Related papers: Interval Estimation for Conditional Failure Rates …
An efficient scheme for initial ranging has recently been proposed by X. Fu et al. in the context of orthogonal frequency-division multiple-access (OFDMA) networks based on the IEEE 802.16e-2005 standard. The proposed solution aims at…
This is the second part of a two-part paper on data-based distributionally robust stochastic optimal power flow (OPF). The general problem formulation and methodology have been presented in Part I [1]. Here, we present extensive numerical…
The mitigation of nonlinear distortion caused by power amplifiers (PA) in Orthogonal Frequency Division Multiplexing (OFDM) systems is an essential issue to enable energy efficient operation. In this work we proposed a new algorithm for…
The failure of overhead transmission lines in the United States can lead to significant economic losses and widespread blackouts, affecting the lives of millions. This study focuses on the reliability of transmission lines, specifically…
In this paper, we propose novel low-complexity adaptive channel estimation techniques for mob ile wireless chan- n els in presence of Rayleigh fading, carrier frequency offsets (CFO) and random channel variations. We show that the selective…
Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem. This is because the number of hypotheses grows exponentially with the network size. A new…
The performance of machine learning models can be impacted by changes in data over time. A promising approach to address this challenge is invariant learning, with a particular focus on a method known as invariant risk minimization (IRM).…
Both the first-order signal statistics (e.g. the outage probability) and the second-order signal statistics (e.g. the average level crossing rate, LCR, and the average fade duration, AFD) are important design criteria and performance…
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops. Being able to estimate a model's performance on test data is important in practice…
This paper deals with the challenging problem of spectrum sensing in cognitive radio. We consider a stochastic system model where the the Primary User (PU) transmits a periodic signal over fading channels. The effect of frequency offsets…
We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a fixed, reference-conditional setting. Existing CTM detectors construct test martingales by continually…
Cellular networks are usually modeled by placing the base stations on a grid, with mobile users either randomly scattered or placed deterministically. These models have been used extensively but suffer from being both highly idealized and…
Uncertainty modeling has become increasingly important in power system decision-making. The widely-used tractable uncertainty modeling method-chance constraints with Conditional Value at Risk (CVaR) approximation, can be overconservative…
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models, so methods of predicting a model's performance on OOD data without labels are important for machine learning safety. While a number of methods have…
This paper introduces a method for detecting, estimating, and localising a soft fault in wired communication networks. The proposed method is based on analysing the transmission coefficients (TC) in the time domain under both fault-free and…
In this work, we discuss techniques for coherently detecting turbo coded orthogonal frequency division multiplexed (OFDM) signals, transmitted through frequency selective Rayleigh (the magnitude of each channel tap is Rayleigh distributed)…
The binary exponential backoff scheme is widely used in WiFi 7 and still incurs poor throughput performance under dynamic channel environments. Recent model-based approaches (e.g., non-persistent and $p$-persistent CSMA) simply optimize…
We propose a new method for predicting multiple missing links in partially observed networks while controlling the false discovery rate (FDR), a largely unresolved challenge in network analysis. The main difficulty lies in handling complex…
Temporal logic inference is the process of extracting formal descriptions of system behaviors from data in the form of temporal logic formulas. The existing temporal logic inference methods mostly neglect uncertainties in the data, which…
Today, detection of anomalous events in civil infrastructures (e.g. water pipe breaks and leaks) is time consuming and often takes hours or days. Pipe breakage as one of the most frequent types of failure of water networks often causes…