Many algorithms related to vehicular applications, such as enhanced perception of the environment, benefit from frequent updates and the use of data from multiple vehicles. Federated learning is a promising method to improve the accuracy of algorithms in the context of vehicular networks. However, limited communication bandwidth, varying wireless channel quality, and potential latency requirements may impact the number of vehicles selected for training per communication round and their assigned radio resources. In this work, we characterize the vehicles participating in federated learning based on their importance to the learning process and their use of wireless resources. We then address the joint vehicle selection and resource allocation problem, considering multi-cell networks with multi-user multiple-input multiple-output (MU-MIMO)-capable base stations and vehicles. We propose a “vehicle-beam-iterative” algorithm to approximate the solution to the resulting optimization problem. We then evaluate its performance through extensive simulations, using realistic road and mobility models, for the task of object classification of European traffic signs. Our results indicate that MU-MIMO improves the convergence time of the global model. Moreover, the application-specific accuracy targets are reached faster in scenarios where the vehicles have the same training data set sizes than in scenarios where the data set sizes differ.

The throughput performance of intelligently shaped and fixed analog elevation beam patterns in millimeter-wave (mm-wave) base stations with hybrid beamforming (HBF) is assessed for the first time. Distinct spatially heterogeneous user distributions (i.e., uniform, near-site, cell-edge, and weighted uniform and near-site) and propagation environments (i.e., line-of-sight (LoS) with multipath and non-line-of-sight (NLoS) with multipath) are considered. The cosecant-squared and flat-top shaped beam patterns are compared to the benchmark pencil beam pattern with a straightforward electrical downtilt. The LoS simulation results show that in case of unknown weight of user distribution scenarios, the cosecant-squared pattern is the most robust, with a gain of up to 16% in the average system throughput and up to 34% in the 90th percentile user throughout compared to the benchmark. If the near-site case has a greater probability of occurrence than the uniform user distribution (e.g., due to daily events and festivals), the flat-top pattern becomes preferable. In the NLoS scenario, the considered HBF architectures with elevation beam pattern shaping do not bring any performance disadvantages compared to the benchmark HBF.

Reconfigurable Intelligent Surfaces (RIS) stand out among the key technologies driving 6G mobile network development. In this paper, we develop and assess radio resource management solutions aimed to exploit the potential of RIS deployments for coverage and throughput enhancement for indoor users in 6G mobile networks. We introduce two heuristic algorithms that jointly control the cell-RIS-user association, user scheduling, transmit beamforming and the RIS's reflective configuration, and compare these algorithms against a RIS-free benchmark. Simulation results are presented to (i) demonstrate the promising potential of RIS deployments in multi-cell/multi-user scenarios; (ii) reveal the inherent trade-off between coverage and throughput enhancement; and (iii) show the performance impact of distinct RIS deployment locations. Our study provides valuable insights for efficiently leveraging RIS in evolving mobile network architectures.

—Wireless communication networks provide a critical infrastructure, particularly in emergency situations due to disruptive events such as natural disasters or terrorist attacks. However, in these kinds of scenarios part of the network may no longer be operational and a traffic hotspot may emerge, which may result in coverage and/or capacity issues. Deploying self-steering drone-mounted base stations offers a potential method to quickly restore coverage and/or provide capacity relief in such situations, but appropriate positioning is crucial in order for a drone base station to be truly effective. Motivated by that challenge, we propose a data-driven algorithm to optimize the position of a drone base station in a scenario with a site failure and emergence of a traffic hotspot. We demonstrate that the use of a drone, when properly positioned, yields significant performance gains, and that our algorithm outperforms benchmark mechanisms in a wide range of scenarios. In addition, we show that our algorithm is able to find a near-optimal position for the drone in a reasonable amount of time, and even has the ability to track the optimal position in case of a moving hotspot.

In this paper, we focus on the link density in random geometric graphs (RGGs) with a distance-based connection function. After deriving the link density in D dimensions, we focus on the two-dimensional (2D) and three-dimensional (3D) space and show that the link density is accurately approximated by the Fréchet distribution, for any rectangular space. We derive expressions, in terms of the link density, for the minimum number of nodes needed in the 2D and 3D spaces to ensure network connectivity. These results provide first-order estimates for, e.g., a swarm of drones to provide coverage in a disaster or crowded area.

This study proposes a two-step ML-based multislice radio resource allocation framework for 5G networks, specifically for emergency scenarios and featuring a good tradeoff between complexity and performance. In the first step, call-level resource demands are predicted using supervised ML, which are then aggregated to predict slice-specific resource demands. An innovative method is included in this step to ensure the collection of representative training data for the supervised ML. In the second step, a contextual multi-armed bandit reinforcement learning model is applied to derive the resource allocation among the slices based on the slice-specific resource demand predictions. The simulation results show that the proposed framework outperforms alternative solutions in the defined utility values for priority emergency traffic at the cost of modest performance sacrifice of the background traffic.

With the recent adoption of millimeter-wave spectrum in cellular communications, deployment of active antenna arrays and use of beamforming become vital to compensate for the increased path loss. However, directional high-frequency signals may suffer heavy attenuations due to blockage effects. Therefore, blockage modelling that adequately incorporates the effects of beamforming becomes increasingly relevant. We propose a Four Knife-Edge Diffraction with antenna Gain (4KED-G) model, a deterministic approach to model blockage with broad applicability. The 4KED-G model advances upon the existing models in its inclusion of both angular antenna gains and the diffraction from all the four edges of a rectangular screen blocker, leading to a more general and flexible blockage modelling approach compared to existing widely accepted blockage models. We theoretically show that the proposed generalised model incorporates the strengths of these existing models, while overcoming their shortcomings in establishing applicability to wider range of blockage scenarios. We validate the generalised model against known knife-edge diffraction blockage models for specific scenarios.

The use of drone base stations offers an agile mechanism to safeguard coverage and provide capacity relief when cellular networks are under stress. Such stress conditions can occur for example in case of special events with massive crowds or network outages. In this paper we focus on a disaster scenario with emergence of a hotspot, and analyze the impact of the drone position (altitude, horizontal position) and selection bias on the network performance. We determine the optimal settings of these control parameters as a function of the hotspot location, and demonstrate that the optimized values can drastically reduce the fraction of failed calls.

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.

We present a simulation-based assessment of the performance potential of distributed MIMO (D-MIMO), multi-user MIMO (MU-MIMO) and particularly the combined D/MU-MIMO operation, for which we extend previously published scheduling and beamforming principles. The assessment study reveals that, when optimizing average user throughput performance, D-MIMO, while fruitless when used in isolation, is very effective when intelligently combined with MU-MIMO. Alternatively, when optimizing the cell edge performance, MU-MIMO, while also shown to be ineffective when used in isolation, is in fact very valuable when accompanied by a suitable configuration of D-MIMO. As an illustrative example, when using a jointly optimised configuration of D/MU-MIMO in a highly loaded urban deployment scenario, a 121% cell edge performance gain can be attained over a scenario using only D-MIMO, and even a demonstrated 153% gain over a scenario where only the MU-MIMO feature is available.

A suitability assessment and performance optimisation is presented of narrowband Internet of Things (NB-IoT) cellular technology for use in smart energy distribution networks. The focus is on the reliable and timely delivery of outage restoration and management (ORM) messages at the event of a local or regional power outage. Both the cellular NB-IoT and the energy distribution networks are modelled in a system-level simulator, which is used to carry out an extensive sensitivity analysis of the ORM service performance w.r.t. various radio network configurations in different environments. In particular, different packet schedulers are proposed and analysed, addressing device prioritization and subcarrier allocation as essential mechanisms in optimizing the service performance. Furthermore, we consider all three possible NB-IoT spectral deployment modes: in-band, guard-band and stand-alone deployment. Results show that, with a proposed near-optimal radio network configuration, the reliability of the ORM message delivery is close to 100% for the majority of power outage scenarios, while the observed 95th transfer delay percentile for the ORM messages is within the acceptable limit of 20 s. The study concludes that indeed NB-IoT is a suitable technology for supporting ORM services in smart energy distribution networks.