Efficient Exploitation of Radio Frequency and Visible Light Communication Bands for D2D in Mobile Networks

The concept of device-to-device (D2D) communication, combining common radio frequency (RF) and visible light communication (VLC), is seen as a feasible way how to cope with spectrum crunch in the RF domain and how to maximize spectral efficiency in general. In this paper, our objective is to decide when RF should be utilized or if VLC proves to be the more profitable option. The selection between RF and VLC is defined as a multi-objective optimization problem targeting primarily to minimize the outage ratio while the secondary objective is to maximize the sum capacity of D2D pairs, composed by D2D transmitters and D2D receivers. To solve this problem, we design a centralized low-complexity heuristic algorithm selecting either RF or VLC band for each D2D pair relying on the mutual interference among the pairs. For interpretation of the mutual interference among the D2D pairs, we exploit directed weighted graphs adopted from the graph theory. The simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of the outage ratio, sum capacity and average energy efficiency. What is more, despite a very low complexity, the proposed algorithm reaches a close-to-optimum performance provided by the exhaustive search algorithm

Waveform Optimization for Large-Scale Multi-Antenna Multi-Sine Wireless Power Transfer

Wireless power transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things, machine-to-machine communications and radio frequency identification networks. Although there has been some progress towards multi-antenna multi-sine WPT design, the large-scale design of WPT, reminiscent of massive multiple-input multiple-output (MIMO) in communications, remains an open problem. Considering the nonlinear rectifier model, a multiuser waveform optimization algorithm is derived based on successive convex approximation (SCA). A lower-complexity algorithm is derived based on asymptotic analysis and sequential approximation (SA). It is shown that the difference between the average output voltage achieved by the two algorithms can be negligible provided the number of antennas is large enough. The performance gain of the nonlinear model based design over the linear model based design can be large, in the presence of a large number of tones.Comment: To appear in the 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2016

HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks

The unsupervised detection of anomalies in time series data has important applications in user behavioral modeling, fraud detection, and cybersecurity. Anomaly detection has, in fact, been extensively studied in categorical sequences. However, we often have access to time series data that represent paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross-referenced documents, or travel itineraries in transportation networks. To reliably detect anomalies, we must account for the fact that such data contain a large number of independent observations of paths constrained by a graph topology. Moreover, the heterogeneity of real systems rules out frequency-based anomaly detection techniques, which do not account for highly skewed edge and degree statistics. To address this problem, we introduce HYPA, a novel framework for the unsupervised detection of anomalies in large corpora of variable-length temporal paths in a graph. HYPA provides an efficient analytical method to detect paths with anomalous frequencies that result from nodes being traversed in unexpected chronological order.Comment: 11 pages with 8 figures and supplementary material. To appear at SIAM Data Mining (SDM 2020