A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding

In this work, with combined belief propagation (BP), mean field (MF) and expectation propagation (EP), an iterative receiver is designed for joint phase noise (PN) estimation, equalization and decoding in a coded communication system. The presence of the PN results in a nonlinear observation model. Conventionally, the nonlinear model is directly linearized by using the first-order Taylor approximation, e.g., in the state-of-the-art soft-input extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to handle the factor due to the nonlinear model, and a second-order Taylor approximation is used to achieve Gaussian approximation to the MF messages, which is crucial to the low-complexity implementation of the receiver with BP and EP. It turns out that our approximation is more effective than the direct linearization in the soft-in EKS with similar complexity, leading to significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter

Turbo-Equalization Using Partial Gaussian Approximation

This paper deals with turbo-equalization for coded data transmission over intersymbol interference (ISI) channels. We propose a message-passing algorithm that uses the expectation-propagation rule to convert messages passed from the demodulator-decoder to the equalizer and computes messages returned by the equalizer by using a partial Gaussian approximation (PGA). Results from Monte Carlo simulations show that this approach leads to a significant performance improvement compared to state-of-the-art turbo-equalizers and allows for trading performance with complexity. We exploit the specific structure of the ISI channel model to significantly reduce the complexity of the PGA compared to that considered in the initial paper proposing the method.Comment: 5 pages, 2 figures, submitted to IEEE Signal Processing Letters on 8 March, 201

Conceptual Study of a Real-Time Hybrid Simulation Framework for Monopile Offshore Wind Turbines Under Wind and Wave Loads

As an attractive renewable energy source, offshore wind plants are becoming increasingly popular for energy production. However, the performance assessment of offshore wind turbine (OWT) structure is a challenging task due to the combined wind-wave loading and difficulties in reproducing such loading conditions in laboratory. Real-time hybrid simulation (RTHS), combining physical testing and numerical simulation in real-time, offers a new venue to study the structural behavior of OWTs. It overcomes the scaling incompatibilities in OWT scaled model testing by replacing the rotor components with an actuation system, driven by an aerodynamic simulation tool running in real-time. In this study, a RTHS framework for monopile OWTs is proposed. A set of sensitivity analyses is carried out to evaluate the feasibility of this RTHS framework and determine possible tolerances on its design. By simulating different scaling laws and possible error contributors (delays and noises) in the proposed framework, the sensitivity of the OWT responses to these parameters are quantified. An example using a National Renewable Energy Lab (NREL) 5-MW reference OWT system at 1:25 scale is simulated in this study to demonstrate the proposed RTHS framework and sensitivity analyses. Three different scaling laws are considered. The sensitivity results show that the delays in the RTHS framework significantly impact the performance on the response evaluation, higher than the impact of noises. The proposed framework and sensitivity analyses presented in this study provides important information for future implementation and further development of the RTHS technology for similar marine structures

Silent Vulnerable Dependency Alert Prediction with Vulnerability Key Aspect Explanation

Due to convenience, open-source software is widely used. For beneficial reasons, open-source maintainers often fix the vulnerabilities silently, exposing their users unaware of the updates to threats. Previous works all focus on black-box binary detection of the silent dependency alerts that suffer from high false-positive rates. Open-source software users need to analyze and explain AI prediction themselves. Explainable AI becomes remarkable as a complementary of black-box AI models, providing details in various forms to explain AI decisions. Noticing there is still no technique that can discover silent dependency alert on time, in this work, we propose a framework using an encoder-decoder model with a binary detector to provide explainable silent dependency alert prediction. Our model generates 4 types of vulnerability key aspects including vulnerability type, root cause, attack vector, and impact to enhance the trustworthiness and users' acceptance to alert prediction. By experiments with several models and inputs, we confirm CodeBERT with both commit messages and code changes achieves the best results. Our user study shows that explainable alert predictions can help users find silent dependency alert more easily than black-box predictions. To the best of our knowledge, this is the first research work on the application of Explainable AI in silent dependency alert prediction, which opens the door of the related domains