The extension from centimeter wave frequencies to millimeter wave (mmWave) frequencies has triggered an enormous transformation in terms of radio access architecture for future wireless networks, and it has therefore empowered unlimited opportunities for the user-oriented services and applications. Besides mmWave as a driving element, beamforming (BF) will be incorporated as a key enabling technology for the future wireless networks. In this paper, we propose a positioning-aided beamforming (PA-BF) framework for enhanced downlink communications in a cloud-oriented mmWave mobile networks. We show that the proposed PA-BF achieves a higher effective transmit ratio that is equivalent to a lower initial access latency than the conventional codebook-based BF, which in turn manifests its capability to support high-velocity mobile users. We also analyze the impact of positioning accuracies on the performance of PA-BF and discuss the trade-offs between different BF strategies with varied system parameters. Our simulation results demonstrate that, with a narrow beam phased array and reasonably good positioning accuracy, the PA-BF framework is capable of achieving higher spectral efficiency than the considered codebook-based BF especially at higher velocities.

Since the beginning of the fifth generation (5G) standardization process, positioning has been considered as a key element in future cellular networks. In order to perform accurate positioning, solutions for estimating and processing location-related measurements such as direction of arrival (DoA) and time of arrival (ToA) for various use-cases need to be developed. In this paper, building on the existing 5G new radio (NR) specifications and millimeter wave frequencies, we propose a novel estimation and tracking solution of the DoA and ToA such that only analog/radio frequency (RF) beamforming-based observations are utilized. In addition to the proposed extended Kalman filter (EKF)-based estimation and tracking approach, we derive Cramér-Rao lower bounds (CRLBs) for the considered RF multi-beam system, and propose an information-based criterion for selecting the necessary beams for the estimation process in order to provide highly accurate performance with feasible computational complexity. The performance of the proposed method is evaluated using extensive ray-tracing simulations and numerical evaluations, and the results are compared with other estimation and beam-selection approaches. Based on the obtained results, beam-selection at the receiver can have a significant impact on the DoA and ToA estimation performance as well as on the subsequent positioning accuracy. Finally, we demonstrate the highly accurate performance of the methods when extended to joint multi-receiver-based device positioning and clock synchronization.

This paper discusses several features of 5G positioning in the context of applications for the Industrial Internet of Things (IIoT) which demand high accuracy of position information. The main opportunities to come with 5G networks, such as huge available spectrum, small cell networks, Multiple Input Multiple Output antennas and beamforming are summarized, and the challenges in the context of robot 5G positioning are pointed out. A case study for the localization of an indoor robot in a multi-wall multi-floor scenario is presented, based on various carrier frequencies and access node densities. We find out that sub-meter positioning accuracy required for most of the future industrial applications is theoretically achievable via a combination of small cell networks, mm Wave carriers and antenna arrays, but practical issues such as node synchronization, connectivity and ultra dense network deployment costs have to be tackled.

The integration of millimeter wave (mmWave) and low frequency interfaces brings an unique opportunity to unify the communications and positioning technologies in the future wireless heterogeneous networks (HetNets), which offer great potential for efficient handover using location awareness, hence a location-aware handover (LHO). Targeting a self-organized communication system with autonomous vehicles, we conduct and describe an experimental and analytical study on the LHO using a mmWave-enabled robotic platform in a multi-radio environment. Compared to the conventional received signal strength indicator (RSSI)-based handover, the studied LHO not only improves the achievable throughput, but also enhances the wireless link robustness for the industrial Internet-of-things (IIoT)-oriented applications. In terms of acquiring location awareness, a geometry-based positioning (GBP) algorithm is proposed and implemented in both simulation and experiments, where its achievable accuracy is assessed and tested. Based on the performed experiments, the location-related measurements acquired by the robot are not accurate enough for the standalone-GBP algorithm to provide an accurate location awareness to perform a reliable handover. Nevertheless, we demonstrate that by combining the GBP with the dead reckoning, more accurate location awareness becomes achievable, the LHO can therefore be performed in a more optimized manner compared to the conventional RSSI-based handover scheme, and is therefore able to achieve approximately twice as high average throughput in certain scenarios. Our study confirms that the achieved location awareness, if accurate enough, could enable an efficient handover scheme, further enhancing the autonomous features in the HetNets.

The millimeter wave (mmWave) device-to-device air interface not only supports a direct wireless connectivity among devices, but it also offers an improved beamforming capability to obtain the direction information among the vehicles and devices for positioning. Both features serve as the key physical layer components for communications and positioning in the industrial Internet of things (IIoT) systems. Exploiting both accurate beamforming and wide bandwidth in a mmWave network, high-accuracy positioning is achievable, which can be then facilitated for location-aware communications, for instance. However, the uncertainty of anchors’ locations in the industrial environment highly degrades the achievable positioning accuracy if left without proper consideration. In order to resolve such challenge, this paper presents a cooperative positioning system (CPS), where the locations of all the vehicles and anchors can be jointly estimated based on acquired location-related measurements (LRMs). Furthermore, the positioning performance is evaluated under random trajectories and different geometric relationships between the vehicles and the anchors. We show that, the proposed positioning solution is capable of resolving the aforementioned challenge by simultaneously tracking the mobile vehicles while mapping the locations of the static anchors. Utilizing the LRMs from both time and angular domains, the achieved positioning accuracy in both 2D and vertical plane is demonstrated based on extensive numerical simulations. Last but not least, the impact of different numbers of the mobile vehicles on the overall positioning performance is also investigated.