Distributing entanglement between distant nodes is an essential task in quantum networks. To achieve this task, quantum repeaters have been introduced to perform entanglement swapping. This paper offers a design of remote entanglement distribution (RED) protocols that maximize the entanglement distribution rate (EDR). We introduce the concept of enodes, representing the entangled quantum bit (qubit) pairs in the network. This concept enables us to design the optimal RED protocols based on the solutions of some linear programming problems. Moreover, we investigate RED in a homogeneous repeater chain, which is a building block for many quantum networks. In particular, we determine the maximum EDR for homogeneous repeater chains in a closed form. Our results enable the distribution of long-distance entanglement with noisy intermediate-scale quantum (NISQ) technologies and provide insights into the design and implementation of general quantum networks.

Queuing delay is an essential topic in the design of quantum networks. This paper introduces a tractable model for analyzing the queuing delay of quantum data, referred to as quantum queuing delay (QQD). The model employs a dynamic programming formalism and accounts for practical aspects such as the finite memory size. Using this model, we develop a cognitive-memory-based policy for memory management and show that this policy can decrease the average queuing delay exponentially with respect to memory size. Such a significant reduction can be traced back to the use of entanglement, a peculiar quantum phenomenon that has no classical counterpart. Numerical results validate the theoretical analysis and demonstrate the near-optimal performance of the developed policy.

Remote state preparation (RSP) is a technique to transmit quantum states with classical communication and previously shared entanglement. In this paper, we consider RSP in a multiparty setting. A simple yet nontrivial case is studied, where there is one sender and two receivers. We put forth a broadcasting method for developing multiparty RSP protocols. We show that this method can remotely prepare arbitrary states by consuming classical and quantum resources. For preparing highly entangled states, the proposed method achieves the lower bound for the amount of consumed resources asymptotically.

Reliable and accurate position information is of great importance for many mass-market and emerging applications. Network localization and navigation (NLN) is a promising paradigm to provide such information ubiquitously, where a network of nodes is used to aid in localizing its members. This paper explores various network operation strategies, which play an essential role in NLN as they determine the network lifetime and localization accuracy. Efficient network operation requires several functionalities, including node prioritization, node activation, and node deployment. The roles of these functionalities are described and different techniques for implementing respective functionalities via algorithmic modules are introduced. Some important concepts such as cooperative operation, robustness guarantee, and distributed design in the development of the network operation strategies are also introduced. Finally, numerical results are provided to demonstrate the localization performance improvement attributed to the optimized network operation strategies.

Network localization and navigation (NLN) is a promising paradigm, in which mobile nodes exploit spatiotemporal cooperation, to provide reliable location information for a diverse range of wireless applications. This paper presents a theoretical foundation of NLN, including a mathematical formulation for NLN, an introduction of equivalent Fisher information analysis, and determination of the fundamental limits of localization accuracy. Key ingredients such as spatiotemporal cooperation, array signal processing, and map exploitation are then studied. We also develop a geometric interpretation to provide insights into the essence of NLN for network design. Finally, the paper highlights the connection between the theoretical foundation and algorithm development for NLN, guiding the design and operation of practical localization systems.

This letter establishes a methodology to design threshold profiles for wideband ranging systems. Differently from conventional methods using a single threshold value that is designed based on the signal detection and false-alarm probability requirements, we propose a threshold profile that is designed based also on the ranging error requirement. The proposed method relies only on channel statistics without requiring channel estimation. A case study shows that, compared to conventional methods, the proposed method for threshold profiling significantly improves the performance in terms of false-alarm probability, detection probability, and ranging error.

Network localization is an emerging paradigm for providing high-accuracy positional information in GPS-challenged environments. To enable efficient network localization, we propose node prioritization strategies for allocating transmission resources among network nodes. This paper develops a computational geometry framework for determining the optimal node prioritization strategy. The framework consists of transforming each node prioritization strategy into a point in a Euclidian space and exploiting geometric properties of these points. Under this framework, we prove the sparsity property of the optimal node prioritization vector (NPV) and reduce the search space of the optimal NPV. Our approach yields exact optimal solutions rather than ε-approximate solutions for efficient network localization. Numerical results show that the proposed approach can significantly reduce the computational complexity of prioritization strategies and improve the accuracy of network localization.

Location-awareness enables a variety of emerging applications on mobile devices. For indoor applications, a desirable way of obtaining real-time locations is by combining different sources of positional information, such as the inertial measurements, ranging measurements, and map information with an infrastructure-free system that does not rely on any customized hardware. These sources of information can be incorporated into the paradigm of network localization and navigation (NLN). However, there still lacks an infrastructure-free localization system that applies the insights of NLN to effectively fuse different types of information. In this paper, we present the Mercury system, which realizes the key ideas of NLN, including the exploitation of spatiotemporal cooperation and the use of environmental knowledge. We design a real-time belief propagation algorithm to fuse inertial measurements as well as range measurements among different users with map information. We implement this algorithm in the Mercury system formed by a network of smartphones, and evaluate its localization accuracy through experimentation. Results show that Mercury provides reliable location information and that combining spatiotemporal cooperation with environmental knowledge remarkably reduces the location uncertainty of users. Moreover, the performance of Mercury is more robust to imperfect initial positional knowledge compared with that of existing systems.

Sharing users' positional information is often required for high accuracy localization, which is a cornerstone for numerous wireless applications in commercial, military, and social sectors. This raises a concern of leaking positional information to adversaries during the localization process. This paper establishes a mathematical foundation for location secrecy. In particular, we determine the location secrecy metric (LSM) for a localization network in which an eavesdropper has a generic measurement capability. We obtain a simple and closed-form expression of the LSM for the case where the eavesdropper's measurement of a user's position is corrupted by a Gaussian noise. We further extend this result to a more general measurement model, and design location secrecy protection strategies based on the insights gained from our analysis. The performance of the proposed algorithms is verified through numerical simulation.

Wireless network localization (WNL) is a paradigm proposed recently for providing reliable location services. The localization performance is highly dependent on the placement of nodes in the network. In this paper, we study the node placement problem for localization networks. We first propose a mathematical formulation of the node placement problem. Such a formulation takes into account the uncertainty in the position of the placed nodes as well as the constraints on the region where the nodes can be deployed. Based on this formulation, we propose a near-optimal node placement algorithm, and present an upper bound on the gap between its performance with that of the optimal placement strategy. The proposed algorithm is validated by numerical results.

Resource management in the power and time-frequency domains is an important issue in distributed network localization. Since highly accurate ranging requires a large amount of time-frequency resources, cooperation among nodes without proper link selection may not be feasible. To address this issue, two resource management games are formulated, and Stackelberg equilibrium and link bargaining equilibrium are proposed as the solution concepts for efficient link selection and power allocation. Distributed algorithms are derived and analyzed using game theoretical approaches. It is demonstrated that the proposed strategies can achieve a lower mean squared error of position estimation with fewer ranging measurements.

High-precision positioning techniques have a promising future in various applications. Recent work shows that inter-node cooperation can increase the positioning accuracy. In general, such cooperation consumes additional power, and current power application techniques are limited in synchronous networks. To study the power allocation in asynchronous networks, this paper proposes a power management game for cooperative localization, where each user allocates power that minimizes its square error positioning bound (SPEB) penalized by the power cost. Such game-theoretic power allocation policy not only considers how to allocate power over different cooperative links, but also manages the total power consumed for a better performance and power trade-off. We show that, in two-user networks, the power management game admits a unique cooperating Nash Equilibrium (NE) in high SNR region. In addition, as the power budget goes to infinity, the proposed game-theoretic power allocation policy can achieve SPEB arbitrarily close to that under exhaustive power allocation, but requires much less power.

Wireless network localization (WNL) is an emerging paradigm for providing high-accuracy positional information in GPS-challenged environments. The localization performance of a node in WNL is determined by the allocation of transmit resources among its neighboring nodes. To achieve the best localization performance, we develop a computational geometry framework for optimal resource allocation in WNL. We first determine an affine map that transforms each resource allocation strategy into a point in 3-D Euclidian space. By exploiting geometric properties of these image points, we prove the sparsity property of the optimal resource allocation vector, i.e., the optimal localization performance can be achieved by allocating resources to only a small subset of neighboring nodes. Moreover, these geometric properties enable the reduction of the search space for optimal solutions, based on which we design efficient resource allocation strategies. Numerical results show that the proposed strategies can achieve significant improvements in both localization performance and computation efficiency. Our approach provides a new methodology for resource allocation in network localization, yielding exact optimal solutions rather than ϵ-approximate solutions.

Network navigation is an emerging paradigm that enables high-accuracy location awareness in GPS-challenged environments via spatiotemporal cooperation. Two important operations of network navigation, location inference and power control, interrelate with each other, thus motivating the joint design of inference and control algorithms. In this paper, we develop efficient algorithms for network navigation with power control. In particular, we first determine the confidence region for location inference based on Fisher information analysis, and then design robust power control that minimizes the location inference errors of the agents within the confidence region. Both centralized and distributed algorithms are developed for energy-efficient network navigation. Simulation results show that the proposed algorithms significantly reduce the location inference errors compared to those without power control.

Device-to-device (D2D) communication in cellular networks is a promising concept that permits cooperation among mobile devices not only to increase data throughput but also to enhance localization services. In those networks, the allocation of transmitting power plays a critical role in determining network lifetime and localization accuracy. Meanwhile, it is a challenging task for implementation in cooperative D2D networks, since each device has only imperfect estimates of local network parameters in distributed settings. In this paper, we establish an optimization framework for robust power allocation in cooperative wireless network localization, and develop distributed power allocation strategies. In particular, we decompose the power allocation problem into infrastructure and cooperation phases, show the sparsity property of the optimal power allocation, and develop efficient power allocation strategies. Simulation results show that these strategies can achieve significant performance improvement in localization accuracy compared to the uniform strategies.

Network navigation is an emerging paradigm that enables high-accuracy location awareness in GPS-challenged environments. Two important operations of network navigation, location inference and power control, interrelate with each other, thus motivating the design of joint inference and control algorithms. In this paper, we develop efficient network navigation algorithms with optimized energy allocation. In particular, we first determine the confidence region for lzocation inference based on Fisher information analysis, and then design robust energy allocation strategies that minimize the position errors of the agents within the confidence region. Based on these strategies, both centralized and distributed energy-efficient network navigation algorithms are developed. Simulation results show that the proposed algorithms significantly reduce the position errors compared to the algorithms with uniform or non-robust power control.

Accurate position information enables numerous location-based applications. Wireless network localization is a promising localization technique that permits cooperation among mobile objects to enhance localization services. The allocation of transmitting power for such networks plays a critical role since it determines network lifetime, throughput, as well as the localization accuracy. In this paper, we establish an optimization framework for power allocation in cooperative localization networks. We first show that the optimal solution for the power allocation problem can be obtained by semi-definite programs (SDPs). For implementation in cooperative localization networks, we develop efficient and distributed power allocation strategies via relaxation of the original problem. In particular, we decompose the power allocation problem into infrastructure and cooperation parts. We transform the former into SDPs and derive upper bounds for the localization accuracy of the latter. These bounds enable us to develop efficient distributed strategies. Simulation results show that these strategies can achieve significant performance improvement compared to the unoptimized strategies in terms of localization accuracy.

A fundamental theoretical problem in opportunistic spectrum access is the following: a single secondary user must choose a channel to sense and access at each time, with the availability of each channel (due to primary user behavior) described by a Markov Chain. The problem of maximizing the expected channel usage can be formulated as a restless multi-armed bandit. We present in this paper an online learning algorithm with the best known results to date for this problem in the case when channels are homogeneous and the channel statistics are unknown a priori. Specifically, we show that this policy, that we refer to as CSE, achieves a regret (the gap between the rewards accumulated by a model-aware Genie and the policy) that is bounded in finite time by a function that scales as O(log t). By explicitly learning the underlying statistics over time, this novel policy outperforms a previously proposed scheme shown to provide near-logarithmic regret.

Reliable and accurate localization of mobile objects is essential for many applications in wireless networks. In range-based localization, the position of the object can be inferred using the distance measurements from wireless signals exchanged with active objects or reflected by passive ones. Power allocation for ranging signals is important since it affects not only network lifetime and throughput but also localization accuracy. In this paper, we establish a unifying optimization framework for power allocation in both active and passive localization networks. In particular, we first determine the functional properties of the localization accuracy metric, which enable us to transform the power allocation problems into second-order cone programs (SOCPs). We then propose the robust counterparts of the problems in the presence of parameter uncertainty and develop asymptotically optimal and efficient near-optimal SOCP-based algorithms. Our simulation results validate the efficiency and robustness of the proposed algorithms .

High-accuracy localization is essential for many location-based applications. The position of an object can be obtained from range measurements based on wireless transmissions. Transmitting power allocation not only affects network lifetime and throughput, but also determines localization accuracy. The number of active transmitting nodes is also crucial since it is related to communication load and computation complexity. In this paper, we formulate the power optimization problem that provides the best localization accuracy under power constraints. We first prove the sparsity of the optimal power allocation, i.e., the optimal localization accuracy can be achieved by activating no more than (J) transmitting nodes in d-dimensional networks. Inspired by such sparsity, we derive the expressions of the optimal power allocation in 2-D networks. We also put forth a near-optimal algorithm for the power allocation problem with individual power constraints. Our results provide a theoretical basis for designing transmitting node selection and power allocation algorithms for network localization.

Power resource allocation is an important task for active and passive network localization, since it affects the localization accuracy in addition to the lifetime and throughput of the network. In this paper, we propose a robust power allocation formulation that guarantees the localization accuracy in the presence of parameter uncertainty for network localization. We first consider wireless network localization (WNL) as an example of active localization, and derive sequential upper and lower bounds for the worst-case localization accuracy as well as their convergence rates. Built on these bounds, we transform the robust formulation into a sequence of second-order cone programs (SOCPs) that yield asymptotically optimal solutions. We also develop an efficient near-optimal SOCP-based algorithm using a relaxation method. Then, we extend all the results to passive localization through the example of radar network localization (RNL). Finally, the simulation results validate the efficiency and robustness of the proposed algorithms.

High-accuracy localization is crucial for numerous location-based applications. In wireless networks, the position information of a node (agent) can be obtained from range measurements with respect to nodes with known positions (anchors). The transmission power allocation among anchors not only affects network lifetime and throughput, but also determines the localization accuracy. In this paper, we formulate the power optimization problem that provides the best location accuracy under a total power constraint. For a given set of anchors, the minimum number of active anchors required for optimal localization in 2-D network is proven to be either two or three, depending on the network parameters. We then derive the closed-form expression for the optimal power allocation in the case of small networks and extend the results to general cases. We also develop a near-optimal strategy that requires less computational complexity, incurring negligible average performance loss. Our results provide a theoretical basis for designing anchor selection and power allocation algorithms for localization.

Power resource allocation is crucial for localization since it affects not only the conventionally recognized lifetime, throughput, and covertness, but also localization efficiency of the network. In this paper, we present an optimization framework for range-based localization to improve the power efficiency and localization accuracy. Our framework unifies the analysis for active and passive localization through the examples of wireless network localization (WNL) and multiple radar localization (MRL). In particular, we determine the functional properties of localization accuracy metric, and based on those properties we formulate the power allocation problem as conic programs. Moreover, we propose robust counterparts that retain the conic structures for power allocation in the presence of parameter uncertainty. Our simulation results validate the efficiency and robustness of the proposed methods.

The problem of opportunistic spectrum access in cognitive radio networks has been recently formulated as a non-Bayesian restless multi-armed bandit problem. In this problem, there are N arms (corresponding to channels) and one player (corresponding to a secondary user). The state of each arm evolves as a finite-state Markov chain with unknown parameters. At each time slot, the player can select K <; N arms to play and receives state-dependent rewards (corresponding to the throughput obtained given the activity of primary users). The objective is to maximize the expected total rewards (i.e., total throughput) obtained over multiple plays. The performance of an algorithm for such a multi-armed bandit problem is measured in terms of regret, defined as the difference in expected reward compared to a model-aware genie who always plays the best K arms. In this paper, we propose a new continuous exploration and exploitation (CEE) algorithm for this problem. When no information is available about the dynamics of the arms, CEE is the first algorithm to guarantee near-logarithmic regret uniformly over time. When some bounds corresponding to the stationary state distributions and the state-dependent rewards are known, we show that CEE can be easily modified to achieve logarithmic regret over time. In contrast, prior algorithms require additional information concerning bounds on the second eigenvalues of the transition matrices in order to guarantee logarithmic regret. Finally, we show through numerical simulations that CEE is more efficient than prior algorithms.

In the classic Bayesian restless multi-armed bandit (RMAB) problem, there are N arms, with rewards on all arms evolving at each time as Markov chains with known parameters. A player seeks to activate K ≥ 1 arms at each time in order to maximize the expected total reward obtained over multiple plays. RMAB is a challenging problem that is known to be PSPACE-hard in general. We consider in this work the even harder non-Bayesian RMAB, in which the parameters of the Markov chain are assumed to be unknown a priori. We develop an original approach to this problem that is applicable when the corresponding Bayesian problem has the structure that, de pending on the known parameter values, the optimal solution is one of a prescribed finite set of policies. In such settings, we propose to learn the optimal policy for the non-Bayesian RMAB by employing a suitable meta-policy which treats each policy from this finite set as an arm in a different non-Bayesian multi-armed bandit problem for which a single-arm selection policy is optimal. We demonstrate this approach by developing a novel sensing policy for opportunistic spectrum access over unknown dynamic channels. We prove that our policy achieves near-logarithmic regret (the difference in expected reward compared to a model-aware genie), which leads to the same average reward that can be achieved by the optimal policy under a known model. This is the first such result in the literature for a non Bayesian RMAB.