Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Abstract

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location of the user terminals are largely undiscovered. In this paper, we propose a novel support detection-based channel training method for frequency selective millimeter-wave (mm-wave) multiple-input-multiple-output system with lens antenna arrays. We show that accurate position and orientation estimation and tracking is possible using signals from a single transmitter with lens antenna arrays. Particularly, the beamspace channel estimation is formulated as two sparse signal recovery problems in the downlink and uplink for the estimation of angle-of-arrival, angle-of-departure, and timeof-arrival. The proposed method offers a higher sparse detection probability compared to the compressed sensing based solutions. Finally, a joint heuristic beamformer design and user position and orientation tracking approach are proposed based on initial estimation of channel parameters obtained in the training phase.

A Review of Advanced Localization Techniques for Crowdsensing Wireless Sensor Networks

“…The simulation analysis highlights that the proposed approach can achieve cm-level position accuracy even in absence of a LOS path. The problem of tracking position and orientation through mmWave MIMO systems has been recently addressed in [152]. In this work, authors derive a channel training method based on the availability of lens antenna arrays.…”

Localization and Throughput Trade-Off in a Multi-User Multi-Carrier mm-Wave System

“…With the generalized localization error formulation in equation 10, we can analyze the effect of each estimation parameter independently onto beamforming by adjusting the weights, which can depend on a priori application requirements. We can however notice that there are two different variables X and X τ in the formulation of the localization error, in equation (10). In order to maintain one unique optimization variable in the equation, we can restructure the latter as follows.…”

“…However, effective beamforming calls for even better knowledge of the propagation channel than within omnidirectional transmissions. In the literature, mm-Wave channel estimation is usually split into two phases: i) Beam training, typically based on spatial beam sweeping [6], [7] and ii) Channel parameters estimation (in both single-and multi-path scenarios), relying for instance on optimization and compressive sensing techniques [8]- [10]. The objective of the latter is to exploit the sparse geometric structure of the mm-Wave channel to estimate the angle of departure (AoD), the angle of arrival (AoA) and the complex channel coefficients of a few well-separated multi-path components.…”

“…With the new parameters, the localization error formulated in Appendix C becomes a special case of the formulation in equation(10) when β τ = k τ,u , β θ = k θ,u and β φ = k φ,u . It must also be noted that the CRLB of the estimation variables are interrelated, as explained in[16].…”

Simultaneous Localization and Mapping in Millimeter Wave Networks with Angle Measurements

“…With regards to tracking the location dependent variables of both line-of-sight (LOS) and non line of sight (NLOS) components, the authors in [8] and [9] present an estimator, exploiting the mm-Wave channel sparsity, relying on simultaneous orthogonal matching pursuit (SOMP) and support detection (SD) algorithms. Similarly, the authors in [10] and [11] present an algorithm for simultaneous localization and mapping (SLAM) based on the multiple location estimates of the user and the scatterers at different time instances.…”