Location awareness plays an increasingly important role in wireless network applications. However, accurate localization in complex indoor environments remains challenging for existing radio frequency (RF)-based systems, among which the ultra-wide bandwidth (UWB) technology ranks to be the most promising one due to its capability in providing channel information with fine time resolution. In this paper, we propose a multipath-assisted single-anchor localization framework that can provide high-accuracy positional information in complex indoor environments. Specifically, a deep variational learning method is proposed to produce calibrated estimates of position-related parameters, including distance, time-difference-of-arrival and angle-of-arrival, which are then fed into a multipath-assisted single-anchor localization algorithm. The proposed method is implemented on self-built UWB transceivers and assessed with real-world data from an indoor measurement campaign. Extensive experimental results show that the proposed method outperforms conventional machine learning-based error mitigation approaches and can achieve 0.15m root mean square position error in non-line-of-sight scenarios.

Passive localization is an important aspect of integrated sensing and communication (ISAC). However, it is challenging to estimate the target position and velocity accurately from the receiving signals due to complex multipath propagation. This article presents TDLoc, a multiple-input–multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM)-based passive localization and tracking system, using channel state information (CSI). We first proposed a fourth-order tensor model that contains the Angle-of-Departure (AoD), Angle-of-Arrival (AoA), Time-of-Flight (ToF), and Doppler frequency shifts (DFSs) information, followed by developing an efficient joint estimation algorithm. We also show that with more than one pair of transceivers, our method can obtain the target velocity from the relativistic Doppler effects, leading to additional DFS-based trajectory information. Moreover, the Cramér–Rao lower bound (CRLB) for multipath parameter estimation and positioning is derived for performance evaluation. Numerical results show that TDLoc outperforms state-of-the-art methods in terms of localization accuracy.

Software defect prediction aims to automatically locate defective code modules to better focus testing resources and human effort. Typically, software defect prediction pipelines are comprised of two parts: the first extracts program features, like abstract syntax trees, by using external tools, and the second applies machine learning-based classification models to those features in order to predict defective modules. Since such approaches depend on specific feature extraction tools, machine learning classifiers have to be custom-tailored to effectively build most accurate models.

This paper studies the secure network coding problem for a unicast wiretap network of type II, where an eavesdropper can intercept a bounded rate of the encoded information from the compromised links. We first discuss the upper bound on the secrecy rate and then figure out the upper bound can be tight for the unicast wiretap network II if each relay within the network is allowed to decode and encode the coset codes. Moreover, the relative proofs are presented in this paper.

The two-way training discriminatory channel estimation (DCE) for non-reciprocal wireless MIMO channels can efficiently differentiate the performance at the legitimate receiver (Bob) and the eavesdropper (Eve). Eve's channel estimation performance is deliberately degraded by the artificial noise (AN) inserted into the training sequence. However, it ignores the possibility that Eve can derive any useful information which can be used to reduce the AN interference during the round-trip stage. In this paper, we discuss the feasibility that Eve can derive certain helpful information from its observations during the two-way training DCE. Then, we propose a pseudo downlink channel (PDC) scheme for Eve to improve its channel estimation performance, which collapses the security of legitimate receiver.