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It is demonstrated that the deployment cost profiling varies for the various AI architecture possibilities, and that this cost should be taken into account when deploying and choosing the AI/ML solution. I The article by Narengerile et al. i proposes a new beam training approach via deep reinforcement learning to take use of machine learning technologies that will be used to build sixth-generation (6G) communication networks. [Extracted from the article]

The single-phase grounding fault of power systems is influenced by a variety of factors, resulting from the developing sizes and increasing complexity of power systems. In order to take advantage of big data in power systems, we propose a revised method with the use of synchronized phasor measurement. The data-driven method is designed to detect and localize the single-phase grounding fault, which reveals the correlation between eigenvalues and status of power systems. First, it calculates the contribution of the fault to each node; it then combines with a split window to monitor power systems in real time and to detect fault more efficiently. Based on the correlation between the elements of the matrix, it is robust against bad data and highly sensitive to weak signals. In general, the proposed method is applicable to various faults and well-functioning with real-time analysis. We test the proposed method with case studies from a distribution network with 80 nodes. [ABSTRACT FROM AUTHOR]

Cyber-physical systems (CPSs) are characterized by integrating computation, communication, and physical system. In typical CPS application scenarios, vehicle-to-vehicle (V2V) and Industry Internet of Things (IIoT), due to doubly selective fading and non-stationary channel characteristics, the robust and reliable end-to-end communication is extremely important. Channel estimation is a major signal processing technology to ensure robust and reliable communication. However, the existing channel estimation methods for V2V and IIoT cannot effectively reduce intercarrier interference (ICI) and lower the computation complexity, thus leading to poor robustness. Aiming at this challenge, according to the channel characteristics of V2V and IIoT, we design two channel estimation methods based on the Bayesian filter to promote the robustness and reliability of end-to-end communication. For the channels with doubly selective fading and non-stationary characteristics of V2V and IIoT scenarios, in the one hand, basis extended model (BEM) is used to further reduce the complexity of the channel estimation algorithm under the premise that ICI can be eliminated in the channel estimation. On the other hand, aiming at the non-stationary channel, a channel estimation and interpolation method based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) Bayesian filters to jointly estimate the channel impulse response (CIR) and time-varying time domain autocorrelation coefficient is adopted. Through the MATLAB simulation, the robustness and reliability of end-to-end communication for V2V and IIoT are promoted by the proposed algorithms. [ABSTRACT FROM AUTHOR]