Can Artificial Noise Boost Further the Secrecy of Dual-hop RIS-aided Networks?

In this paper, we quantify the physical layer security of a dual-hop regenerative relaying-based wireless communication system assisted by reconfigurable intelligent surfaces (RISs). In particular, the setup consists of a source node communicating with a destination node via a regenerative relay. In this setup, a RIS is installed in each hop to increase the source-relay and relay-destination communications reliability, where the RISs' phase shifts are subject to quantization errors. The legitimate transmission is performed under the presence of a malicious eavesdropper attempting to compromise the legitimate transmissions by overhearing the broadcasted signal from the relay. To overcome this problem, we incorporate a jammer to increase the system's secrecy by disrupting the eavesdropper through a broadcasted jamming signal. Leveraging the well-adopted Gamma and Exponential distributions approximations, the system's secrecy level is quantified by deriving approximate and asymptotic expressions of the secrecy intercept probability (IP) metric in terms of the main network parameters. The results show that the secrecy is enhanced significantly by increasing the jamming power and/or the number of reflective elements (REs). In particular, an IP of approximately $10^{-4}$ can be reached with $40$ REs and $10$ dB of jamming power-to-noise ratio even when the legitimate links' average signal-to-noise ratios are $10$-dB less than the eavesdropper's one. We show that cooperative jamming is very helpful in strong eavesdropping scenarios with a fixed number of REs, and the number of quantization bits does not influence the secrecy when exceeding $3$ bits. All the analytical results are endorsed by Monte Carlo simulations

Continual Conscious Active Fine-Tuning to Robustify Online Machine Learning Models Against Data Distribution Shifts

Unlike their offline traditional counterpart, online machine learning models are capable of handling data distribution shifts while serving at the test time. However, they have limitations in addressing this phenomenon. They are either expensive or unreliable. We propose augmenting an online learning approach called test-time adaptation with a continual conscious active fine-tuning layer to develop an enhanced variation that can handle drastic data distribution shifts reliably and cost-effectively. The proposed augmentation incorporates the following aspects: a continual aspect to confront the ever-ending data distribution shifts, a conscious aspect to imply that fine-tuning is a distribution-shift-aware process that occurs at the appropriate time to address the recently detected data distribution shifts, and an active aspect to indicate employing human-machine collaboration for the relabeling to be cost-effective and practical for diverse applications. Our empirical results show that the enhanced test-time adaptation variation outperforms the traditional variation by a factor of two

Comparaison des décodeurs de Chase, l'OSD et ceux basés sur les algorithmes génétiques

Les décodeurs basés sur les algorithmes génétiques (AG) appliqués aux codes BCH ont de bonnes performances par rapport à Chase-2 et l'OSD d'ordre 1 et atteignent les performances de l'OSD-3 pour quelques codes Résidu Quadratiques (RQ). Ces algorithmes restent moins complexes pour les codes linéaires de grandes longueurs; en plus leurs performances peuvent être améliorées en changeant les paramètres, en particulier le nombre d'individus par population et le nombre de générations, ce qui les rend attractifs