In harsh environments where geographic positioning fails, communication between wireless nodes can be used to improve the accuracy of location information.

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cramér-Rao bound (CRB) on position and rotation angle estimation uncertainty from millimeter wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either lineof-sight, non-line-of-sight, or obstructed-line-of-sight conditions.

Abstract-Sensor networks can benefit greatly from locationawareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors.

Abstract-The availability of positional information is of great importance in many commercial, governmental, and military applications. Localization is commonly accomplished through the use of radio communication between mobile devices (agents) and fixed infrastructure (anchors). However, precise determination of agent positions is a challenging task, especially in harsh environments due to radio blockage or limited anchor deployment. In these situations, cooperation among agents can significantly improve localization accuracy and reduce localization outage probabilities. A general framework of analyzing the fundamental limits of wideband localization has been developed in Part I of the paper. Here, we build on this framework and establish the fundamental limits of wideband cooperative location-aware networks. Our analysis is based on the waveforms received at the nodes, in conjunction with Fisher information inequality. We provide a geometrical interpretation of equivalent Fisher information for cooperative networks. This approach allows us to succinctly derive fundamental performance limits and their scaling behaviors, and to treat anchors and agents in a unified way from the perspective of localization accuracy. Our results yield important insights into how and when cooperation is beneficial.Index Terms-Cooperative localization, Cramér-Rao bound (CRB), equivalent Fisher information (EFI), information inequality, ranging information (RI), squared position error bound (SPEB).

AbstrAct5G technologies present a new paradigm to provide connectivity to vehicles, in support of high data-rate services, complementing existing inter-vehicle communication standards based on IEEE 802.11p. As we argue, the specific signal characteristics of 5G communication turn out to be highly conducive for vehicle positioning. Hence, 5G can work in synergy with existing on-vehicle positioning and mapping systems to provide redundancy for certain applications, in particular automated driving. This article provides an overview of the evolution of cellular positioning and discusses the key properties of 5G as they relate to vehicular positioning. Open research challenges are presented. requirements for VehiculAr PositioningWith the increase of automated driving in various forms (highway assistance driving, automatic cruise control, self-parking, up to fully autonomous driving) comes a need for precise positioning information. Positioning of vehicles is achieved through a variety of technologies, as illustrated in Fig. 1, including global navigation satellite-based systems (GNSS), radar, mono and stereo cameras, and laser scanners (lidar), which are fused to give the vehicle an understanding of the environment and its location within this environment. The environment is encoded through a map, which is either stored offline or computed online. The process of learning the environment and building detailed maps using onboard sensors is known as mapping. Different positioning applications have different requirements, which are expressed in terms of accuracy, latency, reliability, and cost. On one hand, standard vehicular navigation applications require only a few meters of absolute positioning accuracy, second-level latency, and low reliability (frequent outages are tolerable), but must rely on low-cost sensors. On the other hand, the safety-critical application of autonomous driving will require centimeter-level absolute and relative positioning accuracy, latencies on the order of tens of milliseconds, and high reliability, but can rely on a more expensive suite of sensors. An overview of the accuracy requirements for several key applications is shown in Table 1.GNSS, which has been the workhorse for vehicular absolute positioning in military, professional, and personal navigation, leads to uncertainties on the order of a few meters. Complemented by dedicated base stations, real-time kinematic GNSS further improves the accuracy down to the centimeter level. However, GNSS fails to work in certain common conditions, such as under tree canopies, in the presence of GNSS jammers, and in dense urban environments, due to the blocking of GNSS signals by buildings. Moreover, GNSS is limited by significant latency and low refresh rate, which are key requirements for guaranteeing safety.For relative positioning, onboard sensors such as cameras, radars, and lidars can generally operate well under these GNSS-challenged conditions, and provide very precise information. However, these sensors are costly in terms of computational ...

Location-awareness is becoming increasingly important in wireless networks. Indoor localization can be enabled through wideband or ultra-wide bandwidth (UWB) transmission, due to its fine delay resolution and obstacle-penetration capabilities. A major hurdle is the presence of obstacles that block the line-of-sight (LOS) path between devices, affecting ranging performance and, in turn, localization accuracy. Many techniques have been proposed to address this issue, most of which make modifications to the localization algorithm. Since many localization algorithms work with distance or angle estimates, rather than received waveforms, information inherent in the wideband waveform is lost, leading to sub-optimal ranging error mitigation. To avoid this information loss, we present a novel approach to mitigate ranging errors directly in the physical layer. In contrast to existing techniques, which detect the non-line-of-sight (NLOS) condition, our approach directly mitigates the bias incurred in both LOS and non-LOS conditions. In particular, we apply two classes of non-parametric regressors to form an estimate of the ranging error. Our work is based on, and validated by, an extensive indoor measurement campaign with FCC-compliant UWB radios. The results show that the proposed regressors provide significant performance improvements in various practical localization scenarios, compared to conventional approaches.

5G networks will be the first generation to benefit from location information that is sufficiently precise to be leveraged in wireless network design and optimization. We argue that location information can aid in addressing several of the key challenges in 5G, complementary to existing and planned technological developments. These challenges include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. This paper gives a broad overview of the growing research area of location-aware communications across different layers of the protocol stack. We highlight several promising trends, tradeoffs, and pitfalls. I. 5G: INTRODUCTION AND CHALLENGES 5 G will be characterized by a wide variety of use cases, as well as orders-of-magnitude increases in mobile data volume per area, number of connected devices, and typical user data rate, all compared to current mobile communication systems [1]. To cope with these demands, a number of challenges must be addressed before 5G can be successfully deployed. These include the demand for extremely high data rates and much lower latencies, potentially down to 1 ms end-to-end for certain applications [2]. Moreover, scalability and reduction of signaling overhead must be accounted for, as well as minimization of (total) energy consumption to enable affordable cost for network operation. To fulfill these requirements in 5G, network densification is key, calling for a variety of coordination and cooperation techniques between various kinds of network elements in an ultra-dense heterogeneous network. Moreover, by implementing sharing and coexistence approaches, along with new multi-GHz frequency bands, spectrum efficiency can be improved. An overview of a number of disruptive technologies for 5G is provided in [1]. It is our vision that context information in general, and location information in particular, can complement both traditional and disruptive technologies in addressing several of the challenges in 5G networks. While location information was available in previous generations of cellular mobile radio systems, e.g., cell-ID positioning in 2G, timing-based positioning using communication-relevant synchronization signals in 3G, and additionally dedicated positioning reference signals in 4G, accuracy ranged from hundreds to tens of meters, rendering position information insufficiently precise for some communications operations. In 5G, for the first time, a majority of

Location-aware communication systems are expected to play a pivotal part in the next generation of mobile communication networks. Therefore, there is a need to understand the localization limits in these networks, particularly, using millimeter-wave technology (mmWave). Towards that, we address the uplink and downlink localization limits in terms of 3D position and orientation error bounds for mmWave multipath channels. We also carry out a detailed analysis of the dependence of the bounds on different system parameters. Our key findings indicate that the uplink and downlink behave differently in two distinct ways. First of all, the error bounds have different scaling factors with respect to the number of antennas in the uplink and downlink. Secondly, uplink localization is sensitive to the orientation angle of the user equipment (UE), whereas downlink is not. Moreover, in the considered outdoor scenarios, the non-line-of-sight paths generally improve localization when a line-of-sight path exists. Finally, our numerical results show that mmWave systems are capable of localizing a UE with sub-meter position error, and sub-degree orientation error. communications [7], assisted living applications [8], or to support the communication robustness and effectiveness in different aspects such as resource allocation [9], beamforming [10], [11], and pilot assignment [12]. Therefore, the study of positioning in 5G mmWave systems becomes specially imperative. Due to the use of directional beamforming in mmWave, in addition to the UE position also the UE orientation plays an important role in location-aided systems.Conventionally position information is obtained by GPS, though this has several limitations.Most importantly, GPS suffers from degraded performance in outdoor rich-scattering scenarios and urban canyons, and may fail to provide a position fix for indoor scenarios. Even in good conditions, GPS positioning accuracy ranges between 1-5 meters. To address these limitations, there has been intense research on competing radio-based localization technologies. To understand the fundamental behavior of any technology, the Cramér-Rao lower bound (CRLB)[13] or related bounds can be used. The CRLB provides a lower bound on the variance of an unbiased estimator of a certain parameter. The square-root of the CRLB of the position and the orientation are termed the position error bound (PEB), and the orientation error bound (OEB), respectively. PEB and OEB can be computed indirectly by transforming the bounds of the channel parameters, namely: directions of arrival (DOA), directions of departure (DOD), and time of arrival (TOA). For conventional MIMO systems, the bounds of the 2D channel parameters are derived in [14], based on received digital signals and uniform linear arrays (ULA), while bounds are derived in [15] based on 3D channel matrix with no transmit beamforming. It was found that having more transmit and receive antennas is beneficial for estimating the DOA and DOD. In both [14], [15] beamforming was not considered. The b...