This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track object movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are expected to be widely used in future wireless communication networks to improve spectrum and energy efficiency. In this paper, RIS-assisted UAV communication systems are studied and analysed by developing a comprehensive mathematical framework for examining their outage performance. In order to study the effect of the RIS on UAV communications, two system scenarios are considered: in the first scenario, the UAV acts as an aerial base station (BS) serving a ground user to offload the terrestrial network, and in the second system, the UAV acts as an aerial user served by a terrestrial BS. We present channel models considering the UAV’s unique characteristics, propose a closed-form approximation for the signal-to-noise-ratio (SNR) distribution, and derive an analytical expression for the relevant outage probability. Results show that RIS can significantly improve the performance of UAV communication systems by introducing energy-efficient and reliable links. This opens the door for UAV networks, which are highly scalable, adaptable, and robust to environmental changes. Furthermore, the results show that the UAV position and altitude optimisation significantly affects the outage performance.

Line of sight (LoS) links that use high frequencies are sensitive to blockages, making it challenging to scale future ultra-dense networks (UDN) that capitalise on millimetre wave (mmWave) and potentially terahertz (THz) networks. This paper embraces two novelties; Firstly, it combines machine learning (ML) and computer vision (CV) to enhance the reliability and latency of next-generation wireless networks through proactive identification of blockage scenarios and triggering proactive handover (PHO). Secondly, this study adopts federated learning (FL) to perform decentralised model training so that data privacy is protected, and channel resources are conserved. Our vision-aided PHO framework localises users using object detection and localisation (ODL) algorithm that feeds a multiple-output neural network (NN) model to predict possible blockages. This involves analysing images captured from the video cameras co-located with the base stations (BSs) in conjunction with wireless parameters to predict future blockages and subsequently trigger PHO. Simulation results show that our approach performs remarkably well in highly dynamic multi-user environments where vehicles move at different speeds, and achieves 93.6% successful PHO. Furthermore, the proposed framework outperforms the reactive-HO methods by a factor of 3.3 in terms of latency while maintaining a high quality of experience (QoE) for the users.

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources.

The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition,this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).