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High-Efficiency Device Positioning and Location-Aware Communications in Dense 5G Networks

Mike Koivisto, Aki Hakkarainen, Mário Costa, Petteri Kela, Kari Leppänen, Mikko Valkama

arXiv:1608.03775v3cs.ITcs.NI

TL;DR

Dense 5G networks create a need and opportunity for efficient device positioning and location-aware communications. The paper reviews network-side fusion of directional and delay measurements and evaluates how resulting location information supports positioning and radio-access functions, including beamforming. It reports technically feasible sub-meter positioning and substantially reduced reference-signal overhead for location-based beamforming, alongside throughput trade-offs under fair scheduling.

  • Problem

    The paper addresses how dense 5G networks can provide accurate, energy-efficient device positioning and exploit location information in communications and network management.

  • Method

    It reviews network-centric DoA and ToA estimation, EKF-based fusion and tracking, and geometric location-based beamforming using the resulting location information.

  • Results

    Sub-meter positioning is technically feasible, while location-based beamforming substantially reduces pilot or reference-signal overhead compared with basic CSI-based beamforming.

  • Takeaways & Limitations

    Location awareness supports tracking, geometric beamforming, spatial interference mitigation, radio-environment maps, and proactive radio-resource management.

Abstract

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In this article, the prospects and enabling technologies for high-efficiency device positioning and location-aware communications in emerging 5G networks are reviewed. We will first describe some key technical enablers and demonstrate by means of realistic ray-tracing and map based evaluations that positioning accuracies below one meter can be achieved by properly fusing direction and delay related measurements on the network side, even when tracking moving devices. We will then discuss the possibilities and opportunities that such high-efficiency positioning capabilities can offer, not only for location-based services in general, but also for the radio access network itself. In particular, we will demonstrate that geometric location-based beamforming schemes become technically feasible, which can offer substantially reduced reference symbol overhead compared to classical full channel state information (CSI)-based beamforming. At the same time, substantial power savings can be realized in future wideband 5G networks where acquiring full CSI calls for wideband reference signals while location estimation and tracking can, in turn, be accomplished with narrowband pilots.

I. INTRODUCTION

Dense 5G deployments create opportunities for highly accurate, network-centric positioning and location-aware communications. The article reviews technical enablers and applications, emphasizing connected vehicles and network management.

  • Densely distributed access nodes give devices multiple nearby connections, reducing propagation losses and enabling highly accurate positioning.The article identifies UE location acquisition and exploitation as a leading theme for 5G networks.
  • 5G positioning is expected to achieve accuracy around one meter or below, outperforming LTE OTDoA, GNSS, and WLAN fingerprinting accuracies.LTE provides accuracy of a couple of tens of meters, GNSS around 5 m, and WLAN fingerprinting 3 m–4 m.
  • Frequently transmitted uplink pilot signals can support network-centric positioning without requiring location calculations in mobile UEs.UE locations may be estimated independently at access nodes or centrally at a fusion center with known access-node locations.
  • The article discusses positioning enablers and location-based communication and network-management techniques, including beamforming, mobility management, and radio-resource management.It also considers location information shared with UEs and third parties for emerging applications.
  • Connected vehicles, self-driving cars, intelligent traffic systems, drones, and autonomous robots are identified as application areas for network-based localization and tracking.The connected-car target includes a minimum of 2000 connected vehicles per km2 and at least 50 Mbps in downlink throughput.

II. 5G NETWORKS AND POSITIONING PROSPECTS

5G radio-network properties support accurate positioning through dense deployments, smart antennas, and frequent location information. Device-centric operation can tailor communication sessions and associated access-node functions to devices or services.

  • Technical properties of 5G radio networks: Ultra-dense 5G networks are envisioned with access-node inter-site distances from a few meters to a few tens of meters.Examples include several access nodes per room indoors and one access node on each outdoor lamp post.
  • Technical properties of 5G radio networks: Smart antenna arrays support MIMO communications and accurate direction-of-arrival estimation, enabling high-accuracy positioning.Densification also increases the likelihood of line-of-sight conditions between devices and access nodes.
  • Technical properties of 5G radio networks: Wide bandwidths and higher frequency bands, including millimeter waves, are expected to increase capacity and frequency reuse but also produce larger propagation losses.The passage frames these propagation conditions as a challenge for effective communications.
  • Technical properties of 5G radio networks: Device-centric 5G architectures can tailor communication sessions and associated access-node functions to particular devices or services rather than to the commanding cell.The passage also associates this development with improved cell-border quality of experience and minimal system-performance degradation.
  • Technical properties of 5G radio networks: Short radio frames are expected to provide frequent location information about transmitting devices.This supports the availability of location information for network functions.

B. Leveraging Location-Awareness in 5G Networks

Continuous location awareness enables tracking, prediction, and radio-network functions such as geometric beamforming and proactive resource management. Location information also supports traffic, safety, and energy-efficiency applications for connected vehicles.

  • Continuous positioning provides current and past UE locations, allowing the network to track devices and use predictive algorithms to estimate future locations.The passage explicitly connects location histories with UE tracking and predicted trajectories.
  • Geometric beamforming and spatial interference mitigation use location awareness to multiplex dense UE populations and improve throughput for high-mobility UEs.These functions exploit the spatial dimension of communications.
  • Combining location information with long-term radio measurements enables radio-environment-map construction and proactive radio-resource management.Large-scale fading and location-based radio conditions can be used for resource management without requiring the full passage's omitted information.
  • Shared car locations and predicted trajectories can support traffic-flow, safety, and energy-efficiency improvements, including enhanced traffic monitoring and control.The passage also identifies accurate in-car location information as useful for navigation.
  • The positioning prospects include radio-environment-map generation, proactive resource management for tracked cars, and intelligent-traffic-system control and collision avoidance.These three prospects are illustrated in Fig. 2.

A. State-of-the-Art

Dense 5G channels often provide line-of-sight conditions that support joint directional and temporal positioning measurements. The reviewed techniques use reference signals, multiantenna access nodes, Bayesian filtering, and EKF-based tracking, while unsurveyed-node localization can increase computational complexity.

  • Technical positioning enablers: Estimating line-of-sight status and directional parameters alongside time-of-flight and clock offsets can improve UE positioning accuracy.The Rice factor can be estimated to assess whether a radio link is line of sight.
  • Technical positioning enablers: Uplink reference signals can support range, access-node synchronization, UE synchronization, and time-of-arrival estimation using multicarrier waveforms.Bayesian filters can perform sequential estimation in cascaded or centralized architectures.
  • Technical positioning enablers: Multiantenna access nodes estimate line-of-sight directions, while planar or conformal arrays provide elevation and azimuth measurements for 3D positioning.Bayesian filtering can track directions from mobile UEs and fuse time-of-arrival and direction-of-arrival measurements.
  • Technical positioning enablers: The reviewed tracking processes use extended Kalman filters because of their estimation performance and lower computational complexity relative to particle and unscented Kalman filters.The state-space models are nonlinear, motivating nonlinear Bayesian filtering techniques.
  • Anchor positioning and scope: A few surveyed access nodes can locate neighboring access nodes for use as new anchors, avoiding the cost of surveying every node.Joint UE tracking and access-node positioning through SLAM is more computationally complex because it estimates many parameters.

B. Tracking of Directional Parameters using EKFs

The paper compares network-centric and decentralized EKFs for tracking line-of-sight directional parameters in dense 5G networks. Network-centric tracking jointly estimates arrival and departure angles, while decentralized tracking uses narrowband single-antenna uplink signals and downlink beamformed references.

  • Tracking schemes: The network-centric EKF jointly tracks line-of-sight arrival and departure angles at the access node using uplink reference signals from all UE antenna elements.The decentralized EKF instead tracks arrival angles at the access node from a single UE antenna element and supports subsequent UE-side tracking through downlink beamformed references.
  • Evaluation setup: The evaluation uses a 74-access-node Madrid deployment with multiantenna access nodes and UEs moving along different routes.Access nodes use 20 dual-polarized elements, UEs use four dual-polarized elements, and transmit powers are 23 dBm and 10 dBm, respectively.
  • Pilot design: Directional tracking pilots occupy a single frequency-hopping subcarrier spanning 10 MHz and are transmitted every 500th TTI, yielding 10 beacons per second.In the decentralized scheme, the latency between uplink and downlink pilots is two TTIs.
  • Results: The network-centric EKF outperforms the decentralized EKF because it estimates and tracks all directional parameters jointly.This improvement requires greater computational complexity and control-channel capacity.
  • Interference and array effects: Muting neighboring access nodes improves decentralized UE angle estimates by reducing downlink interference, while not changing access-node estimates derived from uplink references.Elevation accuracy is also affected by the access-node patch-element beampattern, whose polar attenuation reduces effective elevation aperture.

C. Positioning Accuracy using Cascaded EKFs

Cascaded EKFs fuse directional and temporal measurements from line-of-sight access nodes into moving-UE position and synchronization estimates. In Madrid-grid evaluations, both synchronized and unsynchronized network configurations achieve the envisioned sub-meter accuracy with sufficiently wide reference signals.

  • Cascaded positioning: A positioning and synchronization EKF fuses DoA and ToA estimates from all line-of-sight access nodes into 3D UE location estimates.The cascaded solution can also estimate clock offsets and support 3D or single-access-node positioning.
  • Evaluation: The evaluation compares Pos&Clock, Pos&Sync, and classical DoA-only EKFs for automotive UEs using 4.8 MHz and 9.6 MHz reference-signal bandwidths.The simulations use a constant-velocity model with vehicle speeds up to 50 km/h and evaluate 2D positioning over random Madrid-grid trajectories.
  • Results: At least 93% probability of sub-meter positioning accuracy is achieved by both Pos&Clock and Pos&Sync EKFs with 9.6 MHz reference-signal bandwidth.Pos&Clock is more accurate than Pos&Sync because its access nodes are synchronized, while both outperform the DoA-only EKF through additional ToA estimates.
  • Bandwidth and synchronization: The 9.6 MHz reference-signal bandwidth improves positioning because its finer time resolution produces more accurate ToA estimates.The cascaded EKFs also track UE and access-node clock offsets with high accuracy.

IV. LOCATION-BASED GEOMETRIC BEAMFORMING AND MOBILITY MANAGEMENT

Accurate positioning in ultra-dense 5G networks supports geometric location-based beamforming and device-centric mobility management. These approaches can reduce reference-signal requirements and support nearby access-node coordination for low-latency, seamless service.

  • Geometric beamforming: High line-of-sight probability in ultra-dense networks makes geometric beams feasible without estimating full-band channel-state information at the transmitter.The approach is enabled by accurate positioning and directional information for nearby access nodes and users.
  • Reference-signal overhead: Replacing full-band uplink reference signals with narrowband uplink pilots can substantially reduce energy use, especially at the UE.Location-based processing can also calculate UE receive filters when high-accuracy desired-signal DoA estimates are available.
  • Mobility management: Continuous network tracking lets centralized control assign each UE a small set of nearby serving access nodes and distribute its data among them.This device-centric arrangement supports ultra-short latencies, borderless QoE, seamless mobility, and less legacy cell-measurement reporting.

A. Evaluation Setup

The evaluation models a dense 5G TDD network with mobile users, a 200 MHz carrier, and a specified downlink-to-uplink frame ratio. Users are distributed uniformly over the simulated street area.

  • Network and user configuration: The setup contains 43 access nodes, 1000 users/km2, and users uniformly dropped across the simulated street area.These assumptions represent the network configuration used for the evaluation.
  • Radio and mobility configuration: The simulations use a single unpaired 200 MHz TDD carrier and UE velocity of 30 km/h.The downlink-to-uplink ratio is configured as 4.7:1 in the 5G TDD frame structure.

B. Performance Results and Comparison of Location-based and CSI-based Beamforming

The evaluated beamforming schemes achieve similar overall performance in line-of-sight-dominated ultra-dense networks, while location-based receive filtering improves low-percentile throughput. Combining channel-based transmit beamforming with location-based receive beamforming provides the best overall result, although fairness scheduling reduces area throughput.

  • Block diagonalization extends zero forcing and optimizes beams for multiantenna receivers, improving receive-filter performance.
  • CSI-based and location-based beamforming achieve rather similar throughput distributions because line-of-sight paths dominate.
  • 100% increase in 5-percentile throughput is obtained with location-based receive beamforming compared with CSI-based receive beamforming.Pilot contamination degrades CSI-based receive beamforming when access nodes share physical resources for downlink beamformed pilots.
  • The best overall performance combines channel-based transmit beamforming with location-based receive filtering.Residual non-line-of-sight users explain the advantage of channel-based transmit beamforming with zero-forcing-based precoders such as block diagonalization.
  • Fair time-domain scheduling reduces simulated area throughput from 1 Tbps/km2 to 0.65 Tbps/km2 while reducing throughput variation across users.The trade-off results from favoring users with poor channel conditions over users with high signal-to-interference-plus-noise ratios.

V. CONCLUSIONS AND FUTURE WORK

The article concludes that dense 5G networks can support highly accurate positioning and exploit location information for communications and network management. It identifies sub-meter outdoor positioning and lower-overhead geometric beamforming as technically feasible benefits.

  • Outdoor positioning accuracies below one meter are technically feasible through access-node direction and delay estimation fused with extended Kalman filtering.
  • Location-based beamforming can substantially reduce pilot or reference-signal overhead compared with basic CSI-based beamforming.
  • Tracking device locations can benefit location-based services, communications, and radio-network management.
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