Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Nowadays, the availability of the location information becomes a key factor in today’s communications systems for allowing location based services. In outdoor scenarios, the Mobile Terminal (MT) position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS or cellular systems resides in the indoor environment and in scenarios with deep shadowing effect where the satellite or cellular signals are broken. In this paper, we will present a review over different technologies and concepts used to improve indoor localization. Additionally, we will discuss different applications based on different localization approaches. Finally, comprehensive challenges in terms of accuracy, cost, complexity, security, scalability, etc. are presente

Game theoretic analysis for MIMO radars with multiple targets

This paper considers a distributed beamforming and resource allocation technique for a radar system in the presence of multiple targets. The primary objective of each radar is to minimize its transmission power while attaining an optimal beamforming strategy and satisfying a certain detection criterion for each of the targets. Therefore, we use convex optimization methods together with noncooperative and partially cooperative game theoretic approaches. Initially, we consider a strategic noncooperative game (SNG), where there is no communication between the various radars of the system. Hence each radar selfishly determines its optimal beamforming and power allocation. Subsequently, we assume a more coordinated game theoretic approach incorporating a pricing mechanism. Introducing a price in the utility function of each radar/player, enforces beamformers to minimize the interference induced to other radars and to increase the social fairness of the system. Furthermore, we formulate a Stackelberg game by adding a surveillance radar to the system model, which will play the role of the leader, and hence the remaining radars will be the followers. The leader applies a pricing policy of interference charged to the followers aiming at maximizing his profit while keeping the incoming interference under a certain threshold. We also present a proof of the existence and uniqueness of the Nash Equilibrium (NE) in both the partially cooperative and noncooperative games. Finally, the simulation results confirm the convergence of the algorithm in all three cases

Collaborative Indoor Positioning Systems: A Systematic Review

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified

Global solution to sensor network localization: A non-convex potential game approach and its distributed implementation

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player non-convex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed to reach the NE and shown to have convergence rate $\mathcal{O}(1/\sqrt{k})$. With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem.Comment: arXiv admin note: text overlap with arXiv:2311.0332

Optimization of positioning capabilities in wireless sensor networks : from power efficiency to medium access

In Wireless Sensor Networks (WSN), the ability of sensor nodes to know its position is an enabler for a wide variety of applications for monitoring, control, and automation. Often, sensor data is meaningful only if its position can be determined. Many WSN are deployed indoors or in areas where Global Navigation Satellite System (GNSS) signal coverage is not available, and thus GNSS positioning cannot be guaranteed. In these scenarios, WSN may be relied upon to achieve a satisfactory degree of positioning accuracy. Typically, batteries power sensor nodes in WSN. These batteries are costly to replace. Therefore, power consumption is an important aspect, being performance and lifetime of WSN strongly relying on the ability to reduce it. It is crucial to design effective strategies to maximize battery lifetime. Optimization of power consumption can be made at different layers. For example, at the physical layer, power control and resource optimization may play an important role, as well as at higher layers through network topology and MAC protocols. The objective of this Thesis is to study the optimization of resources in WSN that are employed for positioning purposes, with the ultimate goal being the minimization of power consumption. We focus on anchor-based positioning, where a subset of the WSN nodes know their location (anchors) and send ranging signals to nodes with unknown position (targets) to assist them in estimating it through distance-related measurements. Two well known of such measurements are received signal strength (RSS) and time of arrival (TOA), in which this Thesis focuses. In order to minimize power consumption while providing a certain quality of positioning service, in this dissertation we research on the problems of power control and node selection. Aiming at a distributed implementation of the proposed techniques, we resort to the tools of non-cooperative game theory. First, transmit power allocation is addressed for RSS based ranging. Using game theory formulation, we develop a potential game leading to an iterated best response algorithm with sure convergence. As a performance metric, we introduce the geometric dilution of precision (GDOP), which is shown to help achieving a suitable geometry of the selected anchor nodes. The proposed scheme and relative distributed algorithms provide good equilibrium performance in both static and dynamic scenarios. Moreover, we present a distributed, low complexity implementation and analyze it in terms of computational complexity. Results show that performance close to that of exhaustive search is possible. We then address the transmit power allocation problem for TOA based ranging, also resorting to a game theoretic formulation. In this setup, and also considering GDOP as performance metric, a supermodular game formulation is proposed, along with a distributed algorithm with guaranteed convergence to a unique solution, based on iterated best response. We analyze the proposed algorithm in terms of the price of anarchy (PoA), that is, compared to a centralized optimum solution, and shown to have a moderate performance loss. Finally, this dissertation addresses the effect of different MAC protocols and topologies in the positioning performance. In this direction, we study the performance of mesh and cluster-tree topologies defined in WSN standards. Different topologies place different constraints in network connectivity, having a substantial impact on the performance of positioning algorithms. While mesh topology allows high connectivity with large energy consumption, cluster-tree topologies are more energy efficient but suffer from reduced connectivity and poor positioning performance. In order to improve the performance of cluster-tree topologies, we propose a cluster formation algorithm. It significantly improves connectivity with anchor nodes, achieving vastly improved positioning performance.En les xarxes de sensors sense fils (WSN), l'habilitat dels nodes sensors per conèixer la seva posició facilita una gran varietat d'aplicacions per la monitorització, el control i l'automatització. Així, les dades que proporciona un sensor tenen sentit només si la posició pot ésser determinada. Moltes WSN són desplegades en interiors o en àrees on la senyal de sistemes globals de navegació per satèl.lit (GNSS) no té prou cobertura, i per tant, el posicionament basat en GNSS no pot ésser garantitzat. En aquests escenaris, les WSN poden proporcionar una bona precisió en posicionament. Normalment, en WSN els nodes són alimentats amb bateries. Aquestes bateries són difícils de reemplaçar. Per tant, el consum de potència és un aspecte important i és crucial dissenyar estratègies efectives per maximitzar el temps de vida de la bateria. L'optimització del consum de potència pot ser fet a diferents capes del protocol. Per exemple, en la capa física, el control de potència i l'optimització dels recursos juguen un rol important, igualment que la topologia de xarxa i els protocols MAC en les capes més altes. L'objectiu d'aquesta tesi és estudiar l¿optimització de recursos en WSN que s'utilitzen per fer posicionament, amb el propòsit de minimitzar el consum de potència. Ens focalitzem en el posicionament basat en àncora, en el qual un conjunt de nodes coneixen la seva localització (nodes àncora) i envien missatges als nodes que no saben la seva posició per ajudar-los a estimar les seves coordenades amb mesures de distància. Dues classes de mesures són la potència de la senyal rebuda (RSS) i el temps d'arribada (TOA) en les quals aquesta tesi està focalitzada. Per minimitzar el consum de potència mentre que es proporciona suficient qualitat en el posicionament, en aquesta tesi estudiem els problemes de control de potència i selecció de nodes. Tenint en compte una implementació distribuïda de les tècniques proposades, utilitzem eïnes de teoria de jocs no cooperatius. Primer, l'assignació de potència transmesa és abordada pel càlcul de la distància amb RSS. Utilitzant la teoria de jocs, desenvolupem un joc potencial que convergeix amb un algoritme iteratiu basat en millor resposta (best response). Com a mètrica d'error, introduïm la dilució de la precisió geomètrica (GDOP) que mostra quant d'apropiada és la geometria dels nodes àncora seleccionats. L'esquema proposat i els algoritmes distribuïts proporcionen una bona resolució de l'equilibri en l'escenari estàtic i dinàmic. Altrament, presentem una implementació distribuïda i analitzem la seva complexitat computacional. Els resultats obtinguts són similars als obtinguts amb un algoritme de cerca exhaustiva. El problema d'assignació de la potència transmesa en el càlcul de la distància basat en TOA, també és tractat amb teoria de jocs. En aquest cas, considerant el GDOP com a mètrica d'error, proposem un joc supermodular juntament amb un algoritme distribuït basat en millor resposta amb convergència garantida cap a una única solució. Analitzem la solució proposada amb el preu de l'anarquia (PoA), és a dir, es compara la nostra solució amb una solució òptima centralitzada mostrant que les pèrdues són moderades. Finalment, aquesta tesi tracta l'efecte que causen diferents protocols MAC i topologies en el posicionament. En aquesta direcció, estudiem les topologies de malla i arbre formant clusters (cluster-tree) que estan definides als estàndards de les WSN. La diferència entre les topologies crea diferents restriccions en la connectivitat de la xarxa, afectant els resultats de posicionament. La topologia de malla permet una elevada connectivitat entre els nodes amb gran consum d'energia, mentre que les topologies d'arbre són més energèticament eficients però amb baixa connectivitat entre els nodes i baix rendiment pel posicionament. Per millorar la qualitat del posicionament en les topologies d'arbre, proposem un algoritme de formació de clústers.Postprint (published version

Game Theoretical Approaches for Wireless Networks

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김성철.In this dissertation, I introduce three algorithms, which are connectivity reconstruction game (CRG), adaptive sector coloring game (ASCG), and asymmetric transmission game (ATG), by mainly using supermodular game and exact potential game with considerations of various objectives (e.g., energy consumption and interference management) in wireless sensor and cellular networks. My main contributions are threefold: 1) connectivity relaxation (energy saving) in wireless localization2) intercell interference coordination in wireless cellular networks3) interference minimization in wireless ad-hoc relay networks. The corresponding explanations are as follows. 1) In geographically dense and energy limited wireless sensor networks, connectivity based localization with full power transmission can be inefficient in terms of energy consumption. In this work, I propose a distributed power control based connectivity reconstruction game, which takes into considerations of both energy efficiency and the quality of localization. The proposed scheme results in a better performance with an improved 61.9% reduction in energy consumption while maintaining the performance of localization at a level similar to the conventional algorithm with full power transmission. 2) Inter-cell interference coordination (ICIC) is a promising technique to improve the performance of frequency-domain packet scheduling (FDPS) in downlink LTE/LTEA networks. However, it is difficult to maximize the performance of FDPS using static ICIC schemes because of insufficient consideration of signal-to-interference-and-noise ratio (SINR) distribution and user fairness. On the other hand, dynamic ICIC schemes based on channel state information (CSI) also have difficulty presented in the excessive signaling overhead and X2 interface latency. In order to overcome these drawbacks, I introduce a new concept of ICIC problem based on geometric network information (GNI) and propose an ASCG as a decentralized solution of the GNI based ICIC problem. Furthermore, I develop an ASCG with a dominant strategy space noted as ASCGD to secure a stable solution through proving the existence of Nash equilibrium (NE). The proposed scheme provides better performance in terms of system throughput gain of up to about 44.1%, and especially of up to about 221% for the worst 10% users than static ICIC schemes. Moreover, the performance of the CSI based ICIC, which require too much computational load and signaling overhead, is only 13.0% and 5.6% higher than that of ASCG-D regarding the total user throughput and the worst 10% user throughput, respectively. The most interesting outcome is that the signaling overhead of ASCG-D is 1/144 of dynamic ICIC schemes one. 3) In this work, I introduce the new concept of temporal diversity utilization based on asymmetric transmission to minimize network interference in wireless ad-hoc networks with a two-hop half-duplex relaying (HDR) protocol. Asymmetric transmission is an interference-aware backoff technique, in which each communication session (source-relay-destination link) adaptively chooses a certain subset of spectrallyorthogonal data streaming which should be delayed by the duration of one time-slot (i.e., half of one subframe). I design the problem in the HDR scenario by applying the concept of asymmetric transmission, and evaluate the game-theoretical algorithm, called ATG, to derive the suboptimal solution. I show that ATG is an exact potential game, and derive its convergence and optimality properties. Furthermore, I develop an approximated version of ATG (termed A-ATG) in order to reduce signaling and computational complexity. Numerical results verify that two algorithms proposed showsignificant synergistic effects when collaborating with the conventional methods in terms of interference coordination. Ultimately, the energy consumption to satisfy the rate requirement is reduced by up to 17:4% compared to the conventional schemes alone.1 INTRODUCTION 1 1.1 Application of Supermodular Game for Connectivity Relaxation in Wireless Localization 2 1.2 Application of Exact Potential Game for Effective Inter-Cell Interference Coordination in Wireless Cellular Networks 3 1.3 Application of Exact Potential Game for Interference Minimization in Wireless Ad-hoc Relay Networks 7 1.4 Dissertation Outline 11 2 APPLICATION OF SUPERMODULAR GAME: Distributed Power Control based Connectivity Reconstruction Game inWireless Localization 13 2.1 Brief Introduction 13 2.2 System Model 13 2.3 Proposed Power Control Algorithm 14 2.3.1 Reliability Function 14 2.3.2 Game Formulation 15 2.3.3 Convergence Properties of CRG 17 2.4 Simulation Results 20 3 APPLICATION OF EXACT POTENTIAL GAME: Adaptive Sector Coloring Game for Geometric Network Information based Inter-Cell Interference Coordination in Wireless Cellular Networks 24 3.1 Brief Introduction 24 3.2 Network Model 26 3.2.1 System Preliminaries 26 3.2.2 Determination of Time Policy 27 3.2.3 Two-Stage Framework of RB Allocation 27 3.3 PROBLEM FORMULATION: Geometric Network Information based ICIC 28 3.3.1 Outline 28 3.3.2 What Is the GNI 28 3.3.3 Temporal Perspective: Why GNI 29 3.3.4 Spatial Perspective: How do I Design a Suitable Utility Function 29 3.3.5 GNI based ICIC Problem 33 3.4 ADAPTIVE SECTOR COLORING GAME 33 3.4.1 Design of ASCG 33 3.4.2 ASCG with a Dominant Strategy Space 35 3.4.3 Summary of System Operation 40 3.5 PERFORMANCE EVALUATION 41 3.5.1 Simulation Settings and Baselines for Comparison 41 3.5.2 SINR Distribution and Average User Throughput 43 3.5.3 Signaling Overhead for ICIC and FDPS 47 3.5.4 Reduction of Feasible ASCG Strategy Space 49 4 APPLICATION OF EXACT POTENTIAL GAME: Asymmetric Transmission Game for Interference Coordination in Wireless Ad-hoc Relay Networks 51 4.1 Brief Introduction 51 4.2 Problem Formulation 52 4.2.1 System Preliminaries 52 4.2.2 The Concept of Asymmetric Transmission for Interference Coordination: A Simple Example 53 4.2.3 Optimization Problem 54 4.3 Asymmetric Transmission Game 55 4.3.1 Game Formulation 55 4.3.2 Convergence and Optimality Properties of Asymmetric Transmission Game 55 4.3.3 Approximated Version of Asymmetric Transmission Game . . 58 4.4 Simulation Results 61 4.4.1 Parameters Settings 61 4.4.2 Network Interference in One-shot Game 62 4.4.3 Individual Power Consumption in One-shot Game 66 4.4.4 Total Energy Consumption in 1000-shot Games 70 4.4.5 Complexity Analysis for Varying K and M 71 5 CONCLUSION 74 Appendix A Derivation of number of partitions for extracting the dominant feasible strategy set 76 Appendix B Derivation of the cardinal number of the dominant feasible strategy set 78 Appendix C Existence of NE in ASCG-D 79 Appendix D The Required Signaling overhead of ASCG-D 82 Bibliography 83 Abstract (In Korean) 93Docto

Mathematical optimization and game theoretic methods for radar networks

Radar systems are undoubtedly included in the hall of the most momentous discoveries of the previous century. Although radars were initially used for ship and aircraft detection, nowadays these systems are used in highly diverse fields, expanding from civil aviation, marine navigation and air-defence to ocean surveillance, meteorology and medicine. Recent advances in signal processing and the constant development of computational capabilities led to radar systems with impressive surveillance and tracking characteristics but on the other hand the continuous growth of distributed networks made them susceptible to multisource interference. This thesis aims at addressing vulnerabilities of modern radar networks and further improving their characteristics through the design of signal processing algorithms and by utilizing convex optimization and game theoretic methods. In particular, the problems of beamforming, power allocation, jammer avoidance and uncertainty within the context of multiple-input multiple-output (MIMO) radar networks are addressed. In order to improve the beamforming performance of phased-array and MIMO radars employing two-dimensional arrays of antennas, a hybrid two-dimensional Phased-MIMO radar with fully overlapped subarrays is proposed. The work considers both adaptive (convex optimization, CAPON beamformer) and non-adaptive (conventional) beamforming techniques. The transmit, receive and overall beampatterns of the Phased-MIMO model are compared with the respective beampatterns of the phased-array and the MIMO schemes, proving that the hybrid model provides superior capabilities in beamforming. By incorporating game theoretic techniques in the radar field, various vulnerabilities and problems can be investigated. Hence, a game theoretic power allocation scheme is proposed and a Nash equilibrium analysis for a multistatic MIMO network is performed. A network of radars is considered, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since no communication between the clusters is assumed, non-cooperative game theoretic techniques and convex optimization methods are utilized to tackle the power adaptation problem. During the proof of the existence and the uniqueness of the solution, which is also presented, important contributions on the SINR performance and the transmission power of the radars have been derived. Game theory can also been applied to mitigate jammer interference in a radar network. Hence, a competitive power allocation problem for a MIMO radar system in the presence of multiple jammers is investigated. The main objective of the radar network is to minimize the total power emitted by the radars while achieving a specific detection criterion for each of the targets-jammers, while the intelligent jammers have the ability to observe the radar transmission power and consequently decide its jamming power to maximize the interference to the radar system. In this context, convex optimization methods, noncooperative game theoretic techniques and hypothesis testing are incorporated to identify the jammers and to determine the optimal power allocation. Furthermore, a proof of the existence and the uniqueness of the solution is presented. Apart from resource allocation applications, game theory can also address distributed beamforming problems. More specifically, a distributed beamforming and power allocation technique for a radar system in the presence of multiple targets is considered. The primary goal of each radar is to minimize its transmission power while attaining an optimal beamforming strategy and satisfying a certain detection criterion for each of the targets. Initially, a strategic noncooperative game (SNG) is used, where there is no communication between the various radars of the system. Subsequently, a more coordinated game theoretic approach incorporating a pricing mechanism is adopted. Furthermore, a Stackelberg game is formulated by adding a surveillance radar to the system model, which will play the role of the leader, and thus the remaining radars will be the followers. For each one of these games, a proof of the existence and uniqueness of the solution is presented. In the aforementioned game theoretic applications, the radars are considered to know the exact radar cross section (RCS) parameters of the targets and thus the exact channel gains of all players, which may not be feasible in a real system. Therefore, in the last part of this thesis, uncertainty regarding the channel gains among the radars and the targets is introduced, which originates from the RCS fluctuations of the targets. Bayesian game theory provides a framework to address such problems of incomplete information. Hence, a Bayesian game is proposed, where each radar egotistically maximizes its SINR, under a predefined power constraint