Position and Orientation Estimation through Millimeter Wave MIMO in 5G Systems

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 Cram\'{e}r-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 line-of- sight, non-line-of-sight, or obstructed-line-of-sight conditions.Comment: The manuscript has been revised, and increased from 27 to 31 pages. Also, Fig.2, Fig. 10 and Table I are adde

Novel Solution for Multi-connectivity 5G-mmW Positioning

\ua9 2018 IEEE. The forthcoming fifth generation (5G) systems with high beamforming gain antenna units, millimeter-wave (mmWave) frequency bands together with massive Multiple Input Multiple Output (MIMO) techniques are key components for accurate positioning methods. In this paper, we propose the positioning technique that is relying on the sparsity in the MIMO-OFDM channel in time and spatial domains, together with effective beamforming methods. We will study the proposed solution in a multi-connectivity context, which has been considered so far for the purpose of improving the user equipment (UE) communication data rate. We utilize the multi-connectivity for positioning, in order to improve robustness to measurement errors and increase positioning service continuity. In particular, we show that when a UE that has connectivity to more base stations, the total power and delay needed for positioning can be reduced

Multi-Array 5G V2V Relative Positioning: Performance Bounds

We study the performance bounds of vehicle-to-vehicle (V2V) relative positioning for vehicles with multiple antenna arrays. The Cram\'{e}r-Rao bound for the estimation of the relative position and the orientation of the Tx vehicle is derived, when angle of arrival (AOA) measurements with or without time-difference of arrival (TDOA) measurements are used. In addition, geometrically intuitive expressions for the corresponding Fisher information are provided. The derived bounds are numerically evaluated for different carrier frequencies, bandwidths and array configurations under different V2V scenarios, i.e. overtaking and platooning. The significance of the AOA and TDOA measurements for position estimation is investigated. The achievable positioning accuracy is then compared with the present requirements of the 3rd Generation Partnership Project (3GPP) 5G New Radio (NR) vehicle-to-everything (V2X) standardization

Misspecified Cram\'er-Rao Bound of RIS-aided Localization under Geometry Mismatch

In 5G/6G wireless systems, reconfigurable intelligent surfaces (RIS) can play a role as a passive anchor to enable and enhance localization in various scenarios. However, most existing RIS-aided localization works assume that the geometry of the RIS is perfectly known, which is not realistic in practice due to calibration errors. In this work, we derive the misspecified Cram\'er-Rao bound (MCRB) for a single-input-single-output RIS-aided localization system with RIS geometry mismatch. Specifically, unlike most existing works that use numerical methods, we propose a closed-form solution to the pseudo-true parameter determination problem for MCRB analysis. Simulation results demonstrate the validity of the derived pseudo-true parameters and MCRB, and show that the RIS geometry mismatch causes performance saturation in the high signal-to-noise ratio regions

Localization Error Bounds for 5G mmWave Systems under I/Q Imbalance

Location awareness is expected to play a significant role in 5G millimeter-wave (mmWave) communication systems. One of the basic elements of these systems is quadrature amplitude modulation (QAM), which has in-phase and quadrature (I/Q) modulators. It is not uncommon for transceiver hardware to exhibit an imbalance in the I/Q components, causing degradation in data rate and signal quality. Under an amplitude and phase imbalance model at both the transmitter and receiver, 2D positioning performance in 5G mmWave systems is considered. Towards that, we derive the position and orientation error bounds and study the effects of the I/Q imbalance parameters on the derived bounds. The numerical results reveal that I/Q imbalance impacts the performance similarly, whether it occurs at the transmitter or the receiver, and can cause a degradation up to 12% in position and orientation estimation accuracy

Performance Analysis of Hybrid 5G-GNSS Localization

\ua9 2018 IEEE. We consider a novel positioning solution combining millimeter wave (mmW) 5G and Global Navigation Satellite System (GNSS) technologies. The study is carried out theoretically by deriving the Fisher Information Matrix (FIM) of a combined 5G-GNSS positioning system and, subsequently, the position, rotation and clock-bias error lower bounds. We pursue a two-step approach, namely, computing first the FIM for the channel parameters, and then transforming it into the FIM of the position, rotation and clock-bias. The analysis shows advantages of the hybrid positioning in terms of i) localization accuracy, ii) coverage, iii) precise rotation estimation and iv) clock-error estimation. In other words, we demonstrate that a tight coupling of the two technologies can provide mutual benefits

5G multi-BS positioning with a single-antenna receiver

Cellular localization generally relies on timedifference-of-arrival (TDOA) measurements. In this paper, we investigate a novel scenario where the mobile user estimates its own position by jointly exploiting TDOA and angle of departure (AOD) measurements, which are estimated from downlink transmissions in a millimeter-wave (mmWave) multiple-input singleoutput (MISO) setup. We first perform a Fisher information analysis to derive the lower bounds on the estimation accuracy, and then propose a novel localization algorithm, which is able to provide improved performance also with few transmit antennas and limited bandwidth