1,721,129 research outputs found
Source Engineering for Quantum Key Distribution with Noisy Photon-Added Squeezed States
No full textQuantum key distribution (QKD) is a key enabler toward unconditionally secure communications. The imperfections exhibited by non-ideal sources degrade the QKD performance. This raises the problem of engineering the employed quantum states to mitigate the impairments caused by such imperfections. This paper proposes to employ noisy photon-added squeezed states (PASSs) as QKD sources for the decoy-state protocol. First, noisy PASSs are characterized in the Fock space. Then, noisy PASSs are engineered for the decoy-state protocol. Finally, the performance of the decoy-state protocol with engineered noisy PASSs are quantified in a variety of settings
Selection of Reference Base Station for TDOA-based Localization in 5G and Beyond IIoT
No full textLocation awareness is fundamental for several applications operating in fifth generation (5G) and beyond wireless networks. To provide accurate localization in 5G networks, time difference-of-arrival (TDOA) measurements are commonly employed. However, the quality of TDOA measurements significantly impacts the localization accuracy and heavily depends on the selection of the reference base station (RBS). Selecting the best RBS is particularly challenging in cluttered environments, such as Industrial Internet-of-Things (IIoT) environments, due to non-line-of-sight conditions and multipath propagation. This paper proposes a machine learning-based method for RBS selection, leveraging the rich information encapsulated in the received signals. The localization performance gain provided by the proposed approach is quantified in the 3rd Generation Partnership Project indoor factory scenario. Results show that the proposed RBS selection method provides a new level of location awareness for applications operating in 5G and beyond environments
Inferring Spatiotemporal Mobility Patterns from Multidimensional Trip Data
No full textThe massive amount of data related to spatiotemporal mobility offers new opportunities to understand human behaviors. However, with the increase of volume and complexity of mobility data, it has become challenging to retrieve important information and critical features of spatiotemporal mobility. In particular, predicting large-scale travel demands is challenging and requires a high computational load. This paper introduces a data-driven approach for estimating high-dimensional travel demands. We propose a method to identify mobility patterns using a probabilistic tensor decomposition approach for interpreting the complexity and uncertainty of mobility data. Expectation-maximization (EM) algorithm is applied for inferring mobility patterns. A case study is presented, where the proposed model is applied to New York city taxi data. The results show the model performance according to the number of origin and destination patterns and the number of trip data used. The probabilistic modeling results provide a deeper understanding of large-scale mobility data in the spatiotemporal dimension
Quantum Quadrature Amplitude Modulation with Photon-Added Gaussian States
No full textQuantum communication systems exchange information between a source and a destination by encoding such information into quantum states according to modulation techniques. The design of quantum modulation techniques, including quantum quadrature amplitude modulation (QAM), requires choosing and characterizing quantum states. This paper explores the use of non-Gaussian photon-added Gaussian states (PAGSs) for quantum QAM communications. First, we characterize pure PAGSs in the Fock space and derive a closed-form expression for their inner product. Then, we develop a quantum QAM technique employing PAGSs. In particular, we show how to construct quantum QAM constellations of PAGSs. Finally, we evaluate the performance of the developed quantum QAM technique with PAGSs, when a square root measurement (SRM) receiver is employed, and compare such performance with that of existing coherent states. Results show that PAGSs can provide a performance gain with respect to coherent states
Machine Learning Based Node Selection for UWB Network Localization
No full textIn location-aware networks, only a subset of nodes provides representative measurements for position inference. Therefore, efficient high-accuracy localization calls for strategies to select an appropriate subset of active nodes. While node selection strategies benefit efficient localization, determining an optimal subset of active nodes relies on knowledge of channel state information whose acquisition overhead can be prohibitive. This paper presents a probabilistic node selection strategy for ultra-wideband network localization based on machine learning. We formulate the node selection problem as a classification task given a position estimate and determine near-optimal access probabilities from training data obtained via model-based optimization. A case study in a 3rd Generation Partnership Project scenario validates the proposed strategy and compares it against uniformly distributed random node selection
UWB Localization-of-Things Via Soft Information: Network Experimentation in Indoor Environment
No full textLocation awareness is crucial for numerous applications in fifth generation (5G) and beyond ecosystems. Localization-of-things (LoT) via soft information (SI) enables accurate localization, tracking, and navigation of networked nodes. This paper demonstrates real-time SI-based LoT using ultrawideband (UWB) radios. We consider two data collection approaches and evaluate them via network experimentation in an indoor environment. Experimental results show the potential of SI-based LoT using UWB technology to satisfy service level requirements of 5G and beyond ecosystems
Multi-agent Reinforcement Learning for Distributed Cooperative Vehicular Positioning
No full textWith the advent of cooperative intelligent transport systems (C-ITS) and vehicle-to-everything (V2X) communications, cooperative positioning based on V2X sharing of location information has been emerging as a promising augmentation system for conventional satellite navigation. An example is implicit cooperative positioning (ICP) which relies on Bayesian filtering for cooperative sensing of targets that are used as reference points for improving vehicle positioning. ICP methods, however, rely on pre-determined models which makes them sub-optimal in case of non-Gaussian non-linear models or complex cooperation graphs. To address these limitations, the paper proposes a decentralized-partially observable Markov decision process (Dec-POMDP) framework, paired with deep multi-agent reinforcement learning (MARL) algorithms. We introduce a novel ICP-multi-agent proximal policy optimization (MAPPO) algorithm where distributed agents (i.e., vehicles) dynamically activate/deactivate the radio links for cooperation with the neighbors to optimize the communication efficiency, still guaranteeing accurate positioning. We reproduce a realistic C-ITS scenario with CARLA simulator, where vehicles move according to real-world dynamics and communicate with each other to cooperatively sense their locations. Results show that the proposed ICP-MAPPO algorithm, with its dynamic-decentralized-execution and centralized-training schemes, outperforms state-of-the-art ICP methods by 21% in terms of positioning accuracy, and it can reduce the communication overhead by following the optimal learned policy
Real-Time Bayesian Neural Networks for 6G Cooperative Positioning and Tracking
No full textIn the evolving landscape of 5G new radio and related 6G evolution, achieving centimeter-level dynamic positioning is pivotal, especially in cooperative intelligent transportation system frameworks. With the challenges posed by higher path loss and blockages in the new frequency bands (i.e., millimeter waves), machine learning (ML) offers new approaches to draw location information from space-time wide-bandwidth radio signals and enable enhanced location-based services. This paper presents an approach to real-time 6G location tracking in urban settings with frequent signal blockages. We introduce a novel teacher-student Bayesian neural network (BNN) method, called Bayesian bright knowledge (BBK), that predicts both the location estimate and the associated uncertainty in real-time. Moreover, we propose a seamless integration of BNNs into a cellular multi-base station tracking system, where more complex channel measurements are taken into account. Our method employs a deep learning (DL)-based autoencoder structure that leverages the complete channel impulse response to deduce location-specific attributes in both line-of-sight and non-line-of-sight environments. Testing in 3GPP specification-compliant urban micro (UMi) scenario with ray-tracing and traffic simulations confirms the BBK’s superiority in estimating uncertainties and handling out-of-distribution testing positions. In dynamic conditions, our BNN-based tracking system surpasses geometric-based tracking techniques and state-of-the-art DL models, localizing a moving target with a median error of 46 cm
Networked Filtering with Feedback for Continuous-Time Observations
No full textThis paper investigates distributed filtering in continuous-time scenarios building on an information-theoretic view of Kalman–Bucy filtering. We consider a two-node system where each node is associated with a time-varying state and obtains noisy observations of both nodal states at each time. In addition, one of the two nodes receives encoded messages from the other node via a Gaussian channel with feedback and infers its current state based on available observations and received messages. We design a real-time encoding strategy for generating the transmitted messages and show under which conditions this strategy is optimal. Moreover, we present a relation between information dissipation rate and Fisher information for distributed filtering. Our finding is an extension of the connection established by Mitter and Newton between Shannon and Fisher information for Kalman–Bucy filtering
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