Hierarchical beam alignment in SU-MIMO terahertz communications

Single-Carrier Frequency Division Multiple Access (SC-FDMA) is a promising technique for high data rate indoor Terahertz (THz) communications in future beyond 5G systems. In an indoor propagation scenario, the Line-Of-Sight (LOS) component may be blocked by the obstacles. Thus, efficient THz SC-FDMA communications require a fast and reliable Beam Alignment (BA) method for both LOS and Non-Line-Of-Sight (NLOS) scenarios. In this paper, we first adopt the hierarchical discrete Fourier transform codebook for LOS BA, and introduce the hierarchical k-means codebook for NLOS BA to improve the beamforming gain. Simulation results illustrate that the hierarchical DFT codebook and the hierarchical k-means codebook can achieve the beamforming gain close to that of the maximum ratio transmission in LOS and NLOS cases, respectively. Based on these two codebooks, we propose a Multi-Armed Bandit (MAB) algorithm named Hierarchical Beam Alignment (HBA) for single-user SC-FDMA THz systems to reduce the BA latency. HBA utilizes a hierarchical structure in the adopted codebook and prior knowledge regarding the noise power to speed up the BA process. Both theoretical analysis and simulation results indicate that the proposed BA method converges to the optimal beam with high probability for both the hierarchical DFT codebook and the hierarchical k-means codebook in the LOS and NLOS scenarios, respectively. The latency introduced by HBA is significantly lower when compared to an exhaustive search method and other MAB-based methods.

Node Importance Evaluation of Cyber-Physical System under Cyber-Attacks Spreading

The study of cyber-attacks, and in particular the spread of attack on the power cyber-physical system, has recently attracted considerable attention. Identifying and evaluating the important nodes under the cyber-attack propagation scenario are of great significance for improving the reliability and survivability of the power system. In this paper, we improve the closeness centrality algorithm and propose a compound centrality algorithm based on adaptive coefficient to evaluate the importance of single-layer network nodes. Moreover, we quantitatively calculated the decouple degree of cascading failures caused by exposed nodes formed by attack propagation. At last, experiments based on the IEEE 57 test system show that the proposed compound centrality algorithm can match the cyber-attack propagation scenario well, and we give the importance values of the nodes in a specific attack scenario.

Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning

A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Response estimation from the real signal received by each sensor. Conventional localization techniques mitigate errors trying to avoid NLOS measurements in processing emitter position, while the proposed method uses the multipath fingerprint information produced by ray tracing (RT) simulation together with calibration emitters to refine a Machine Learning engine, which gives an extra layer of information to improve the emitter position estimation. The ray-tracing fingerprints perform the target localization embedding all the reflection and diffraction in the propagation scenario. A validation campaign was performed and showed the feasibility of the proposed method, provided that the buildings can be appropriately included in the scenario description.

Analysis of Twitter-based Malware Propagation using SIR Epidemic Model

This paper presented an analysis of malware propagation scenario in which attacker exploit news spreading dynamics on Twitter. The malware propagation starts with an attacker crafting tweets about breaking news, event or tragedy that will lure user to click the short-URL provided in the tweet which then redirect user to malicious website, which in turn installed the malware in the user mobile device or computer. As the information spread in the Twitter then more users will be infected with malware. The underlying principle to analyze this type of malware propagation is that the spreading of information in Twitter can be modeled by using formal epidemic model of disease. The simulation result of the model shows parameters that highly impacting the spread of malware using Twitter as the medium.