Proportional fairness in wireless powered CSMA/CA based IoT networks

This paper considers the deployment of a hybrid wireless data/power access point in an 802.11-based wireless powered IoT network. The proportionally fair allocation of throughputs across IoT nodes is considered under the constraints of energy neutrality and CPU capability for each device. The joint optimization of wireless powering and data communication resources takes the CSMA/CA random channel access features, e.g. the backoff procedure, collisions, protocol overhead into account. Numerical results show that the optimized solution can effectively balance individual throughput across nodes, and meanwhile proportionally maximize the overall sum throughput under energy constraints.Comment: Accepted by Globecom 201

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.Comment: Accepted to the thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), 9 pages, 5 figure

Modeling the performance of distributed fiber optical sensor based on spontaneous Brillouin scattering

An optical model to simulate the distributed fiber optical sensor based on spontaneous Brillouin spectrum is derived. The reliability of this model is validated with experimental measurements. Using this analytical expression, parametric studies are conducted to investigate impacts of key factors including fiber loss, signal to noise ratio, bandwidth and scanning step on the optical fiber sensor measurement error. The simulation results exhibit good agreement with previous published calculation results. Applying this novel model into the data interpretation, measurement error of distributed fiber optical sensor based on spontaneous Brillouin scattering can be better controlled

Constrained stochastic LQ control with regime switching and application to portfolio selection

This paper is concerned with a stochastic linear-quadratic optimal control problem with regime switching, random coefficients, and cone control constraint. The randomness of the coefficients comes from two aspects: the Brownian motion and the Markov chain. Using It\^{o}'s lemma for Markov chain, we obtain the optimal state feedback control and optimal cost value explicitly via two new systems of extended stochastic Riccati equations (ESREs). We prove the existence and uniqueness of the two ESREs using tools including multidimensional comparison theorem, truncation function technique, log transformation and the John-Nirenberg inequality. These results are then applied to study mean-variance portfolio selection problems with and without short-selling prohibition with random parameters depending on both the Brownian motion and the Markov chain. Finally, the efficient portfolios and efficient frontiers are presented in closed forms

Comparison theorems for multi-dimensional BSDEs with jumps and applications to constrained stochastic linear-quadratic control

In this paper, we, for the first time, establish two comparison theorems for multi-dimensional backward stochastic differential equations with jumps. Our approach is novel and completely different from the existing results for one-dimensional case. Using these and other delicate tools, we then construct solutions to coupled two-dimensional stochastic Riccati equation with jumps in both standard and singular cases. In the end, these results are applied to solve a cone-constrained stochastic linear-quadratic and a mean-variance portfolio selection problem with jumps. Different from no jump problems, the optimal (relative) state processes may change their signs, which is of course due to the presence of jumps

Constrained monotone mean-variance problem with random coefficients

This paper studies the monotone mean-variance (MMV) problem and the classical mean-variance (MV) problem with convex cone trading constraints in a market with random coefficients. We provide semiclosed optimal strategies and optimal values for both problems via certain backward stochastic differential equations (BSDEs). After noting the links between these BSDEs, we find that the two problems share the same optimal portfolio and optimal value. This generalizes the result of Shen and Zou $[$ SIAM J. Financial Math., 13 (2022), pp. SC99-SC112$]$ from deterministic coefficients to random ones

Simulation of BOTDA and Rayleigh COTDR systems to study the impact of noise on dynamic sensing

This is the author acepted manuscript. It is currently under an indefinite embargo pending publication of the final version.Dynamic distributed sensing of strain and temperature is the key for real-time structural health monitoring (SHM) across a wide range of geo-engineering challenges, for which Brillouin Optical Time Domain Analysis (BOTDA) and Rayleigh Coherent Optical Time Domain Reflectometry (COTDR) are promising candidates. A noise model with specific parametric simulation of the two systems has been developed. Noise in both laser(s) and detector is independently simulated to identify the key noise sources. In this simulation, although averaging can significantly enhance the signal-to-noise ratio (SNR) in the two systems, it is a barrier to dynamic sensing due to its time-consuming accumulation procedure. The sequence of averaging in the signal processing workflow can vary the SNR for the two systems. The system components should be optimized to reduce the averaging times to achieve the required system specifications, especially the dynamic sensing performance.This project was carried out under the UCL-Cambridge Centre for Doctoral Training in Photonic Systems Development, with funding from EPSRC (EP/G037256/1) gratefully acknowledged. The funding from Cambridge Centre for Smart Infrastructure and Construction is acknowledged