In disaster scenarios, ensuring both reliable communication and situational awareness becomes a critical challenge due to the partial or complete collapse of terrestrial networks. This paper proposes an integrated sensing and communication (ISAC) over non-terrestrial networks (NTN) architecture— referred to as ISAC-over-NTN—that integrates multiple uncrewed aerial vehicles (UAVs) and a high-altitude platform station (HAPS) to maintain resilient and reliable network operations in post-disaster conditions. We aim to achieve two main objectives: i) provide a reliable communication infrastructure, thereby ensuring the continuity of search-and-rescue activities and connecting people to their loved ones, and ii) detect users, such as those trapped under rubble or those who are mobile, using a Doppler-based mobility detection model. We employ an innovative beamforming method that simultaneously transmits data and detects Doppler-based mobility by integrating multiuser multiple-input multiple-output (MU-MIMO) communication and monostatic sensing within the same transmission chain. The results show that the proposed framework maintains reliable connectivity and achieves high detection accuracy of users in critical locations, reaching 90% motion detection sensitivity and 88% detection accuracy.

This paper investigates the integration of active reconfigurable intelligent surfaces (RIS) relay with high-altitude platform stations (HAPS) to enhance non-terrestrial network (NTN) performance in next-generation wireless systems. While prior studies focused on passive RIS architectures, the severe path loss and double fading in long-distance HAPS links make active RIS a more suitable alternative due to its inherent signal amplification capabilities. We formulate a sum-rate maximization problem to jointly optimize power allocation and RIS element assignment for ground user equipments (UEs) supported by a HAPS-based active RIS-assisted communication system. To reduce power consumption and hardware complexity, several sub-connected active RIS architectures are also explored. Simulation results reveal that active RIS configurations significantly outperform passive RIS in terms of quality of service (QoS). Moreover, although fully-connected architectures achieve the highest throughput, sub-connected schemes demonstrate superior energy efficiency under practical power constraints. These findings highlight the potential of active RIS-enabled HAPS systems to meet the growing demands of beyond-cellular coverage and green networking.

This study introduces and addresses the critical challenge of traffic load estimation in cell switching within vertical heterogeneous networks (vHetNets). The effectiveness of cell switching is significantly limited by the lack of accurate traffic load data for small base stations (SBSs) in sleep mode, making many load-dependent energy-saving approaches impractical, as they assume perfect knowledge of traffic loads—an assumption that is unrealistic when SBSs are inactive. In other words, when SBSs are in sleep mode, their traffic loads cannot be directly known and can only be estimated, inevitably with corresponding errors. Rather than proposing a new switching algorithm, we focus on eliminating this foundational barrier by exploring effective prediction techniques. A novel vHetNet model is considered, integrating a high-altitude platform station (HAPS) as a super macro base station (SMBS). We investigate both spatial and temporal load estimation approaches, including three spatial interpolation schemes—random neighboring selection, distance-based selection, and multi-level clustering (MLC)—alongside a temporal deep learning method based on long short-term memory (LSTM) networks. Using a real-world dataset for empirical validation, our results show that both spatial and temporal methods significantly improve estimation accuracy, with the MLC and LSTM approaches demonstrating particularly strong performance.

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels.

Recently, the use of unmanned aerial vehicles (UAVs) for wireless communications has attracted much research attention. However, most applications of UAVs for wireless communication provisioning are not feasible as researchers fail to consider some vital aspects of their deployment, especially the energy requirements of both the UAV and communication system. The considerable energy consumption overhead involved in flying or hovering UAVs makes them less appealing for green wireless communications. Therefore, in this work, we examine the feasibility of an alternative energy-efficient deployment scheme where UAVs can be made to land-on designated locations, also known as landing stations (LSs). The idea of LS makes the UAV-based wireless communication more durable and advantageous, since the total energy consumption is reduced by minimizing the flying/hovering energy consumption, which, in turn, enables diverse set of applications including emergency and pop-up networking. We evaluate the impact of the separation distance between these LSs and the Optimal Hovering Position (OHP) on the network performance. Specifically, we develop mathematical frameworks to model the relationship between UAV power consumption, coverage probability, throughput, and separation distance. Numerical results reveal that a significant energy reduction can be achieved when the LS concept is exploited with a slight compromise in coverage probability and throughput. However, the choice of a suitable LS location depends on the users’ service requirements, transmit power, and frequency band utilized.

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of Things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multiobjective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.

In this study, we focus on the cell individual offset (CIO) parameter in the handover process, which represents the willingness of a cell to admit the incoming handovers. However, it is challenging to tune the CIO parameter, as any poor implementation can lead to undesired outcomes, such as making the neighboring cells over-loaded while decreasing the traffic load of the cell. In this work, a reinforcement learning-based approach for parameter selection is introduced, since it is quite convenient for dynamically changing environments. In that regard, two different techniques, namely Q-learning and SARSA, are proposed, as they are known for their multi-objective optimization capabilities. Moreover, fixed CIO values are used as a benchmark for the proposed methods for comparison purposes. Results reveal that the reinforcement learning assisted mobility load balancing (MLB) approach can alleviate the burden on the overloaded cells while keeping the neighboring cells at some reasonable load levels. The proposed methods outperform the fixed-parameter solution in terms of the given metric.

In the next few years, a considerable innovation concerning the design of the future 5G mobile networks will be a concrete step towards enabling effective high throughput and low latency services. Software Defined Networking (SDN), Network Function Virtualization (NFV) and Self Organizing Network (SON) are considered the enabling technologies to achieve these goals. In this paper, assuming a Control-Data Separation Architecture (CDSA), we propose a flexible SDN/NFV-based SON testbed, for future 5G mobile networks. The main contribution of our work is to cover the need for a CDSA based testbed, enabling the investigation of the NG-SON capabilities for practical implementations. We implement two different testbed setups, a real one and a virtualized one, both based on the FlexRAN and OpenAir-Interface software tools. First, we implement a specific case study, i.e., the RAN entities activation/deactivation procedures. Next, we carry out time measurements, concerning the aforementioned procedures, in order to prove proper testbed functioning. Finally, we validate the C-SON and D-SON capabilities of our testbed, considering the features of the results.

One of the fundamental goals of mobile networks is to enable uninterrupted access to wireless services without compromising the expected quality of service (QoS). This paper reports a number of significant contributions. First, a novel analytical model is proposed for holistic handover (HO) cost evaluation, that integrates signaling overhead, latency, call dropping, and radio resource wastage. The developed mathematical model is applicable to several cellular architectures, but the focus here is on the Control/Data Separation Architecture (CDSA). Second, data-driven HO prediction is proposed and evaluated as part of the holistic cost, for the first time, through novel application of a recurrent deep learning architecture, specifically, a stacked long-short-term memory (LSTM) model. Finally, simulation results and preliminary analysis reveal different cases where non-predictive and predictive deep neural networks can be effectively utilized, based on HO management requirements. Both analytical and machine learning models are evaluated with a benchmark, real-world dataset measuring human behaviors and interactions. Numerical and comparative simulation results demonstrate the potential of our proposed deep learning-driven HO management framework, as a future benchmark for the mobile networking and machine learning communities.

The Internet of Things (IoT) is spreading much faster than the speed at which the supporting technology is maturing. Today, there are tens of wireless technologies competing for IoT and a myriad of IoT devices with disparate capabilities and constraints. Moreover, each of many verticals employing IoT networks dictates distinctive and differential network qualities. In this work, we present a context‐aware framework that jointly optimises the connectivity and computational speed of the IoT network to deliver the qualities required by each vertical. Based on a smart port application, we identify energy efficiency, security, and response time as essential quality features and consider a wireless realisation of IoT connectivity using short range and long‐range technologies. We propose a reinforcement learning technique and demonstrate significant reduction in energy consumption while meeting the quality requirements of all related applications.

Handover (HO) management is one of the critical challenges in current and future mobile communication systems due to new technologies being deployed at a network level, such as small and femtocells. Because of the smaller sizes of cells, users are expected to perform more frequent HOs, which can increase signaling costs and also decrease user's performance, if a HO is performed poorly. In order to address this issue, predictive HO techniques, such as Markov chains (MC), have been introduced in the literature due to their simplicity and generality. This technique, however, experiences a path dependency problem, specially when a user performs a HO to the same cell, also known as a re-visit. In this paper, the path dependency problem of this kind of predictors is tackled by introducing a new 3D transition matrix, which has an additional dimension representing the orders of HOs, instead of a conventional 2D one. Results show that the proposed algorithm outperforms the classical MC based predictors both in terms of accuracy and HO cost when re-visits are considered.

Predictive algorithms have become very important for handover (HO) management in mobile communications. In this regard, numerous techniques are being applied in order to obtain more accurate and robust methods. Markov chains (MC) are one of the most commonly used predictors since their easy implementation. In this paper, a threshold-based approach is introduced to common MC predictors in order to make predictions more accurate, since the probability is the main actor in a prediction process. The threshold value aims to prevent the predictor from making inaccurate predictions in case the probabilities of two or more states are very close. Results show that the proposed threshold-based method can improve the performance in terms of both prediction accuracy and signaling cost, specially for high randomness degrees, while also decreasing the number of inaccurate predictions.