Soft-connected Rigid Body Localization: State-of-the-Art and Research Directions for 6G

This white paper describes a proposed article that will aim to provide a thorough study of the evolution of the typical paradigm of wireless localization (WL), which is based on a single point model of each target, towards wireless rigid body localization (W-RBL). We also look beyond the concept of RBL itself, whereby each target is modeled as an independent multi-point three-dimensional (3D), with shape enforced via a set of conformation constraints, as a step towards a more general approach we refer to as soft-connected RBL, whereby an ensemble of several objects embedded in a given environment, is modeled as a set of soft-connected 3D objects, with rigid and soft conformation constraints enforced within each object and among them, respectively. A first intended contribution of the full version of this article is a compact but comprehensive survey on mechanisms to evolve WL algorithms in W-RBL schemes, considering their peculiarities in terms of the type of information, mathematical approach, and features the build on or offer. A subsequent contribution is a discussion of mechanisms to extend W-RBL techniques to soft-connected rigid body localization (SCW-RBL) algorithms

Delay-Accuracy Tradeoff in Opportunistic Time-of-Arrival Localization

While designing a positioning network, the localization performance is traditionally the main concern. However, collection of measurements together with channel access methods require a nonzero time, causing a delay experienced by network nodes. This fact is usually neglected in the positioning-related literature. In terms of the delay-accuracy tradeoff, broadcast schemes have an advantage over unicast, provided nodes can be properly synchronized. In this letter, we analyze the delay-accuracy tradeoff for localization schemes in which the position estimates are obtained based on broadcasted ranging signals. We find that for dense networks, the tradeoff is the same for cooperative and noncooperative networks, and cannot exceed a certain threshold value

Collaborative Sensor Network Localization: Algorithms and Practical Issues

Emerging communication network applications including fifth-generation (5G) cellular and the Internet-of-Things (IoT) will almost certainly require location information at as many network nodes as possible. Given the energy requirements and lack of indoor coverage of Global Positioning System (GPS), collaborative localization appears to be a powerful tool for such networks. In this paper, we survey the state of the art in collaborative localization with an eye toward 5G cellular and IoT applications. In particular, we discuss theoretical limits, algorithms, and practical challenges associated with collaborative localization based on range-based as well as range-angle-based techniques

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Decentralized Scheduling for Cooperative Localization With Deep Reinforcement Learning

Cooperative localization is a promising solution to the vehicular high-accuracy localization problem. Despite its high potential, exhaustive measurement and information exchange between all adjacent vehicles are expensive and impractical for applications with limited resources. Greedy policies or hand-engineering heuristics may not be able to meet the requirement of complicated use cases. In this paper, we formulate a scheduling problem to improve the localization accuracy (measured through the Cram\ue9r-Rao lower bound) of every vehicle up to a given threshold using the minimum number of measurements. The problem is cast as a partially observable Markov decision process and solved using decentralized scheduling algorithms with deep reinforcement learning, which allow vehicles to optimize the scheduling (i.e., the instants to execute measurement and information exchange with each adjacent vehicle) in a distributed manner without a central controlling unit. Simulation results show that the proposed algorithms have a significant advantage over random and greedy policies in terms of both required numbers of measurements to localize all nodes and achievable localization precision with limited numbers of measurements

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Communications in Mobile Wireless Networks: A Finite Time-Horizon Viewpoint

In mobile wireless networks (MWNs), short-term communications carry two key features: 1) Different from communications over a large time window where the performance is governed by the long-term average effect, the short-term communications in MWNs are sensitive to the instantaneous location and channel condition caused by node mobility. 2) The short-term communications in MWNs have the finite blocklength coding effect which means it is not amenable to the well-known Shannon's capacity formulation. To deal with the short-term communications in MWNs, this thesis focuses on three main issues: how the node mobility affects the instantaneous interference, how to reduce the uncertainty in the locations of mobile users, and what is the maximal throughput of a multi-user network over a short time-horizon. First, we study interference prediction in MWNs by proposing and using a general-order linear model for node mobility. The proposed mobility model can well approximate node dynamics of practical MWNs. Unlike previous studies on interference statistics, we are able through this model to give a best estimate of the time-varying interference at any time rather than long-term average effects. In particular, we propose a compound Gaussian point process functional (CGPPF) in a general framework to obtain analytical results on the mean value and moment-generating function of the interference prediction. Second, to reduce the uncertainty in nodal locations, the cooperative localization problem for mobile nodes is studied. In contrast to previous works, which highly rely on the synchronized time-slotted systems, this cooperative localization framework we establish does not need any synchronization for the communication links and measurement processes in the entire wireless network. To solve the cooperative localization problem in a distributed manner, we first propose the centralized localization algorithm based on the global information, and use it as the benchmark. Then, we rigorously prove when a localization estimation with partial information has a small performance gap from the one with global information. Finally, by applying this result at each node, the distributed prior-cut algorithm is designed to solve this asynchronous localization problem. Finally, we study the throughput region of any MWN consisting of multiple transmitter-receiver pairs where interference is treated as noise. Unlike the infinite-horizon throughput region, which is simply the convex hull of the throughput region of one time slot, the finite-horizon throughput region is generally non-convex. Instead of directly characterizing all achievable rate-tuples in the finite-horizon throughput region, we propose a metric termed the rate margin, which not only determines whether any given rate-tuple is within the throughput region (i.e., achievable or unachievable), but also tells the amount of scaling that can be done to the given achievable (unachievable) rate-tuple such that the resulting rate-tuple is still within (brought back into) the throughput region. This thesis advances our understanding in communications in MWNs from a finite-time horizon viewpoint. It establishes new frameworks for tracking the instantaneous behaviors, such as interference and nodal location, of MWNs. It also reveals the fundamental limits on short-term communications of a multi-user mobile network, which sheds light on communications with low latency