Joint Localization and Mapping through Millimeter Wave MIMO in 5G Systems - Extended Version

Millimeter wave signals with multiple transmit and receive antennas are considered as enabling technology for enhanced mobile broadband services in 5G systems. While this combination is mainly associated with achieving high data rates, it also offers huge potential for radio-based positioning. Recent studies showed that millimeter wave systems with multiple transmit and receive antennas are capable of jointly estimating the position and orientation of a mobile terminal while mapping the radio environment simultaneously. To this end, we present a message passing-based estimator which jointly estimates the position and orientation of the mobile terminal, as well as the location of reflectors or scatterers. We provide numerical examples showing that this estimator can provide considerably higher estimation accuracy compared to a state-of-the-art estimator. Our examples demonstrate that our message passing-based estimator neither requires the presence of a line-of-sight path nor prior knowledge regarding any of the parameters to be estimated

Joint Localization and Mapping through Millimeter Wave MIMO in 5G Systems

Millimeter wave signals with multiple transmit and receive antennas are considered as enabling technology for enhanced mobile broadband services in 5G systems. While this combination is mainly associated with achieving high data rates, it also offers huge potential for radio-based positioning. Recent studies showed that millimeter wave signals with multiple transmit and receive antennas are capable of jointly estimating the position and orientation of a mobile terminal while mapping the radio environment simultaneously. To this end, we present a message passing-based estimator which jointly estimates the position and orientation of the mobile terminal, as well as the location of reflectors or scatterers in the absence of the line-of-sight path. We provide numerical examples showing that our estimator can provide considerably higher estimation accuracy compared to a state-of-the-art estimator. Our examples demonstrate that our message passing-based estimator neither requires the presence of a line-of-sight path nor prior knowledge regarding any of the parameters to be estimated

Joint CKF-PHD Filter and Map Fusion for 5G Multi-cell SLAM

5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to promote the overall performance of SLAM. In 5G multi-cell SLAM, there are challenges such as the unknown number of targets, uncertainty regarding the association between the targets and the measurements, unknown types of targets, as well as map management among BSs. To address those challenges, we propose a new method for 5G multi-cell SLAM which comprises a joint cubature Kalman filter and multi-model probability hypothesis density, and a map fusion routine. Simulation results demonstrate that the proposed method solves the aforementioned challenges and also improves vehicle state and map estimates

5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method.Comment: This work has been accepted in the IEEE Transactions on Wireless Communication

A survey on 5G massive MIMO Localization

Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method

Millimeter wave picocellular networks: capacity analysis and system design

The explosive growth in demand for wireless mobile data, driven by the proliferationof ever more sophisticated handhelds creating and consuming rich multimedia, calls fororders of magnitude increase in the capacity of cellular data networks. Millimeter wavecommunication from picocellular base stations to mobile devices is a particularly promisingapproach for meeting this challenge because of two reasons. First, there is a largeamount of available spectrum, enabling channel bandwidths of the order of Gigahertz(GHz) which are 1-2 orders of magnitude higher than those in existing WiFi and cellularsystems at lower carrier frequencies. Second, the small carrier wavelength enables therealization of highly directive steerable arrays with a large number of antenna elements,in compact form factors, thus significantly enhancing spatial reuse. Hence, we propose toemploy the 60 GHz unlicensed band for basestation to mobile communication in outdoorpicocells.We first investigate the basic feasibility of such networks, showing that 60GHz linksare indeed viable for outdoor applications. For this purpose, we provided link budgetcalculations along with preliminary simulations which show that despite the commonconcerns about higher oxygen absorption and sensitivity to movement and blockage,picocloud architecture provides availability rate of more than 99%.Next, we explore the idea of increasing spatial reuse by shrinking picocells hopingthat interference is no longer the bottleneck given the highly directive antenna arrays atthis band. Our goal is to estimate the achievable capacity for small picocells along an urban canyon. We consider basestations with multiple faces or sectors, each with one or more antenna arrays. Each such array, termed subarray can employ Radio Frequency(RF) beamforming to communicate with one mobile user at a time. We first focus oncharacterization and modeling the inter-cell interference for one subarray on each face.Our analysis provides a strong indication of very large capacity (in the order of Tbps/km)with a few GHz of bandwidth.Following this, we explore the impact of adding multiple subarrays per face. This leadsto intra-cell interference as well as additional inter-cell interference. While the effect ofadditional inter-cell interference can be quantified within our previous framework, intracellinterference has inherently different features that call for new approaches for analysisand design. We propose a cross-layer approach to suppress the intra-cell interference intwo stages: (a) Physical layer (PHY-layer) method which mitigates interference by jointprecoding and power adaptation and (b) Medium Access Control layer (MAC-layer)method which manages the residual interference by optimizing resource allocation. Wethen estimate the capacity gain over conventional LTE cellular networks and establishthat 1000-fold capacity increase is indeed feasible via mm-wave picocellular networks.Lastly, we examine fundamental signal processing challenges associated with channelestimation and tracking for large arrays, placed within the context of system designfor a mm-wave picocellular network. Maintainance of highly directive links in the faceof blockage and mobility requires accurate estimation of the spatial channels betweenbasestation and mobile users. Here we develop the analytical framework for compressivechannel estimation and tracking. We also address the system level design discussinglink budget, overhead, and inter-cell beacon interference. Simulation results demonstratethat our compressive scheme is able to resolve mm-wave spatial channels with a relativelysmall number of compressive measurements