Halim Yanikomeroglu

Afsoon Alidadi Shamsabadi, Animesh Yadav, Halim Yanikomeroglu

High altitude platform stations (HAPS) offer a promising solution for achieving ubiquitous connectivity in next-generation wireless networks (xG). Integrating HAPS with terrestrial networks, creating HAPS-empowered vertical heterogeneous networks (vHetNets), significantly improves coverage and capacity and supports emerging novel use cases. In HAPS-empowered vHetNets, HAPS and terrestrial network tiers can share the same spectrum, forming harmonized spectrum vHetNets that enhance spectral efficiency (SE). However, harmonized spectrum vHetNets face major challenges, including severe co-channel interference and scalability in large-scale deployments. To address the first challenge, we adopt a cell-free multiple-input multiple-output (MIMO) network architecture in which users are simultaneously served by multiple base stations using beamforming. However, beamforming weight design leads to a nonconvex, high-dimensional optimization problem, highlighting the scalability challenge. To address this second challenge, we develop a two-level distributed proportional fairness beamforming weight design (PFBWD) algorithm. This algorithm combines the augmented Lagrangian method (ALM) with a three-block ADMM framework. Simulation results demonstrate the performance improvements achieved by integrating HAPS with standalone terrestrial networks, as well as the reduced complexity and signaling overhead of the distributed algorithm compared to centralized algorithms.

Chaorong Zhang, Benjamin K. Ng, Hui Xu et al.

High-mobility wireless communication systems suffer from severe Doppler spread and multi-path delay, which degrade the reliability and spectral efficiency of conventional modulation schemes. Orthogonal time frequency space (OTFS) modulation offers strong robustness in such environments by representing symbols in the delay-Doppler (DD) domain, while faster-than-Nyquist (FTN) signaling can further enhance spectral efficiency through intentional symbol packing. Meanwhile, reconfigurable intelligent surfaces (RIS) provide a promising means to improve link quality via passive beamforming. Motivated by these advantages, we propose a novel RIS-empowered OTFS modulation with FTN signaling (RIS-OTFS-FTN) scheme. First, we establish a unified DD-domain input-output relationship that jointly accounts for RIS passive beamforming, FTN-induced inter-symbol interference, and DD-domain channel characteristics. Based on this model, we provide comprehensive analytical performance for the frame error rate, spectral efficiency, and peak-to-average power ratio (PAPR), etc. Furthermore, a practical RIS phase adjustment strategy with quantized phase selection is designed to maximize the effective channel gain. Extensive Monte Carlo simulations under a standardized extended vehicular A (EVA) channel model validate the theoretical results and provide key insights into the trade-offs among spectral efficiency, PAPR, input back-off (IBO), and error performance, with some interesting insights.The proposed RIS-OTFS-FTN scheme demonstrates notable performance gains in both reliability and spectral efficiency, offering a viable solution for future high-mobility and spectrum-constrained wireless systems.

Lavanya S S Anjapuli, Animesh Yadav, Halim Yanikomeroglu

In terrestrial networks, especially in urban areas, cell-edge users often face significant capacity limitations due to high path loss, shadowing, and inter-cell interference (ICI). This paper proposes integrating a high-altitude platform station (HAPS) into terrestrial networks, where terrestrial base stations (BS) can alleviate these issues by relaying data intended for cell-edge users via HAPS, thereby leveraging line-of-sight (LoS) links. We formulate an energy-efficiency (EE) maximization problem to jointly design beamforming vectors at the BS and HAPS with the goal of improving cell-edge user performance. Since the resulting problem is non-convex, we develop an online optimization framework based on a graph neural networks (GNN), which effectively captures the network topology. Numerical results show that the proposed HAPS-assisted architecture improves network performance, particularly by increasing the 5th-percentile EE, thereby enhancing service for cell-edge users.

Rawan Alkurd, Ibrahim Abualhaol, Halim Yanikomeroglu

The design and optimization of wireless networks have mostly been based on strong mathematical and theoretical modeling. Nonetheless, as novel applications emerge in the era of 5G and beyond, unprecedented levels of complexity will be encountered in the design and optimization of the network. As a result, the use of Artificial Intelligence (AI) is envisioned for wireless network design and optimization due to the flexibility and adaptability it offers in solving extremely complex problems in real-time. One of the main future applications of AI is enabling user-level personalization for numerous use cases. AI will revolutionize the way we interact with computers in which computers will be able to sense commands and emotions from humans in a non-intrusive manner, making the entire process transparent to users. By leveraging this capability, and accelerated by the advances in computing technologies, wireless networks can be redesigned to enable the personalization of network services to the user level in real-time. While current wireless networks are being optimized to achieve a predefined set of quality requirements, the personalization technology advocated in this article is supported by an intelligent big data-driven layer designed to micro-manage the scarce network resources. This layer provides the intelligence required to decide the necessary service quality that achieves the target satisfaction level for each user. Due to its dynamic and flexible design, personalized networks are expected to achieve unprecedented improvements in optimizing two contradicting objectives in wireless networks: saving resources and improving user satisfaction levels.

Rawan Alkurd, Ibrahim Abualhaol, Halim Yanikomeroglu

Current communication networks use design methodologies that prevent the realization of maximum network efficiency. In the first place, while users' perception of satisfactory service diverges widely, current networks are designed to be a "universal fit," where they are generally over-engineered to deliver services appealing to all types of users. Also, current networks lack user-level data cognitive intelligence that would enable fast personalized network decisions and actions through automation. Thus, in this article, we propose the utilization of AI, big data analytics, and real-time non-intrusive user feedback in order to enable the personalization of wireless networks. Based on each user's actual QoS requirements and context, a multi-objective formulation enables the network to micro-manage and optimize the provided QoS and user satisfaction levels simultaneously. Moreover, in order to enable user feedback tracking and measurement, we propose a user satisfaction model based on the zone of tolerance concept. Furthermore, we propose a big-data-driven and AI-based personalization framework to integrate personalization into wireless networks. Finally, we implement a personalized network prototype to demonstrate the proposed personalization concept and its potential benefits through a case study. The case study shows how personalization can be realized to enable the efficient optimization of network resources such that certain requirement levels of user satisfaction and revenue in the form of saved resources are achieved.

Dong Chu, Wael Jaafar, Halim Yanikomeroglu

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets.

Amin Farajzadeh, Animesh Yadav, Omid Abbasi et al.

We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithm to achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problem is non-convex; thus, we propose an iterative algorithm to solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.

Weili Wang, Omid Abbasi, Halim Yanikomeroglu et al.

Vertical heterogenous networks (VHetNets) and artificial intelligence (AI) play critical roles in 6G and beyond networks. This article presents an AI-native VHetNets architecture to enable the synergy of VHetNets and AI, thereby supporting varieties of AI services while facilitating automatic and intelligent network management. Anomaly detection in Internet of Things (IoT) is a major AI service required by many fields, including intrusion detection, state monitoring, device-activity analysis, security supervision and so on. Conventional anomaly detection technologies mainly consider the anomaly detection as a standalone service that is independent of any other network management functionalities, which cannot be used directly in ubiquitous IoT due to the resource constrained end nodes and decentralized data distribution. In this article, we develop an AI-native VHetNets-enabled framework to provide the anomaly detection service for ubiquitous IoT, whose implementation is assisted by intelligent network management functionalities. We first discuss the possibilities of VHetNets used for distributed AI model training to provide anomaly detection service for ubiquitous IoT, i.e., VHetNets for AI. After that, we study the application of AI approaches in helping provide automatic and intelligent network management functionalities for VHetNets, i.e., AI for VHetNets, whose aim is to facilitate the efficient implementation of anomaly detection service. Finally, a case study is presented to demonstrate the efficiency and effectiveness of the proposed AI-native VHetNets-enabled anomaly detection framework.

Afsoon Alidadi Shamsabadi, Animesh Yadav, Halim Yanikomeroglu

High Altitude Platform Stations (HAPS) have emerged as a promising enabler for next-generation wireless networks, offering ubiquitous connectivity to ground users. Operating either in standalone mode or in integration with terrestrial networks, HAPS can significantly enhance both coverage and capacity due to their strategic placement in the stratosphere. However, interference management in HAPS-empowered networks requires special attention due to the unique propagation characteristics of HAPS links. In particular, the strong line-of-sight (LoS) conditions between HAPS and ground users result in limited channel variability, thereby intensifying inter-user interference. In this work, we consider a single HAPS serving multiple ground users through multiple beams over a limited number of orthogonal resource blocks (RBs). To address the resulting interference, we propose a novel angular-aware user clustering and interference-aware RB allocation framework that strategically clusters users, designs beams to serve each cluster, and allocates RBs to users across clusters. To further mitigate intra-RB interference, a rate-splitting multiple access (RSMA) scheme is incorporated. Simulation results demonstrate that the proposed clustering and RSMA-based approach significantly outperforms baseline schemes in terms of achievable per-user spectral efficiency.

Youssra Cheriguene, Wael Jaafar, Chaker Abdelaziz Kerrache et al.

Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers. However, due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. Consequently, we investigate in this paper the problem of selecting UAV participants for edge FL, aiming to improve the FL model's accuracy, under UAV constraints of energy consumption, communication quality, and local datasets' heterogeneity. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS), which consists of selecting the best FL participant in each sub-region based on the structural similarity index measure (SSIM) average score of its local dataset and its power consumption profile. Through experiments, we demonstrate that the proposed selection scheme is superior to the benchmark random selection method, in terms of model accuracy, training time, and UAV energy consumption.

Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu

In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of crafting such schemes hinges on our ability to accurately characterize spectrum demand patterns across space and time. This paper presents a data-driven methodology for estimating spectrum demand variations over space and identifying key drivers of these variations in the mobile broadband landscape. By leveraging geospatial analytics and machine learning, the methodology is applied to a case study in Canada to estimate spectrum demand dynamics in urban regions. Our proposed model captures 70\% of the variability in spectrum demand when trained on one urban area and tested on another. These insights empower regulators to navigate the complexities of 6G networks and devise effective policies to meet future network demands.

Berk Çiloğlu, Görkem Berkay Koç, Afsoon Alidadi Shamsabadi et al.

Generative-AI (GenAI), a novel technology capable of producing various types of outputs, including text, images, and videos, offers significant potential for wireless communications. This article introduces the concept of strategic demand-planning through demand-labeling, demand-shaping, and demand-rescheduling. Accordingly, GenAI is proposed as a powerful tool to facilitate demand-shaping in wireless networks. More specifically, GenAI is used to compress and convert the content of various types (e.g., from a higher bandwidth mode to a lower one, such as from a video to text), which subsequently enhances performance of wireless networks in various usage scenarios, such as cell-switching, user association and load balancing, interference management, as well as disasters and unusual gatherings. Therefore, GenAI can serve a function in saving energy and spectrum in wireless networks. With recent advancements in AI, including sophisticated algorithms like large language models and the development of more powerful hardware built exclusively for AI tasks, such as AI accelerators, the concept of demand-planning, particularly demand-shaping through GenAI, becomes increasingly relevant. Furthermore, recent efforts to make GenAI accessible on devices, such as user terminals, make the implementation of this concept even more straightforward and feasible.

Afsoon Alidadi Shamsabadi, Cosmas Mwaba, Thomas Nugent et al.

Advanced Air Mobility (AAM) has emerged as a key pillar of next-generation transportation systems, encompassing a wide range of uncrewed aerial vehicle (UAV) applications. To enable AAM, maintaining reliable and efficient communication links between UAVs and control centers is essential. At the same time, the highly dynamic nature of wireless networks, combined with the limited onboard energy of UAVs, makes efficient trajectory planning and network association crucial. Existing terrestrial networks often fail to provide ubiquitous coverage due to frequent handovers and coverage gaps. To address these challenges, geostationary Earth orbit (GEO) satellites offer a promising complementary solution for extending UAV connectivity beyond terrestrial boundaries. This work proposes an integrated GEO terrestrial network architecture to ensure seamless UAV connectivity. Leveraging artificial intelligence (AI), a deep Q network (DQN) based algorithm is developed for joint UAV trajectory and association planning (JUTAP), aiming to minimize energy consumption, handover frequency, and disconnectivity. Simulation results validate the effectiveness of the proposed algorithm within the integrated GEO terrestrial framework.

Maryam Salamatmoghadasi, Metin Ozturk, Halim Yanikomeroglu

This paper proposes a two phase framework to improve the sustainability in vertical heterogeneous networks that integrate various types of base stations~(BSs), including terrestrial macro BSs~(MBSs), small BSs~(SBSs), and a high altitude platform station super MBS (HAPS SMBS). In Phase I, we address the critical and often overlooked challenge of estimating the traffic load of sleeping SBSs, a prerequisite for practical cell switching, by introducing three methods with varying data dependencies: (i) a distance based estimator (no historical data), (ii) a multi level clustering (MLC) estimator (limited historical data), and (iii) a long short term memory~(LSTM) based temporal predictor (full historical data). In Phase II, we incorporate the most accurate estimation results from Phase I into a renewable energy aware cell switching strategy, explicitly modeling solar powered SBSs in three operational scenarios that reflect realistic hybrid grid renewable deployments. This flexible design allows the framework to adapt switching strategies based on renewable availability and storage conditions, making it more practical and robust for real world networks. Using a real call detail record dataset from Milan, simulation results show that the LSTM method achieves a mean absolute percentage error (MAPE) below 1% in Phase I, while in Phase II, the threshold based solar integration scenario achieves up to 23% network energy saving (NES) relative to conventional cell switching. Overall, the proposed framework bridges the gap between theoretical cell switching models and practical, sustainable 6G radio access network~(RAN) operation, enabling significant energy saving without compromising quality of service.

Zijiang Yan, Hao Zhou, Wael Jaafar et al.

Uncrewed aerial vehicles (UAVs) are increasingly deployed in complex networked environments, yet the joint optimization of multi-UAV motion control and connectivity remains a fundamental challenge. In this paper, we study a multi-UAV system operating in an integrated terrestrial and non-terrestrial network (ITNTN) comprising terrestrial base stations and high-altitude platform stations (HAPS). We consider a three-dimensional (3D) aerial highway scenario where UAVs must adapt their motion to ensure collision avoidance, efficient traffic flow, and reliable communication under dynamic and partially observable conditions. We first model the problem as a hierarchical multi-objective partially observable Markov decision process (H-MO-POMDP), capturing the coupling between control and communication objectives. Based on this formulation, we propose a large language model (LLM)-driven hierarchical multi-rate control framework. At the global level, an LLM-based controller on the HAPS performs long-term planning for load balancing and handover decisions. At the local level, each UAV employs a hybrid controller that integrates a slow-timescale LLM for high-level spatial reasoning with a reinforcement learning agent for faster UAV-to-infrastructure (U2I) communication and motion control. We further develop a high-fidelity 3D simulation platform by integrating the gym-pybullet-drones environment with 3GPP-compliant RF/THz channel models. Numerical results demonstrate that the proposed framework significantly outperforms state-of-the-art baselines, achieving a 14% increase in transportation efficiency and a 25% improvement in telecommunication throughput. Additionally, it achieves a 23% reduction in physical collision rates, demonstrating strong handover stability and zero-shot generalization in dynamic scenarios.

Bilal Karaman, Ilhan Basturk, Engin Zeydan et al.

Reliable and resilient communication is essential for disaster recovery and emergency response, yet terrestrial infrastructure often fails during large-scale natural disasters. This paper proposes a High-Altitude Platform Station (HAPS) and Reconfigurable Intelligent Surfaces (RIS)-assisted Internet of Things (IoT) communication system to restore connectivity in disaster-affected areas. Distributed IoT sensors collect critical environmental data and forward it to nearby gateways via short-range links, while the HAPS-RIS system provides backhaul to these gateways. To overcome the severe double path loss of passive RIS at high altitudes, we propose a dynamically adjustable sub-connected active RIS architecture that can reconfigure the number of elements connected to each power amplifier through switching mechanisms. Simulation results demonstrate substantial gains in downlink and uplink data rates, as well as system energy efficiency, compared with conventional passive RIS schemes. Moreover, a 1 dB increase in ground-station transmit power yields approximately 20-30 Mbps improvement in gateway data rates. These findings confirm that HAPS-RIS technology offers an effective and energy-efficient approach for resilient IoT backhaul in 6G non-terrestrial networks, particularly in line-of-sight (LoS)-dominant HAPS-ground backhaul scenarios.

Noha Hassan, Xavier Fernando, Halim Yanikomeroglu

Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobility. Here we propose a hierarchical multi-objective quantum metalearning algorithm that switches among specific quantum paths based on historical success, energy cost, and current data rate. Candidate RIS control directions are arranged as switch paths between quantum neural network layers to minimize inference, and a scoring mechanism selects the top performing paths per layer. Instead of merely storing past successful settings of the RIS and picking the closest match when a new problem is encountered, the algorithm learns how to select and recombine the best parts of different solutions to solve new scenarios. In our model, high-dimensional RIS scenario features are compressed into a quantum state using the tensor product, then superimposed during quantum path selection, significantly improving quantum computational advantage. Results demonstrate efficient performance with enhanced spectral efficiency, convergence rate, and adaptability.

Noha Hassan, Xavier Fernando, Halim Yanikomeroglu

As a key enabler for sixth-generation (6G) wireless communications, reconfigurable intelligent surfaces (RISs) provide the flexibility to control signal strength. Nevertheless, optimizing hundreds of elements is computationally expensive. To overcome this challenge, we present a quantum framework (QGCN) to jointly optimize the physical and electromagnetic response of a double-sided RIS design that incorporates discrete phase shifts and inter-element coupling. The core contribution is the adaptive activation or deactivation of elements, allowing a virtual spacing mechanism using PIN diode switches. We then solve a multi-objective problem that maximizes the minimum user data rate subject to constraints on aperture length and mutual coupling between active elements. Experimental results on IBM Quantum's 127-qubit ibm_kyiv superconducting processor demonstrate that the proposed QGCN algorithm reduces both per-iteration computational complexity and memory requirements compared to existing approaches. Also, the QGCN outperforms classical graph neural networks (GNN) on an equivalent graph topology by an additional $+$0.38 bps/Hz. This advantage is increasing with increasing array sizes.

Mohammed Mahyoub, Wael Jaafar, Sami Muhaidat et al.

We address cross-slice co-location risk in multi-slice low Earth orbit (LEO) satellite edge networks, where virtual network functions (VNFs) from different network slices sharing the same satellite instance create a cross-slice security exposure channel. We formulate a risk-aware service function chain (SFC) placement problem as a mixed-integer linear program (MILP) over a dynamically evolving LEO satellite constellation, jointly optimizing cross-slice co-location risk, CPU resource consumption, and VNF migration stability under satellite capacity, inter-satellite link (ISL) capacity, visibility, and end-to-end (E2E) delay constraints. The risk model employs a multiplicative co-location formulation, inspired by the risk assessment principles from ISO/NIST frameworks, with exact and coarse (slice-level)formulations that analytically establish bounds on the co-location exposure. To solve this problem, we propose a three-stage hybrid optimizer combining time epoch preprocessing, simulated annealing-based warm-start, and branch-and-bound refinement. Experimental evaluation demonstrates a 40% reduction in co-location risk and an 80% reduction in avoidable VNF migrations relative to the greedy baseline at negligible CPU overhead, and a 23x warm-start speedup from 256s cold-start to 11s per epoch, confirming real-time viability from the second epoch.

Mohamad Alkadamani, Halim Yanikomeroglu, Amir Ghasemi

The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR-GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR-GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability.

Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu

The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more efficient spectrum management. Spectrum sharing is a promising approach; however, regulators need accurate methods to characterize demand dynamics and guide allocation decisions. This paper builds and validates a spectrum demand proxy from public deployment records and uses a graph attention network in a hierarchical, multi-resolution setup (HR-GAT) to estimate spectrum demand at fine spatial scales. The model captures both neighborhood effects and cross-scale patterns, reducing spatial autocorrelation and improving generalization. Evaluated across five Canadian cities and against eight competitive baselines, HR-GAT reduces median RMSE by roughly 21% relative to the best alternative and lowers residual spatial bias. The resulting demand maps are regulator-accessible and support spectrum sharing and spectrum allocation in wireless networks.

Mohamad Alkadamani, Colin Brown, Halim Yanikomeroglu

Accurate spatial prediction of cellular traffic demand is essential for 5G NR capacity planning, network densification, and data-driven 6G planning. Although machine learning can fuse heterogeneous geospatial and socio-economic layers to estimate fine-grained demand maps, spatial autocorrelation can cause neighborhood leakage under naive train/test splits, inflating accuracy and weakening planning reliability. This paper presents an AI-driven framework that reduces leakage and improves spatial generalization via a context-aware two-stage splitting strategy with residual spatial error correction. Experiments using crowdsourced usage indicators across five major Canadian cities show consistent mean absolute error (MAE) reductions relative to location-only clustering, supporting more reliable bandwidth provisioning and evidence-based spectrum planning and sharing assessments.

Colin Brown, Mohamad Alkadamani, Halim Yanikomeroglu

Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile network operators and regulators face increasing challenges in ensuring adequate spectrum availability. This paper presents a data-driven approach leveraging artificial intelligence (AI) and machine learning (ML) to estimate and manage spectrum demand. The approach uses multiple proxies of spectrum demand, drawing from site license data and derived from crowdsourced data. These proxies are validated against real-world mobile network traffic data to ensure reliability, achieving an R$^2$ value of 0.89 for an enhanced proxy. The proposed ML models are tested and validated across five major Canadian cities, demonstrating their generalizability and robustness. These contributions assist spectrum regulators in dynamic spectrum planning, enabling better resource allocation and policy adjustments to meet future network demands.

Youssra Cheriguene, Wael Jaafar, Halim Yanikomeroglu et al.

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.

Nesrine Cherif, Wael Jaafar, Halim Yanikomeroglu et al.

Unmanned aerial vehicle (UAV) is a promising technology for last-mile cargo delivery. However, the limited on-board battery capacity, cellular unreliability, and frequent handoffs in the airspace are the main obstacles to unleash its full potential. Given that existing cellular networks were primarily designed to service ground users, re-utilizing the same architecture for highly mobile aerial users, e.g., cargo-UAVs, is deemed challenging. Indeed, to ensure a safe delivery using cargo-UAVs, it is crucial to utilize the available energy efficiently, while guaranteeing reliable connectivity for command-and-control and avoiding frequent handoff. To achieve this goal, we propose a novel approach for joint cargo-UAV trajectory planning and cell association. Specifically, we formulate the cargo-UAV mission as a multi-objective problem aiming to 1) minimize energy consumption, 2) reduce handoff events, and 3) guarantee cellular reliability along the trajectory. We leverage reinforcement learning (RL) to jointly optimize the cargo-UAV's trajectory and cell association. Simulation results demonstrate a performance improvement of our proposed method, in terms of handoffs, disconnectivity, and energy consumption, compared to benchmarks.

Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu

Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from 2-D obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics.

Berk Çiloğlu, Görkem Berkay Koç, Metin Ozturk et al.

This study aims to introduce the cell load estimation problem of cell switching approaches in cellular networks specially-presented in a high-altitude platform station (HAPS)-assisted network. The problem arises from the fact that the traffic loads of sleeping base stations for the next time slot cannot be perfectly known, but they can rather be estimated, and any estimation error could result in divergence from the optimal decision, which subsequently affects the performance of energy efficiency. The traffic loads of the sleeping base stations for the next time slot are required because the switching decisions are made proactively in the current time slot. Two different Q-learning algorithms are developed; one is full-scale, focusing solely on the performance, while the other one is lightweight and addresses the computational cost. Results confirm that the estimation error is capable of changing cell switching decisions that yields performance divergence compared to no-error scenarios. Moreover, the developed Q-learning algorithms perform well since an insignificant difference (i.e., 0.3%) is observed between them and the optimum algorithm.

Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu

Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx). Accurate path loss predictions can optimize use of the radio frequency spectrum and minimize unwanted interference. Modern path loss modeling often leverages data-driven approaches, using machine learning to train models on drive test measurement datasets. Drive tests primarily represent downlink scenarios, where the Tx is located on a building and the Rx is located on a moving vehicle. Consequently, trained models are frequently reserved for downlink coverage estimation, lacking representation of uplink scenarios. In this paper, we demonstrate that data augmentation can be used to train a path loss model that is generalized to uplink, downlink, and backhaul scenarios, training using only downlink drive test measurements. By adding a small number of synthetic samples representing uplink scenarios to the training set, root mean squared error is reduced by > 8 dB on uplink examples in the test set.

Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu

The deployment of federated learning (FL) in non-terrestrial networks (NTN) that are supported by high-altitude platform stations (HAPS) offers numerous advantages. Due to its large footprint, it facilitates interaction with a large number of line-of-sight (LoS) ground clients, each possessing diverse datasets along with distinct communication and computational capabilities. The presence of many clients enhances the accuracy of the FL model and speeds up convergence. However, the variety of datasets among these clients poses a significant challenge, as it leads to pervasive non-independent and identically distributed (non-IID) data. The data non-IIDness results in markedly reduced training accuracy and slower convergence rates. To address this issue, we propose a novel weighted attribute-based client selection strategy that leverages multiple user-specific attributes, including historical traffic patterns, instantaneous channel conditions, computational capabilities, and previous-round learning performance. By combining these attributes into a composite score for each user at every FL round and selecting users with higher scores as FL clients, the framework ensures more uniform and representative data distributions, effectively mitigating the adverse effects of non-IID data. Simulation results corroborate the effectiveness of the proposed client selection strategy in enhancing FL model accuracy and convergence rate, as well as reducing training loss, by effectively addressing the critical challenge of data non-IIDness in large-scale FL system implementations.

Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu

Non-terrestrial networks (NTNs) are emerging as a core component of future 6G communication systems, providing global connectivity and supporting data-intensive applications. In this paper, we propose a distributed hierarchical federated learning (HFL) framework within the NTN architecture, leveraging a high altitude platform station (HAPS) constellation as intermediate distributed FL servers. Our framework integrates both low-Earth orbit (LEO) satellites and ground clients in the FL training process while utilizing geostationary orbit (GEO) and medium-Earth orbit (MEO) satellites as relays to exchange FL global models across other HAPS constellations worldwide, enabling seamless, global-scale learning. The proposed framework offers several key benefits: (i) enhanced privacy through the decentralization of the FL mechanism by leveraging the HAPS constellation, (ii) improved model accuracy and reduced training loss while balancing latency, (iii) increased scalability of FL systems through ubiquitous connectivity by utilizing MEO and GEO satellites, and (iv) the ability to use FL data, such as resource utilization metrics, to further optimize the NTN architecture from a network management perspective. A numerical study demonstrates the proposed framework's effectiveness, with improved model accuracy, reduced training loss, and efficient latency management. The article also includes a brief review of FL in NTNs and highlights key challenges and future research directions.

Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu

Path loss prediction is a beneficial tool for efficient use of the radio frequency spectrum. Building on prior research on high-resolution map-based path loss models, this paper studies convolutional neural network input representations in more detail. We investigate different methods of representing scalar features in convolutional neural networks. Specifically, we compare using frequency and distance as input channels to convolutional layers or as scalar inputs to regression layers. We assess model performance using three different feature configurations and find that representing scalar features as image channels results in the strongest generalization.

Amin Farajzadeh, Hongzhao Zheng, Sarah Dumoulin et al.

Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication networks. It supports governmental efforts, particularly those led by the international telecommunication union (ITU), to establish fair spectrum allocation policies, improve auction mechanisms, and meet the requirements of emerging technologies such as advanced 5G, forthcoming 6G, and the internet of things (IoT). This paper presents an effective spatio-temporal prediction framework that leverages crowdsourced user-side key performance indicators (KPIs) and regulatory datasets to model and forecast spectrum demand. The proposed methodology achieves superior prediction accuracy and cross-regional generalizability by incorporating advanced feature engineering, comprehensive correlation analysis, and transfer learning techniques. Unlike traditional ITU models, which are often constrained by arbitrary inputs and unrealistic assumptions, this approach exploits granular, data-driven insights to account for spatial and temporal variations in spectrum utilization. Comparative evaluations against ITU estimates, as the benchmark, underscore our framework's capability to deliver more realistic and actionable predictions. Experimental results validate the efficacy of our methodology, highlighting its potential as a robust approach for policymakers and regulatory bodies to enhance spectrum management and planning.

Ahmet Melih Ince, Ayse Elif Canbilen, Halim Yanikomeroglu

Sixth-generation (6G) networks are designed to meet the hyper-reliable and low-latency communication (HRLLC) requirements of safety-critical applications such as autonomous driving. Integrating non-terrestrial networks (NTN) into the 6G infrastructure brings redundancy to the network, ensuring continuity of communications even under extreme conditions. In particular, high-altitude platform stations (HAPS) stand out for their wide coverage and low latency advantages, supporting communication reliability and enhancing information freshness, especially in rural areas and regions with infrastructure constraints. In this paper, we present reinforcement learning-based approaches using deep deterministic policy gradient (DDPG) to dynamically optimize the age-of-information (AoI) in HAPS-enabled vehicle-to-everything (V2X) networks. The proposed method improves information freshness and overall network reliability by enabling independent learning without centralized coordination. The findings reveal the potential of HAPS-supported solutions, combined with DDPG-based learning, for efficient AoI-aware resource allocation in platoon-based autonomous vehicle systems.

Mustafa Ghaleb, Mohanad Obeed, Muhamad Felemban et al.

Federated Learning (FL) has emerged as a significant paradigm for training machine learning models. This is due to its data-privacy-preserving property and its efficient exploitation of distributed computational resources. This is achieved by conducting the training process in parallel at distributed users. However, traditional FL strategies grapple with difficulties in evaluating the quality of received models, handling unbalanced models, and reducing the impact of detrimental models. To resolve these problems, we introduce a novel federated learning framework, which we call federated testing for federated learning (FedTest). In the FedTest method, the local data of a specific user is used to train the model of that user and test the models of the other users. This approach enables users to test each other's models and determine an accurate score for each. This score can then be used to aggregate the models efficiently and identify any malicious ones. Our numerical results reveal that the proposed method not only accelerates convergence rates but also diminishes the potential influence of malicious users. This significantly enhances the overall efficiency and robustness of FL systems.

Amin Farajzadeh, Animesh Yadav, Halim Yanikomeroglu

In the ever-expanding landscape of the IoT, managing the intricate network of interconnected devices presents a fundamental challenge. This leads us to ask: "What if we invite the IoT devices to collaboratively participate in real-time network management and IoT data-handling decisions?" This inquiry forms the foundation of our innovative approach, addressing the burgeoning complexities in IoT through the integration of NTN architecture, in particular, VHetNet, and an MT-HFL framework. VHetNets transcend traditional network paradigms by harmonizing terrestrial and non-terrestrial elements, thus ensuring expansive connectivity and resilience, especially crucial in areas with limited terrestrial infrastructure. The incorporation of MT-HFL further revolutionizes this architecture, distributing intelligent data processing across a multi-tiered network spectrum, from edge devices on the ground to aerial platforms and satellites above. This study explores MT-HFL's role in fostering a decentralized, collaborative learning environment, enabling IoT devices to not only contribute but also make informed decisions in network management. This methodology adeptly handles the challenges posed by the non-IID nature of IoT data and efficiently curtails communication overheads prevalent in extensive IoT networks. Significantly, MT-HFL enhances data privacy, a paramount aspect in IoT ecosystems, by facilitating local data processing and limiting the sharing of model updates instead of raw data. By evaluating a case-study, our findings demonstrate that the synergistic integration of MT-HFL within VHetNets creates an intelligent network architecture that is robust, scalable, and dynamically adaptive to the ever-changing demands of IoT environments. This setup ensures efficient data handling, advanced privacy and security measures, and responsive adaptability to fluctuating network conditions.

Yucel Aydin, Gunes K. Kurt, Enver Ozdemir et al.

Drones are begin used for various purposes such as border security, surveillance, cargo delivery, visual shows and it is not possible to overcome such intensive tasks with a single drone. In order to expedite performing such tasks, drone swarms are employed. The number of drones in a swarm can be high depending on the assigned duty. The current solution to authenticate a single drone using a 5G new radio (NR) network requires the execution of two steps. The first step covers the authentication between a drone and the 5G core network, and the second step is the authentication between the drone and the drone control station. It is not feasible to authenticate each drone in a swarm with the current solution without causing a significant latency. Authentication keys between a base station (BS) and a user equipment (UE) must be shared with the new BS while performing handover. The drone swarms are heavily mobile and require several handovers from BS to a new BS. Sharing authentication keys for each drone as explained in 5G NR is not scalable for the drone swarms. Also, the drones can be used as a UE or a radio access node on board unmanned aerial vehicle (UxNB). A UxNB may provide service to a drone swarm in a rural area or emergency. The number of handovers may increase and the process of sharing authentication keys between UxNB to new UxNB may be vulnerable to eavesdropping due to the wireless connectivity. In this work, we present a method where the time and the number of the communication for the authentication of a new drone joining the swarm are less than 5G NR. In addition, group-based handover solutions for the scenarios in which the base stations are terrestrial or mobile are proposed to overcome the scalability and latency issues in the 5G NR.

Nizar Masmoudi, Wael Jaafar, Safa Cherif et al.

To cope with the current pandemic situation and reinstate pseudo-normal daily life, several measures have been deployed and maintained, such as mask wearing, social distancing, hands sanitizing, etc. Since outdoor cultural events, concerts, and picnics, are gradually allowed, a close monitoring of the crowd activity is needed to avoid undesired contact and disease transmission. In this context, intelligent unmanned aerial vehicles (UAVs) can be occasionally deployed to ensure the surveillance of these activities, that health restriction measures are applied, and to trigger alerts when the latter are not respected. Consequently, we propose in this paper a complete UAV framework for intelligent monitoring of post COVID-19 outdoor activities. Specifically, we propose a three steps approach. In the first step, captured images by a UAV are analyzed using machine learning to detect and locate individuals. The second step consists of a novel coordinates mapping approach to evaluate distances among individuals, then cluster them, while the third step provides an energy-efficient and/or reliable UAV trajectory to inspect clusters for restrictions violation such as mask wearing. Obtained results provide the following insights: 1) Efficient detection of individuals depends on the angle from which the image was captured, 2) coordinates mapping is very sensitive to the estimation error in individuals' bounding boxes, and 3) UAV trajectory design algorithm 2-Opt is recommended for practical real-time deployments due to its low-complexity and near-optimal performance.

Yucel Aydin, Gunes Karabulur Kurt, Enver Ozdemir et al.

In parallel with the advances of aerial networks, the use of drones is quickly included in daily activities. According to the characteristics of the operations to be carried out using the drones, the need for simultaneous use of one or more drones has arisen. The use of a drone swarm is preferred rather than the use of a single drone to complete activities such as secure crowd monitoring systems, cargo delivery. Due to the limited airtime of the drones, new members may be included in the swarm, or there may be a unification of two or more drone swarms when needed. Authentication of the new drone that will take its place in the drone swarm and the rapid mutual-verification of two different swarms of drones are some of the security issues in the swarm structures. In this study, group authentication-based solutions have been put forward to solve the identified security issues. The proposed methods and 5G new radio (NR) authentication methods were compared in terms of time and a significant time difference was obtained. According to the 5G NR standard, it takes 22 ms for a user equipment (UE) to be verified by unified data management (UDM), while in the proposed method, this time varies according to the threshold value of the polynomial used and it is substantially lower than 22 ms for most threshold values.

Caner Goztepe, Saliha Buyukcorak, Gunes Karabulut Kurt et al.

The impact of localization systems in our daily lives is increasing. As next-generation networks will introduce hyperconnectivity with the emerging applications, this impact will undoubtedly further increase, proliferating the importance of the location information's reliability. As society becomes more dependent on this information in terms of the products and services, security solutions will have to be enriched to provide countermeasures sufficiently advanced to ever-evolving threats, forcing the joint design of communication and localization systems. This paper envisions integrated communication and localization systems by focusing on localization security. Also, conventional and next-generation attacks on localization are discussed along with an efficient attack detection method and test-bed-based demonstration, highlighting the need for effective countermeasures.

Ozan Alp Topal, Gunes Karabulut Kurt, Halim Yanikomeroglu

In this work, we propose a secret key generation procedure specifically designed for the inter-spacecraft communication links. As a novel secrecy source, the spacecrafts utilize Doppler frequency shift based measurements. In this way, the mobilities of the communication devices are exploited to generate secret keys, where this resource can be utilized in the environments that the channel fading based key generation methods are not available. The mobility of a spacecraft is modeled as the superposition of a pre-determined component and a dynamic component. We derive the maximum achievable secret key generation rate from the Doppler frequency shift. The proposed secret key generation procedure extracts the Doppler frequency shift in the form of nominal power spectral density samples (NPSDS). We propose a maximum-likelihood (ML) estimation for the NPSDS at the spacecrafts, then a uniform quantizer is utilized to obtain secret key bits. The key disagreement rate (KDR) is analytically obtained for the proposed key generation procedure. Through numerical studies, the tightness of the provided approximations is shown. Both the theoretical and numerical results demonstrate the validity and the practicality of the presented physical layer key generation procedure considering the security of the communication links of spacecrafts.

Kürşat Tekbıyık, Güneş Karabulut Kurt, Ali Rıza Ekti et al.

Direct-to-satellite (DtS) communication has gained importance recently to support globally connected Internet of things (IoT) networks. However, relatively long distances of densely deployed satellite networks around the Earth cause a high path loss. In addition, since high complexity operations such as beamforming, tracking and equalization have to be performed in IoT devices partially, both the hardware complexity and the need for high-capacity batteries of IoT devices increase. The reconfigurable intelligent surfaces (RISs) have the potential to increase the energy-efficiency and to perform complex signal processing over the transmission environment instead of IoT devices. But, RISs need the information of the cascaded channel in order to change the phase of the incident signal. This study evaluates the pilot signal as a graph and incorporates this information into the graph attention networks (GATs) to track the phase relation through pilot signaling. The proposed GAT-based channel estimation method examines the performance of the DtS IoT networks for different RIS configurations to solve the challenging channel estimation problem. It is shown that the proposed GAT both demonstrates a higher performance with increased robustness under changing conditions and has lower computational complexity compared to conventional deep learning methods. Moreover, bit error rate performance is investigated for RIS designs with discrete and non-uniform phase shifts under channel estimation based on the proposed method. One of the findings in this study is that the channel models of the operating environment and the performance of the channel estimation method must be considered during RIS design to exploit performance improvement as far as possible.

Ahmet Emir, Ferdi Kara, Hakan Kaya et al.

In this letter, we propose a deep learning-aided multi-user detection (DeepMuD) in uplink non-orthogonal multiple access (NOMA) to empower the massive machine-type communication where an offline-trained Long Short-Term Memory (LSTM)-based network is used for multi-user detection. In the proposed DeepMuD, a perfect channel state information (CSI) is also not required since it is able to perform a joint channel estimation and multi-user detection with the pilot responses, where the pilot-to-frame ratio is very low. The proposed DeepMuD improves the error performance of the uplink NOMA significantly and outperforms the conventional detectors (even with perfect CSI). Moreover, this gain becomes superb with the increase in the number of Internet of Things (IoT) devices. Furthermore, the proposed DeepMuD has a flexible detection and regardless of the number of IoT devices, the multi-user detection can be performed. Thus, an arbitrary number of IoT devices can be served without a signaling overhead, which enables the grant-free communication.

Selen Gecgel, Caner Goztepe, Gunes Karabulut Kurt et al.

Communications are realized as a result of successive decisions at the physical layer, from modulation selection to multi-antenna strategy, and each decision affects the performance of the communication systems. Future communication systems must include extensive capabilities as they will encompass a wide variety of devices and applications. Conventional physical layer decision mechanisms may not meet these requirements, as they are often based on impractical and oversimplifying assumptions that result in a trade-off between complexity and efficiency. By leveraging past experiences, learning-driven designs are promising solutions to present a resilient decision mechanism and enable rapid response even under exceptional circumstances. The corresponding design solutions should evolve following the lines of learning-driven paradigms that offer more autonomy and robustness. This evolution must take place by considering the facts of real-world systems and without restraining assumptions. In this paper, the common assumptions in the physical layer are presented to highlight their discrepancies with practical systems. As a solution, learning algorithms are examined by considering the implementation steps and challenges. Furthermore, these issues are discussed through a real-time case study using software-defined radio nodes to demonstrate the potential performance improvement. A cyber-physical framework is presented to incorporate future remedies.

Yucel Aydin, Gunes Karabulut Kurt, Enver Ozdemir et al.

The widespread use of new technologies such as the Internet of things (IoT) and machine type communication(MTC) forces an increase on the number of user equipments(UEs) and MTC devices that are connecting to mobile networks. Inherently, as the number of UEs inside a base station's (BS) coverage area surges, the quality of service (QoS) tends to decline. The use of drone-mounted BS (UxNB) is a solution in places where UEs are densely populated, such as stadiums. UxNB emerges as a promising technology that can be used for capacity injection purposes in the future due to its fast deployment. However, this emerging technology introduces a new security issue. Mutual authentication, creating a communication channel between terrestrial BS and UxNB, and fast handover operations may cause security issues in the use of UxNB for capacity injection. This new protocol also suggests performing UE handover from terrestrial to UxNB as a group. To the best of the authors' knowledge, there is no authentication solution between BSs according to LTE and 5G standards. The proposed scheme provides a solution for the authentication of UxNB by the terrestrial BS. Additionally, a credential sharing phase for each UE in handover is not required in the proposed method. The absence of a credential sharing step saves resources by reducing the number of communications between BSs. Moreover, many UE handover operations are completed in concise time within the proposed group handover method.

Kürşat Tekbıyık, Güneş Karabulut Kurt, Ali Rıza Ekti et al.

Next-generation communication technology will be made possible by cooperation between terrestrial networks with non-terrestrial networks (NTN) comprised of high-altitude platform stations and satellites. Further, as humanity embarks on the long road to establish new habitats on other planets, cooperation between NTN and deep-space networks (DSN) will be necessary. In this regard, we propose the use of reconfigurable intelligent surfaces (RIS) to improve coordination between these networks given that RIS perfectly match the size, weight, and power restrictions of operating in space. A comprehensive framework of RIS-assisted non-terrestrial and interplanetary communications is presented that pinpoints challenges, use cases, and open issues. Furthermore, the performance of RIS-assisted NTN under environmental effects such as solar scintillation and satellite drag is discussed in light of simulation results.

Kürşat Tekbıyık, Güneş Karabulut Kurt, Chongwen Huang et al.

In this paper, graph attention network (GAT) is firstly utilized for the channel estimation. In accordance with the 6G expectations, we consider a high-altitude platform station (HAPS) mounted reconfigurable intelligent surface-assisted two-way communications and obtain a low overhead and a high normalized mean square error performance. The performance of the proposed method is investigated on the two-way backhauling link over the RIS-integrated HAPS. The simulation results denote that the GAT estimator overperforms the least square in full-duplex channel estimation. Contrary to the previously introduced methods, GAT at one of the nodes can separately estimate the cascaded channel coefficients. Thus, there is no need to use time-division duplex mode during pilot signaling in full-duplex communication. Moreover, it is shown that the GAT estimator is robust to hardware imperfections and changes in small-scale fading characteristics even if the training data do not include all these variations.

Mohammad G. Khoshkholgh, Halim Yanikomeroglu

We address how to exploit power control data, gathered from a monitored environment, for performing power control in an unexplored environment. We adopt offline deep reinforcement learning, whereby the agent learns the policy to produce the transmission powers solely by using the data. Experiments demonstrate that despite discrepancies between the monitored and unexplored environments, the agent successfully learns the power control very quickly, even if the objective functions in the monitored and unexplored environments are dissimilar. About one third of the collected data is sufficient to be of high-quality and the rest can be from any sub-optimal algorithm.

Mohammad G. Khoshkholgh, Halim Yanikomeroglu

Policy gradient reinforcement learning techniques enable an agent to directly learn an optimal action policy through the interactions with the environment. Nevertheless, despite its advantages, it sometimes suffers from slow convergence speed. Inspired by human decision making approach, we work toward enhancing its convergence speed by augmenting the agent to memorize and use the recently learned policies. We apply our method to the trust-region policy optimization (TRPO), primarily developed for locomotion tasks, and propose faded-experience (FE) TRPO. To substantiate its effectiveness, we adopt it to learn continuous power control in an interference channel when only noisy location information of devices is available. Results indicate that with FE-TRPO it is possible to almost double the learning speed compared to TRPO. Importantly, our method neither increases the learning complexity nor imposes performance loss.

Yucel Aydin, Gunes Karabulut Kurt, Enver Özdemir et al.

Internet of Things (IoT) networks are becoming a part of our daily lives, as the number of IoT devices around us are surging. The authentication of millions of connected things and the distribution and management of secret keys between these devices pose challenging research problems. Current one-to-one authentication schemes do not take the resource limitations of IoT devices into consideration. Nor do they address the scalability problem of massive machine type communication (mMTC) networks. Group authentication schemes (GAS), on the other hand, have emerged as novel approaches for many-to-many authentication problems. They can be used to simultaneously authenticate numerous resource-constrained devices. However, existing GAS are not energy efficient, and they do not provide enough security for widespread use. In this paper, we propose a lightweight GAS that significantly reduces energy consumption on devices, providing almost 80% energy savings when compared to the state-of-the-art solutions. Our approach is also resistant to the replay and man-in-the-middle attacks. The proposed approach also includes a solution for key agreement and key distribution problems in mMTC environments. Moreover, this approach can be used in both centralized and decentralized group authentication scenarios. The proposed approach has the potential to address the fast authentication requirements of the envisioned agile 6G networks, supported through aerial networking nodes.

Rozhina Ghanavi, Maryam Sabbaghian, Halim Yanikomeroglu

In this paper, we use an aerial base station (aerial-BS) to enhance fairness in a dynamic environment with user mobility. The problem of optimally placing the aerial-BS is a non-deterministic polynomial-time hard (NP-hard) problem. Moreover, the network topology is subject to continuous changes due to the user mobility. These issues intensify the quest to develop an adaptive and fast algorithm for 3D placement of the aerial-BS. To this end, we propose a method based on reinforcement learning to achieve these goals. Simulation results show that our method increases fairness among users in a reasonable computing time, while the solution is comparatively close to the optimal solution obtained by exhaustive search.