legitimate users by designing both active and passive beamforming at BS and (RIS), respectively, under the constraint of the limited maximum tolerable rate of eavesdroppers and a limited transmission power budget. The non-convex problem is solved by alternating optimization (AO) for active and passive beamforming optimization, and also successive convex approximation (SCA), and the penalty concave-convex procedure (PCCP). According to the simulation results, Star-(RIS) outperforms conventional (RIS), ES mode outperforms MS and TS, and the performance is higher with lower channel estimation error.

This paper introduces a new method for improving wireless communication systems by employing beyond diagonal Reconfigurable Intelligent Surfaces (BD-(RIS)) and unmanned aerial vehicle (UAV) alongside deep reinforcement learning (DRL) techniques. BD-(RIS) represents a departure from traditional (RIS) designs, providing advanced capabilities for manipulating electromagnetic waves to optimize the performance of communication. We propose a DRL-based framework for optimizing the UAV and configuration of BD-(RIS) elements, including hybrid beamforming, phase shift adjustments, and transmit power coefficients for non-orthogonal multiple access (NOMA) transmission by considering max-min fairness. Through extensive simulations and performance evaluations, we demonstrate that BD-(RIS) outperforms conventional (RIS) architectures. Additionally, we analyze the convergence speed and performance trade-offs of different DRL algorithms, emphasizing the importance of selecting the appropriate algorithm and hyper-parameters for specific applications. Our findings underscore the transformative potential of BD-(RIS) and DRL in enhancing wireless communication systems, laying the groundwork for next-generation network optimization and deployment.

With the advent of the next generation of wireless communication networks, the requirements for high-rate and timely information delivery, especially in real-time applications, have increased dramatically. In particular, Millimeter wave (mmWave) communications can provide a high data rate, and Reconfigurable Intelligent Surfaces ((RIS)s) can reduce the blockage sensitivity of mmWave links. Also, to quantify the data freshness, the age of information (AoI) metric is introduced in the recent literature, defined as the time elapsed since the last successfully received status update was generated. This paper aims to optimize the average AoI in a multi-user (RIS)-assisted millimeter wave network. For this purpose, the proposed problem was formulated as a Markov decision process (MDP) that aims to obtain the optimal control policy that minimizes the sum of the average AoI of all users. Under the unknown system statistics assumption, we propose a model-free deep reinforcement learning algorithm to schedule the users’ transmission, adjust the transmit power, and configure the (RIS) to reflect the signals.  The performance of the proposed method is evaluated in terms of convergence characte(RIS)tics and the impact of changes in network parameters.

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Many of the video processing algorithms cannot be implemented in real time on general computers, due to their computational complexity. For an efficient implementation, a custom hardware that can be reconfigured for the algorithm, is used. In this paper a new acceleration hardware based on FPGA elements is proposed. This hardware can be adapted with the processing algorithm through FPGA design reconfiguration. Using a PCI slot, this hardware communicates with a Pc. The FPGAs are programmed through the PCI slot. The video frames are supplied to this hardware for processing. The performance of this hardware is evaluated using warping algorithms. The first and second order warping for a 512*512 frame can be done in 7.9 ms.

One of the features of new theories of development is that it considers dynamism at the regional level, innovation, production and transfer of knowledge as a key factor in regional development. Accordingly, innovation at the regional level is mainly done through collective learning and synergy between different actors who work inside and outside the organization. The central goal of this article is to identify the factors affecting the regional innovation system. In order to investigate the impact of different variables on the rate of regional innovation, the panel model related to the variables of one hundred and fifty countries during the years 2017-2013 has been used. Then, according to the hypotheses and diagnostic tests using panel data and the fixed effects pattern method, the relationship between innovation and the factors affecting it is examined. The results confirm that the variables of R&D expenditures, industry and university participation in R&D, access to the latest technology and finally higher education and patent programs have a positive and significant effect on countries' innovation.