Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Opportunistic timing signals for pervasive mobile localization

Mención Internacional en el título de doctorThe proliferation of handheld devices and the pressing need of location-based services call for precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices. Despite the large investments and efforts in academic and industrial communities, a pin-point solution is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban areas and indoor environments, where the visibility of GNSS satellites is reduced drastically. In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive positioning system should still rely on GNSS, yet complemented with technologies that can guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals are made of data with timing information. We then investigate solutions where opportunistic timing signals can be extracted out of terrestrial technologies. These signals can then be used as additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system that combines range measurements from the Global Positioning System (GPS), the world’s most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques have been recently suggested for ranging mobile devices overWiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes the method we propose as the first contribution of the present work particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments, and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters. The next step we envisioned towards preparing theoretical and practical basis for the aforementioned hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF ranges, and initial foundations of single-technology self-localization. Self-localization systems based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance therefore heavily rely on the design and parametrization of robust algorithms. We study the noise sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning algorithms at a mobile node using those ranges. Our results show that the localization error varies greatly depending on the ranging technology, algorithm selection, and appropriate tuning of the algorithms. We characterize the localization error using real-world measurements and different parameter settings to provide guidance for the design of robust location estimators in realistic settings. These tools and foundations are necessary to tackle the problem of hybrid positioning system providing high localization capabilities across indoor and outdoor environments. In this context, the lack of a single positioning system that is able the fulfill the specific requirements of diverse indoor and outdoor applications settings has led the development of a multitude of localization technologies. Existing mobile devices such as smartphones therefore commonly rely on a multi-RAT (Radio Access Technology) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is foreseen to provide the highest accuracy in the particular context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies and improve the overall localization accuracy in different contexts. We introduce an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a rich set of multiple ranges can be available across different RATs, the intelligent selection of the subset of ranges with accurate timing information is critical to achieve the best positioning accuracy. We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging measurements. We also address practical problems of the design space, such as removal of WiFi chipset and environmental calibration to make the positioning system as autonomous as possible. Experimental results show that our solution considerably outperforms the use of monolithic technologies and methods based on classical fault detection and identification typically applied in standalone GPS technology. All the contributions and research questions described previously in localization and positioning related topics suppose full knowledge of the anchors positions. In the last part of this work, we study the problem of deriving proximity metrics without any prior knowledge of the positions of the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and respective received signal strength indicator (RSSI) values. Applications that benefit from proximity metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors, we show that metrics from related work perform poorly on real-world data. We analyze the cause for this poor performance, and show that imperfect observations of APs with commodity WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide such proximity estimates, without requiring knowledge of location for the observed AP. We address the challenge of imperfect observations of APs in the design of these improved metrics. Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints. We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en Ingeniería TelemáticaPresidente: Francisco Barceló Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior

Positioning by multicell fingerprinting in urban NB-IoT networks

Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments

Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)