Cellular connectivity for UAV systems is interesting because it promises coverage in beyond visual line of sight scenarios. Inter-cell interference has been shown to be the main limiting factor at high altitudes. Using a realistic 3D simulator model, with real base station locations, this study confirms that UAVs at high altitudes suffer from significant interference, resulting in a worse coverage compared to ground users. When replacing the existing base stations by mmWave cells, our results indicate that ground coverage is decreased to only 90%, while UAVs just above rooftop level have a coverage probability of 100%. However, UAVs at higher altitude still suffer from excessive interference. Beamforming has the potential to improve mmWave link budget and to decrease interference and is for this reason a promising technology for ensuring connectivity to aerial users.

Unmanned aerial vehicles (UAVs) have gained popularity in the communications research community because of their versatility in placement and potential to extend the functions of communication networks. However, there remains a gap in existing works regarding measurement-verified stationarity analysis of the air-to-ground (A2G) Massive Multi-Input Multi-Output (MaMIMO) channel which plays an important role in realistic UAV deployment. In this paper, we first design a UAV MaMIMO communication platform for channel acquisition. We then use the testbed to measure uplink Channel State Information (CSI) between a rotary-wing drone and a 64-element MaMIMO base station (BS). For characterization, we focus on multidimensional channel stationarity which is a fundamental metric in communication systems. Afterward, we present measurement results and analyze the channel statistics based on power delay profiles (PDPs) considering space, time, and frequency domains. We propose the stationary angle (SA) as a supplementary metric of stationary distance (SD) in the time domain. We analyze the coherence bandwidth and RMS delay spread for frequency stationarity. Finally, spatial correlations between elements are analyzed to indicate the spatial stationarity of the array. The space-time-frequency channel stationary characterization will benefit the physical layer design of MaMIMO-UAV communications.

Cell-free massive MIMO networks have recently emerged as an attractive solution capable of solving the performance degradation at the cell edge of cellular networks. For scalability reasons, user-centric clusters were recently proposed to serve users via a subset of APs. In the case of dynamic mobile scenarios, this network organization requires predictive algorithms for forecasting propagation parameters to maintain performance by proactively allocating new APs to a user. However, a major scientific challenge is the accuracy of predicting the channel gain evolution in non-stationary channels with low computational complexity, considering the uncertainty caused by user mobility. The novelty of this paper is the design of a multidimensional BiLSTM-based multivariate channel gain forecasting algorithm achieving a similar accuracy to previous research at reduced computational complexity. Indeed, thanks to the combination of dual prediction by the multidimensional BiLSTM exploiting the channel diversity from multiple antennas, our model mitigates the error propagation typically faced by sequential neural networks. Our model has a lower error by at least a factor of 2.7 and lower complexity by a factor of 3.6 (for a single prediction), compared to hybrid CNN-LSTM model. Also, in contrast to parallel transformer solutions, the growth rate of the complexity of our algorithm is significantly lower.

This paper presents a comprehensive measurement-based trajectory-aware characterization of low-altitude Air-to-Ground (A2G) channels in a suburban environment. A 64-element Massive Multi-Input Multi-Output (MaMIMO) array was used to capture channels for three trajectories of an Uncrewed Aerial Vehicle (UAV), including two horizontal zig-zag flights at fixed altitudes and one vertical ascent, chosen to emulate Aerial User Equipment (AUE) operations and to induce controlled azimuth and elevation sweeps for analyzing geometry-dependent propagation dynamics. We examine large-scale power variations and their correlation with geometric features, such as elevation, azimuth, and 3D distance, followed by an analysis of fading behavior through distribution fitting and Rician K-factor estimation. Furthermore, temporal non-stationarity is quantified using the Correlation Matrix Distance (CMD), and angular stationarity spans are utilized to demonstrate how channel characteristics change with the movement of the UAV. We also analyze Spectral Efficiency (SE) in relation to K-factor and Root Mean Square (RMS) delay spread, highlighting their combined influence on link performance. The results show that the elevation angle is the strongest predictor of the received power, with a correlation of more than 0.77 for each trajectory. At the same time, the Nakagami model best fits the small-scale fading. The K-factor increases from approximately 5 dB at low altitudes to over 15 dB at higher elevations, indicating stronger Line-of-Sight (LoS) dominance. Non-stationarity patterns are highly trajectory- and geometry-dependent, with azimuth most affected in horizontal flights and elevation during vertical flight. These findings offer measurement-driven insights into low-altitude AUE channel behavior in suburban LoS-dominant environments and provide a baseline for future extensions toward higher-altitude and dense urban Non-Terrestrial Network (NTN) scenarios.

In future drone applications fast moving unmanned aerial vehicles (UAVs) will need to be connected via a high throughput ultra reliable wireless link. MmWave communication is assumed to be a promising technology for UAV communication, as the narrow beams cause little interference to and from the ground. A challenge for such networks is the beamforming requirement, and the fact that frequent handovers are required as the cells are small. In the UAV communication research community, mobility and especially handovers are often neglected, however when considering beamforming, antenna array sizes start to matter and the effect of azimuth and elevation should be studied, especially their impact on handover rate and outage capacity. This paper aims to fill some of this knowledge gap and to shed some light on the existing problems. This work will analyse the performance of 3D beamforming and handovers for UAV networks through a case study of a realistic 5G deployment using mmWave. We will look at the performance of a UAV flying over a city utilizing a beamformed mmWave link.

In this paper, we present a fixed mmWave Multi-User Multiple-Input Multiple-Output (MIMO) system for fixed wireless access with a unique architecture. A digital MIMO system is combined with an analog multi-beam antenna array which uses a high-dimension 16x16 Butler matrix to obtain 16 orthogonal beams. A system model of this architecture is presented and used to simulate its performance comparing to the performance of common-used patch antennas. Several MIMO precoding techniques are considered and compared with basic analog beamforming. To verify these results, a prototype is built and a dedicated measurement campaign is performed. The results show that the system model is a good approximation and that the use of the multi-beam antenna array is a good alternative to patch antennas for a large number of users.