This paper studies the performance of a user positioning system using Channel State Information (CSI) of a Massive MIMO (MaMIMO) system. To infer the position of the user from the CSI, a Convolutional Neural Network is designed and evaluated through a novel dataset. This dataset contains indoor MaMIMO CSI measurements using three different antenna topologies, covering a 2.5 m by 2.5 m indoor area. We show that we can train a Convolutional Neural Network (CNN) model to estimate the position of a user inside this area with a mean error of less than half a wavelength. Moreover, once the model is trained on a given scenario and antenna topology, Transfer Learning is used to repurpose the acquired knowledge towards another scenario with significantly different antenna topology and configuration. Our results show that it is possible to further train the CNN using only a small amount of extra labelled samples for the new topology. This transfer learning approach is able to reach accurate results, paving the road to a practical CSI-based positioning system powered by CNNs.

This paper considers the problem of radar sensing by using a large number of antennas. We use the orthogonal frequency division multiplexing (OFDM) waveform, and show that the large arrays used in massive multiple-input multiple-output (MIMO) communications enable accurate localization in the array near-field, even at the narrow bandwidths typically encountered at low carrier frequencies. We validate our findings experimentally with a massive MIMO testbed operating at 3.5 GHz carrier frequency and 18 MHz OFDM bandwidth in an indoor environment. We consider a single moving cylinder, and demonstrate a median accuracy of (3.4, 5.6) cm in ( $x$ , $y$ ) in the near-field. We show that the accuracy is maintained with only a single subcarrier, and that the resolution increases with an order of magnitude when combining all antennas, effectively surpassing the 16.67 m bistatic range resolution set by the OFDM waveform. We use a radar symbol duration of $71.88~\mu $ s at an effective transmission period of 2.5 ms, which indicates that the radar and communication systems can be implemented in time-division with a capacity loss of only 2.9%. Our results suggest that near-field radar sensing can be integrated into future massive MIMO systems operating at low carrier frequencies and narrow bandwidths.

Tutorial on UAVs: A Blue Sky View on Wireless Communication

The inherent limitations in scaling up ground infrastructure for future wireless networks, combined with decreasing operational costs of aerial and space networks, are driving considerable research interest in multisegment ground-air-space (GAS) networks. In GAS networks, where ground and aerial users share network resources, ubiquitous and accurate user localization becomes indispensable, not only as an end-user service but also as an enabler for location-aware communications. This breaks the convention of having localization as a byproduct in networks primarily designed for communications. To address these imperative localization needs, the design and utilization of ground, aerial, and space anchors require thorough investigation. In this tutorial, we provide an in-depth systemic analysis of the radio localization problem in GAS networks, considering ground and aerial users as targets to be localized. Starting from a survey of the most relevant works, we then define the key characteristics of anchors and targets in GAS networks. Subsequently, we detail localization fundamentals in GAS networks, considering 3D positions, orientations, and velocities. Afterward, we thoroughly analyze radio localization systems in GAS networks, detailing the system model, design aspects, and considerations for each of the three GAS anchors. Preliminary results are presented to provide a quantifiable perspective on key design aspects in GAS-based localization scenarios. We then identify the vital roles 6G enablers are expected to play in radio localization in GAS networks.

Up until now, path planning for unmanned aerial vehicles (UAVs) has mainly been focused on the optimisation towards energy efficiency. However, to operate UAVs safely, wireless coverage is of utmost importance. Currently, deployed cellular networks often exhibit an inadequate performance for aerial users due to high amounts of intercell interference. Furthermore, taking the never-ending trend of densification into account, the level of interference experienced by UAVs will only increase in the future. For the purpose of UAV trajectory planning, wireless coverage should be taken into account to mitigate interference and to lower the risk of dangerous connectivity outages. In this paper, several path planning strategies are proposed and evaluated to optimise wireless coverage for UAVs. A simulator using a real-life 3D map is used to evaluate the proposed algorithms for both 4G and 5G scenarios. We show that the proposed Coverage-Aware A* algorithm, which alters the UAV's flying altitude, is able to improve the mean SINR by 3-4dB and lower the cellular outage probability by a factor of 10. Furthermore, the outages that still occur have a 60% shorter length, hence posing a lower risk to induce harmful accidents.

Wireless-based human activity recognition has become an essential technology that enables contact-free human–machine and human–environment interactions. In this article, we consider contact-free multitarget tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6-GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then, the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets’ locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multipedestrian tracking experiment is conducted in a room with a size of 6.5 $\text{m}\times 10$ m to evaluate the performance of the proposed algorithm. According to the experimental results, we achieve 75th and 95th percentile accuracy of 12.7 and 18.2 cm for single-person tracking and 28.9 and 45.7 cm for multiperson tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real time, which is promising for practical MTT use cases.