On the utilization of MIMO-OFDM channel sparsity for accurate positioning

Abstract Recent results have revealed that MIMO channels at high carrier frequencies exhibit sparsity structure, i.e., a few dominant propagation paths. Also channel parameters, namely angular information and propagation delay can be modelled with the physical location of the transmitter, receiver and scatters. In this paper, we leverage these features into the development of a single base-station localization algorithm, and show that the location of an unknown device can be estimated with an accuracy below a meter based on pilot signalling with a OFDM transmission. The method relies on the utilization of the “Adaptive-LASSO” optimization method, in which an ℓ1-based minimization problem is solved by adapting the sparsifying matrix (dictionary) and the sparse vector jointly. Then the location of the device is estimated from the parameters of the sparsifying matrix. Finally, the positioning method is evaluated in different channel setting utilizing a ray-tracing channel model at 28GHz

Comparison of different beamtraining strategies from a rate-positioning trade-off perspective

Abstract In next generation of mobile networks, the 5G, millimeter-wave communication is considered one of the key technologies. It allows high data rate as well as the utilization of large antennas for massive multiple-input-multiple-output (MIMO) and beamforming. However, it is mandatory that transmitter and receiver perform a training of their beams in order to gain all the benefits of a large array gain. In this paper, we study the impact of the beamtraining overhead on the data rate when an exhaustive or hierarchical strategy is used. Also, we show that the beamtraining phase can be used for positioning and, in this regard, we study the trade-off between positioning and data rate

On trade-off between 5G positioning and mmWave communication in a multi-user scenario

Abstract Millimeter-wave communication is considered one of the key enablers for 5G systems as it contributes to achieving high data rate with very wideband transmission, high beamforming gain and massive MIMO techniques. Further, millimeter-wave technology can also be used for the accurate positioning. However, it is yet unclear how communication and positioning systems can share resources to flexibly fulfill data-rate and quality-of-position requirements, especially, in multiuser scenarios. In this regard, the objective of this paper is to investigate and quantify the trade-off between positioning quality and achievable sum-rate as a function of number of receive antennas and transmitter locations in an uplink multi-user scenario

Novel solution for multi-connectivity 5G-mmW positioning

Abstract 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

Performance analysis of hybrid 5G-GNSS localization

Abstract 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

Reliable positioning and mmWave communication via multi-point connectivity

Abstract One of the key elements of future 5G and beyond mobile technology is millimeter-wave (mmWave) communications, which is targeted to extreme high-data rate services. Furthermore, combining the possibility of a wideband signal transmission with the capability of pencil-beamforming, mmWave technology is key for accurate cellular-based positioning. However, it is also well-known that at the mmWave frequency band the radio channel is very sensitive to line-of-sight blockages giving rise to unstable connectivity and inefficient communication. In this paper, we tackle the blockage problem and propose a solution to increase the communication reliability by means of a coordinated multi-point reception. We also investigate the advantage of this solution in terms of positioning quality. More specifically, we describe a robust hybrid analog–digital receive beamforming strategy to combat the unavailability of dominant links. Numerical examples are provided to validate the efficiency of our proposed method

Impact of imperfect beam alignment on the rate-positioning trade-off

Abstract We consider the beam-training procedure in the future 5G millimeter-wave systems and collect position information based on the received signals. We analyze the degradation due to beam misalignment on the achievable rate and on the amount of information available for positioning. We evaluate the performance of two beam-training strategies, namely, exhaustive and hierarchical. Our results reveal new insights on the trade-off between positioning and communication performance

Collaborative positioning mechanism using Bayesian probabilistic models for industry verticals

Abstract In this paper, we develop a collaborative positioning mechanism which uses Bayesian probabilistic models to combine multidimensional sensory data and localize target nodes over the network deployment area. Herein, heterogeneous anchor nodes with distinct radio access technologies and experiencing various radio channel features implement a joint sensor fusion and positioning system for industry verticals. The proposed mechanism also relies on a modern network architecture whereby devices offload high-demand computation to more capable edge servers which then estimate the target node position after gathering anchors measurements and prior history. Kernel density estimation results are used to show that edge servers implementing Bayesian-based sensor fusion and positioning system effectively estimate the target node location when using hybrid metrics and combining past and current sensory inputs

Hierarchical Bayesian-based indoor positioning using distributed antenna systems

Abstract This work proposes and evaluates hierarchical Bayesian-based localization methods to estimate the position of a target node in indoor deployment scenarios. The measurements are acquired through a distributed antenna system which is connected to a common master anchor node. Each antenna head is affected by different channels parameters, what makes the estimation more difficult. The proposed method combines received signal strength and time of flight measurements to estimate the target location. In our investigations, we also consider a one-level hierarchical Bayesian network model, which introduces conditional interdependencies to the model parameters, resulting in less susceptibility to local variations. The Markov Chain Monte Carlo sampling method is used to approximate the posterior distribution of the two-dimensional target’s location coordinates. The root mean square error is used to evaluate the performance of the proposed solution in indoor scenarios. Our results show that by combining hybrid measurements or increasing conditions between the parameters by a hierarchical approach, the proposed mechanisms outperform the classic Bayesian model when estimating the target node using even fewer measurements

Hybrid Bayesian-based indoor localization mechanisms for distributed antenna systems

Abstract This work proposes and evaluates a hybrid Bayesian-based localization method to estimate the position of a target node using received signal strength and time of flight measurements. In our investigations, we consider these measurements are acquired through a distributed antenna system which is connected to a common master anchor node. The baseline non-hybrid scenarios use only received signal strength measurements to estimate the position of interest, while the hybrid implementation combines time of arrival measurements as well. Both Bayesian-based (non) hierarchical approaches approximates the posterior distribution of the target’s location coordinates using Markov Chain Monte Carlo methods. The hierarchical method introduces conditional interdependencies to the model parameters, resulting in less model variance. Herein, the root mean square error is used to evaluate the performance of the indoor test scenarios. Our results show that both hybrid and hierarchical approaches outperform the baseline Bayesian model, while the former significantly increase the accuracy the target position estimate