1,720,996 research outputs found
Quantum Discrimination of Noisy Photon-Subtracted Squeezed States
No full textQuantum state discrimination (QSD) is crucial for various applications of quantum sensing, communications, and computing. This paper characterizes the binary QSD with photon-subtracted squeezed states (PSSSs) affected by thermal noise during state preparation. First, noisy PSSSs are characterized and their Fock representation is given. Then, the binary QSD problem with noisy PSSSs affected by phase diffusion is presented. Finally, it is shown the suitability of PSSSs for QSD-based applications, especially when employing quantum states with unbalanced prior probabilities
Neural Network Based Node Prioritization for Efficient Localization
No full textOptimizing the resource utilization is essential for efficiently providing reliable location awareness in complex wireless environments. This paper presents a data-driven approach to node prioritization for efficient localization based on neural networks. We develop a node prioritization strategy for power allocation consisting of offline training and online operation. In the offline phase, we train a neural network to approximate a mapping of node prioritization decisions obtained via model-based optimization. In the online phase, the trained neural network is employed to determine the resource allocation. A case study validates the proposed approach and compares it against conventional methods based on uniform power allocation
Beyond 5G Localization at mmWaves in 3GPP Urban Scenarios with Blockage Intelligence
No full textAccurate positional information is crucial for numerous emerging applications in fifth generation (5G) and beyond wireless ecosystems. However, the localization requirements defined by the 3rd Generation Partnership Project (3GPP) are particularly challenging to achieve, especially in complex environments such as urban scenarios, due to non-line-of-sight conditions, outdoor-to-indoor penetration loss, and multipath propagation. Such effects are detrimental to localization accuracy, especially at mmWaves. This paper introduces the concept of blockage intelligence (BI) to provide a probabilistic representation of wireless propagation conditions. Such representation is then exploited in soft information (SI)-based localization to overcome the limitations of conventional localization approaches. Localization case studies are presented according to the 3GPP-standardized urban microcell (UMi) scenario at mmWaves with fully 3GPP-compliant simulations. Results show that BI together with SI-based localization is able to provide a significant performance gain with respect to existing techniques in 5G and beyond wireless networks
Location Secrecy Enhancement in Adversarial Networks via Trajectory Control
No full textIn networked environments, adversaries may exploit location information to perform carefully crafted attacks on cyber-physical systems (CPS). To prevent such security breaches, this letter develops a network localization and navigation (NLN) paradigm that accounts for network secrecy in the control of mobile agents. We consider a scenario in which a mobile agent is tasked with maneuvering through an adversarial network, based on a nominal control policy, and we aim to reduce the ability of the adversarial network to infer the mobile agent's position. Specifically, the Fisher information of the agent's position obtained by the adversarial network is adopted as a secrecy metric. We propose a new control policy that results from an optimization problem and achieves a compromise between maximizing location secrecy and minimizing the deviation from the nominal control policy. Results show that the proposed optimization-based control policy significantly improves the secrecy of the mobile agent
Communication-Efficient Distributed Learning Over Networks-Part II: Necessary Conditions for Accuracy
No full textDistributed learning is crucial for many applications such as localization and tracking, autonomy, and crowd sensing. This paper investigates communication-efficient distributed learning of time-varying states over networks. Specifically, the paper considers a network of nodes that infer their current states in a decentralized manner using observations obtained via local sensing and messages obtained via noisy inter-node communications. The paper derives a necessary condition in terms of the sensing and communication capabilities of the network for the boundedness of the learning error over time. The necessary condition is compared with the sufficient condition established in a companion paper and the gap between the two conditions is discussed. The paper provides guidelines for efficient management of the sensing and communication resources for distributed learning in complex networked systems
Node Deployment under Position Uncertainty for Network Localization
No full textNetwork localization performance depends on the network geometry and, therefore, node deployment methods are critical for high-accuracy localization. Optimal node deployment is challenging in practical problems due to various uncertainties present in the position knowledge of the deployed nodes. In this paper, we propose a node-deployment method for network localization that accounts for such uncertainties. We develop a framework for the optimal deployment of location-aware networks under bounded disturbances in the positions of the sensing nodes. More specifically, by considering bounded discrepancies in the network geometry, we characterize the optimal deployment according to the D-optimality criterion and assert its implications for the A-optimality and E-optimality criteria. Results show that the proposed optimization-based design achieves a significative improvement according to the D-optimality criterion
Filtering Over Non-Gaussian Channels: The Role of Anytime Capacity
No full textFiltering over noisy channels is of interest in many network applications, in which a node infers a time-varying state by using messages received from another node that can observe such a state. This letter explores filtering with general models for state disturbance and communication channels by deriving a sufficient condition for which the estimation error is bounded. Specifically, the sufficient condition is expressed in terms of anytime capacity, a notion that characterizes the maximum sequential communication rate. The joint design of encoder and estimator with bounded estimation error is also presented
Location Awareness Via Intelligent Surfaces: A Path Toward Holographic NLN
No full textLocation information is critical for numerous applications, such as smart cities, autonomous vehicles, and Industry 4.0. In this article, we present the paradigm of holographic network localization and navigation (NLN), namely NLN with holographic radios. We describe holographic NLN enabled by reconfigurable intelligent surfaces (RISs), where the phase responses of the RISs are controlled to create desirable electromagnetic (EM) environments for wireless communications. Specifically, we present a mathematical model for signal transmission and reception in the presence of RISs. This model applies to both continuous intelligent surfaces (CISs) and discrete intelligent surfaces (DISs) and accounts for the polarization and directivity of antennas. We review recent research progress on RISs and summarize their characteristics that benefit communication and localization. We present key ingredients of NLN and describe how RISs can be employed to enable holographic NLN. We quantify the performance of RIS-enabled holographic NLN with a case study, showing that RISs can significantly improve localization accuracy, especially in scenarios where line-of-sight (LOS) paths between agents and anchors are obstructed
Communication-Efficient Distributed Learning Over Networks-Part I: Sufficient Conditions for Accuracy
No full textDistributed learning is an important task in emerging applications such as localization and navigation, Internet-of-Things, and autonomous vehicles. This paper establishes a theoretical framework for learning states that evolve in real time over networks. Specifically, each agent node in the network aims to infer a time-varying state in a decentralized manner by using the node's local observations and the messages received from other nodes within its communication range. As a result, the inference accuracy of a node is significantly affected by the quality of its received messages. This calls for carefully designed strategies for generating messages that are able to provide sufficient information for the receiver and are robust to channel impairments. This paper presents communication-efficient encoding strategies for generating transmitted messages and derives a sufficient condition for the boundedness of the distributed inference error of all the agent nodes over time. The findings of this paper provide guidelines for the design of communication-efficient distributed learning in complex networked systems
Latency in Downlink Cellular Networks with Random Scheduling
No full textThe characterization of the latency is essential for operation of 5G and beyond 5G cellular networks. This paper develops a spatiotemporal model to characterize latency in the downlink of large-scale cellular networks with random scheduling. In particular, the framework integrates stochastic geometry and queueing theory, to capture the interwoven interactions between the microscopic behavior of each wireless link and the macroscopic mutual interference between all links in the network. The developed framework enables a traffic-aware characterization of the transmission success probability and of the latency across the network
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