Fingerprinting-Based Positioning in Distributed Massive MIMO Systems

Location awareness in wireless networks may enable many applications such as emergency services, autonomous driving and geographic routing. Although there are many available positioning techniques, none of them is adapted to work with massive multiple-in-multiple-out (MIMO) systems, which represent a leading 5G technology candidate. In this paper, we discuss possible solutions for positioning of mobile stations using a vector of signals at the base station, equipped with many antennas distributed over deployment area. Our main proposal is to use fingerprinting techniques based on a vector of received signal strengths. This kind of methods are able to work in highly-cluttered multipath environments, and require just one base station, in contrast to standard range-based and angle-based techniques. We also provide a solution for fingerprinting-based positioning based on Gaussian process regression, and discuss main applications and challenges.Comment: Proc. of IEEE 82nd Vehicular Technology Conference (VTC2015-Fall

Indoor wireless communications and applications

Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter

Fingerprint-based localization in massive MIMO systems using machine learning and deep learning methods

À mesure que les réseaux de communication sans fil se développent vers la 5G, une énorme quantité de données sera produite et partagée sur la nouvelle plate-forme qui pourra être utilisée pour promouvoir de nouveaux services. Parmis ceux-ci, les informations de localisation des terminaux mobiles (MT) sont remarquablement utiles. Par exemple, les informations de localisation peuvent être utilisées dans différents cas de services d'enquête et d'information, de services communautaires, de suivi personnel, ainsi que de communications sensibles à la localisation. De nos jours, bien que le système de positionnement global (GPS) des MT offre la possibilité de localiser les MT, ses performances sont médiocres dans les zones urbaines où une ligne de vue directe (LoS) aux satellites est bloqué avec de nombreux immeubles de grande hauteur. En outre, le GPS a une consommation d'énergie élevée. Par conséquent, les techniques de localisation utilisant la télémétrie, qui sont basées sur les informations de signal radio reçues des MT tels que le temps d'arrivée (ToA), l'angle d'arrivée (AoA) et la réception de la force du signal (RSS), ne sont pas en mesure de fournir une localisation de précision satisfaisante. Par conséquent, il est particulièrement difficile de fournir des informations de localisation fiables des MT dans des environnements complexes avec diffusion et propagation par trajets multiples. Les méthodes d'apprentissage automatique basées sur les empreintes digitales (FP) sont largement utilisées pour la localisation dans des zones complexes en raison de leur haute fiabilité, rentabilité et précision et elles sont flexibles pour être utilisées dans de nombreux systèmes. Dans les réseaux 5G, en plus d'accueillir plus d'utilisateurs à des débits de données plus élevés avec une meilleure fiabilité tout en consommant moins d'énergie, une localisation de haute précision est également requise. Pour relever un tel défi, des systèmes massifs à entrées multiples et sorties multiples (MIMO) ont été introduits dans la 5G en tant que technologie puissante et potentielle pour non seulement améliorer l'efficacité spectrale et énergétique à l'aide d'un traitement relativement simple, mais également pour fournir les emplacements précis des MT à l'aide d'un très grand nombre d'antennes associées à des fréquences porteuses élevées. Il existe deux types de MIMO massifs (M-MIMO), soit distribué et colocalisé. Ici, nous visons à utiliser la méthode basée sur les FP dans les systèmes M-MIMO pour fournir un système de localisation précis et fiable dans un réseau sans fil 5G. Nous nous concentrons principalement sur les deux extrêmes du paradigme M-MIMO. Un grand réseau d'antennes colocalisé (c'est-à-dire un MIMO massif colocalisé) et un grand réseau d'antennes géographiquement distribué (c'est-à-dire un MIMO massif distribué). Ensuite, nous ex trayons les caractéristiques du signal et du canal à partir du signal reçu dans les systèmes M-MIMO sous forme d'empreintes digitales et proposons des modèles utilisant les FP basés sur le regroupement et la régression pour estimer l'emplacement des MT. Grâce à cette procédure, nous sommes en mesure d'améliorer les performances de localisation de manière significative et de réduire la complexité de calcul de la méthode basée sur les FP.As wireless communication networks are growing into 5G, an enormous amount of data will be produced and shared on the new platform, which can be employed in promoting new services. Location information of mobile terminals (MTs) is remarkably useful among them, which can be used in different use cases of inquiry and information services, community services, personal tracking, as well as location-aware communications. Nowadays, although the Global Positioning System (GPS) offers the possibility to localize MTs, it has poor performance in urban areas where a direct line-of-sight (LoS) to the satellites is blocked by many tall buildings. Besides, GPS has a high power consumption. Consequently, the ranging based localization techniques, which are based on radio signal information received from MTs such as time-of-arrival (ToA), angle-of-arrival (AoA), and received signal strength (RSS), are not able to provide satisfactory localization accuracy. Therefore, it is a notably challenging problem to provide precise and reliable location information of MTs in complex environments with rich scattering and multipath propagation. Fingerprinting (FP)-based machine learning methods are widely used for localization in complex areas due to their high reliability, cost-efficiency, and accuracy and they are flexible to be used in many systems. In 5G networks, besides accommodating more users at higher data rates with better reliability while consuming less power, high accuracy localization is also required in 5G networks. To meet such a challenge, massive multiple-input multiple-output (MIMO) systems have been introduced in 5G as a powerful and potential technology to not only improve spectral and energy efficiency using relatively simple processing but also provide an accurate locations of MTs using a very large number of antennas combined with high carrier frequencies. There are two types of massive MIMO (M-MIMO), distributed and collocated. Here, we aim to use the FP-based method in M-MIMO systems to provide an accurate and reliable localization system in a 5G wireless network. We mainly focus on the two extremes of the M-MIMO paradigm. A large collocated antenna array (i.e., collocated M-MIMO ) and a large geographically distributed antenna array (i.e., distributed M-MIMO). Then, we extract signal and channel features from the received signal in M-MIMO systems as fingerprints and propose FP-based models using clustering and regression to estimate MT's location. Through this procedure, we are able to improve localization performance significantly and reduce the computational complexity of the FP-based method

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Mobile node-aided localization and tracking in terrestrial and underwater networks

In large-scale wireless sensor networks (WSNs), the position information of individual sensors is very important for many applications. Generally, there are a small number of position-aware nodes, referred to as the anchors. Every other node can estimate its distances to the surrounding anchors, and then employ trilateration or triangulation for self-localization. Such a system is easy to implement, and thus popular for both terrestrial and underwater applications, but it suffers from some major drawbacks. First, the density of the anchors is generally very low due to economical considerations, leading to poor localization accuracy. Secondly, the energy and bandwidth consumptions of such systems are quite significant. Last but not the least, the scalability of a network based on fixed anchors is not good. Therefore, whenever the network expands, more anchors should be deployed to guarantee the required performance. Apart from these general challenges, both terrestrial and underwater networks have their own specific ones. For example, realtime channel parameters are generally required for localization in terrestrial WSNs. For underwater networks, the clock skew between the target sensor and the anchors must be considered. That is to say, time synchronization should be performed together with localization, which makes the problem complicated. An alternative approach is to employ mobile anchors to replace the fixed ones. For terrestrial networks, commercial drones and unmanned aerial vehicles (UAVs) are very good choices, while autonomous underwater vehicles (AUVs) can be used for underwater applications. Mobile anchors can move along a predefined trajectory and broadcast beacon signals. By listening to the messages, the other nodes in the network can localize themselves passively. This architecture has three major advantages: first, energy and bandwidth consumptions can be significantly reduced; secondly, the localization accuracy can be much improved with the increased number of virtual anchors, which can be boosted at negligible cost; thirdly, the coverage can be easily extended, which makes the solution and the network highly scalable. Motivated by this idea, this thesis investigates the mobile node-aided localization and tracking in large-scale WSNs. For both terrestrial and underwater WSNs, the system design, modeling, and performance analyses will be presented for various applications, including: (1) the drone-assisted localization in terrestrial networks; (2) the ToA-based underwater localization and time synchronization; (3) the Doppler-based underwater localization; (4) the underwater target detection and tracking based on the convolutional neural network and the fractional Fourier transform. In these applications, different challenges will present, and we will see how these challenges can be addressed by replacing the fixed anchors with mobile ones. Detailed mathematical models will be presented, and extensive simulation and experimental results will be provided to verify the theoretical results. Also, we will investigate the channel estimation for the fifth generation (5G) wireless communications. A pilot decontamination method will be presented for the massive multiple-input-multiple-output communications, and the data-aided channel tracking will be discussed for millimeter wave communications. We will see that the localization problem is highly coupled with the channel estimation in wireless communications