arXiv subjects

Safwan Alfattani

Publications and source records attributed to Safwan Alfattani.

14 recordsLinked to original sources

arXiv2026

RSMA Enabled Hierarchical UAV Networks with Non Linear Energy Harvesting: Outage Probability Analysis and UAV Placement Optimization

Uncrewed aerial vehicles (UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil applications. Although multi-UAV systems offer greater efficiency and cost-effectiveness than single-UAV deployments, their implementation still faces several fundamental challenges that limit their reliability, sustainability, and scalability. The limited onboard energy restricts mission duration and communication continuity. Therefore, wireless energy harvesting (EH) emerges as a promising solution to overcome this limitation. However, terrestrial energy sources experience path loss, making EH from surrounding UAVs more sustainable. Moreover, rate-splitting multiple access (RSMA) remains insufficiently explored in hierarchical UAV networks under hardware impairments (HWI) and imperfect channel state information (ICSI). This paper proposes a hierarchical ad hoc UAV network with non-linear EH and RSMA to enhance both energy and cost efficiency, where UAVs harvest energy from surrounding UAVs. For a practical scenario, we consider the effect of HWI and ICSI in our proposed system. To the best of the authors knowledge, this study is the first to investigate such a scenario in the literature. The outage probability expressions for ground Internet of things (IoT) devices, each CMU, and the overall outage probability of the proposed system are derived over Nakagami-$m$ fading channels while considering practical constraints such as HWI, ICSI, and non-linear EH. Additionally, approximate outage probability expressions are derived for high transmit power regimes. Subsequently, we formulate two optimization problems to enhance reliability and performance. Our findings indicate that the proposed system outperforms all benchmarks in terms of outage probability.

eess.SP↗

arXiv2026

Two-Tier High Altitude Platform Stations (HAPS) for Exploring Wireless Energy Harvesting

In sixth-generation (6G) cellular networks and beyond, aerial platforms, such as uncrewed aerial vehicles (UAVs) and high-altitude platform stations (HAPS), are anticipated to play a crucial role in enhancing connectivity, expanding network coverage, and supporting advanced communication services. However, the deployment of energy-efficient onboard communication systems is essential for their widespread adoption and effectiveness. The integration of energy harvesting (EH) into aerial platforms is envisioned to be pivotal in promoting both energy and cost efficiency. In this paper, we propose a new paradigm for aerial platforms in which they can collect energy from the transmitted signals of nearby aerial platforms. The paper employs a two-tier architecture with HAPS super-macro base stations (HAPS-SMBS) system: regular HAPS-SMBS nodes serve as base stations, while a "mother" HAPS-SMBS node acts as a manager to coordinate communications between regular HAPS-SMBS and the ground station, thus enabling wireless energy transfer. Specifically, we analyze the characteristics of EH-enabled HAPS-SMBS and compare their performance with those without EH. Additionally, we derive the optimal regular HAPS-SMBS positioning to mitigate signal attenuation and power loss. Subsequently, we formulate a joint optimization problem for regular HAPS-SMBS positioning and the EH factor. We solve the problem using the iterative distance and EH factor algorithm (IDFA); however, we employ $Q$-learning to verify its effectiveness. Our findings indicate that, compared to conventional EH systems, IDFA and $Q$-learning exhibit higher data rate performance. In contrast, $Q$-learning outperforms IDFA systems in linear modelswith intensive training in approximating optimal values. Furthermore, maximizing transmit power achieves higher gains than systems without EH.

eess.SP↗

arXiv2026

Outage Probability Analysis of NOMA Enabled Hierarchical UAV Networks with Non-Linear Energy Harvesting

Uncrewed aerial vehicles (UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil domains. Although multi-UAV systems enhance connectivity for IoT networks more than single-UAV systems, energy-efficient communication systems and the integration of energy harvesting (EH) are crucial for their widespread adoption and effectiveness. In this regard, this paper proposes a hierarchical ad hoc UAV network with non-linear EH and non-orthogonal multiple access (NOMA) to enhance both energy and cost efficiency. The proposed system consists of two UAV layers: a cluster head UAV (CHU), which acts as the source, and cluster member UAVs (CMUs), which serve as relays and are capable of harvesting energy from a terrestrial power beacon. For the considered IoT network architecture, the outage probability expressions of ground Internet of things (IoT) devices, each CMU, and the overall outage probability of the proposed system are derived over Nakagami-m fading channels with practical constraints such as hardware impairments and non-linear EH. We compare the proposed system against a non EH system, and our findings indicate that the proposed system outperforms the benchmark in terms of outage probability.

eess.SP↗

arXiv2025

Computing in Integrated Terrestrial and Non-Terrestrial Networks: A Comprehensive Survey

The rapid growth of Internet-of-things (IoT) devices, smart vehicles, and other connected objects is driving demand for ubiquitous connectivity and intensive computing capacity. 5G and upcoming 6G networks are crucial to meeting these demands and the fast-evolving services and applications. However, traditional terrestrial networks face limitations in coverage and capacity. Integrated Terrestrial and Non-Terrestrial Networks (ITNTN) are emerging to address these challenges. In essence, ITNTN combines ground-based infrastructure with aerial, space, and water surface networks to provide seamless connectivity and computing resources anytime, anywhere. Given the stringent quality-of-service (QoS) of future services, edge computing will be an inseparable component of ITNTN. Consequently, we dive in this survey into current efforts of integrating cloud/fog/edge computing into ITNTN layers to facilitate stringent QoS services and address the data processing needs of modern applications. Since there have been only limited and partial efforts in integrating computing functionalities within ITNTN, we aim to extend the discussion to the full integration of computing and identifying the challenges and future research directions to achieve it.

cs.NI↗

arXiv2024

XLM for Autonomous Driving Systems: A Comprehensive Review

Large Language Models (LLMs) have showcased remarkable proficiency in various information-processing tasks. These tasks span from extracting data and summarizing literature to generating content, predictive modeling, decision-making, and system controls. Moreover, Vision Large Models (VLMs) and Multimodal LLMs (MLLMs), which represent the next generation of language models, a.k.a., XLMs, can combine and integrate many data modalities with the strength of language understanding, thus advancing several information-based systems, such as Autonomous Driving Systems (ADS). Indeed, by combining language communication with multimodal sensory inputs, e.g., panoramic images and LiDAR or radar data, accurate driving actions can be taken. In this context, we provide in this survey paper a comprehensive overview of the potential of XLMs towards achieving autonomous driving. Specifically, we review the relevant literature on ADS and XLMs, including their architectures, tools, and frameworks. Then, we detail the proposed approaches to deploy XLMs for autonomous driving solutions. Finally, we provide the related challenges to XLM deployment for ADS and point to future research directions aiming to enable XLM adoption in future ADS frameworks.

eess.SY↗

arXiv2023

Multi-Mode High Altitude Platform Stations (HAPS) for Next Generation Wireless Networks

The high altitude platform station (HAPS) concept has recently received notable attention from both industry and academia to support future wireless networks. A HAPS can be equipped with 5th generation (5G) and beyond technologies such as massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS). Hence, it is expected that HAPS will support numerous applications in both rural and urban areas. However, this comes at the expense of high energy consumption and thus shorter loitering time. To tackle this issue, we envision the use of a multi-mode HAPS that can adaptively switch between different modes so as to reduce energy consumption and extend the HAPS loitering time. These modes comprise a HAPS super macro base station (HAPS-SMBS) mode for enhanced computing, caching, and communication services, a HAPS relay station (HAPS-RS) mode for active communication, and a HAPS-RIS mode for passive communication. This multimode HAPS ensures that operations rely mostly on the passive communication payload, while switching to an energy-greedy active mode only when necessary. In this article, we begin with a brief review of HAPS features compared to other non-terrestrial systems, followed by an exposition of the different HAPS modes proposed. Subsequently, we illustrate the envisioned multi-mode HAPS, and discuss its benefits and challenges. Finally, we validate the multi-mode efficiency through a case study.

eess.SP↗

arXiv2023

Resource-Efficient HAPS-RIS Enabled Beyond-Cell Communications

In the future, urban regions will encounter a massive number of capacity-hungry devices. Relying solely on terrestrial networks for serving all UEs will be a cost-ineffective approach. Consequently, with the anticipated supply and demand mismatch, several UEs will be unsupported. To offer service to the left-out UEs, we employ an energy-efficient and cost-effective beyond-cell communications approach, which uses reconfigurable intelligent surfaces (RIS) on a high-altitude platform station (HAPS). Particularly, unsupported UEs will be connected to a dedicated control station (CS) through RIS-mounted HAPS. A novel resource-efficient optimization problem is formulated that maximizes the number of connected UEs, while minimizing the total power consumed by the CS and RIS. Since the resulting problem is a mixed-integer nonlinear program (MINLP), a low-complexity two-stage algorithm is developed. Numerical results demonstrate that the proposed algorithm outperforms the benchmark approach in terms of the percentage of connected UEs and the resource-efficiency (RE). Also, the results show that the number of connected UEs is more sensitive to transmit power at the CS than the HAPS size.

eess.SY↗

arXiv2022

Beyond-Cell Communications via HAPS-RIS

The ever-increasing number of users and new services in urban regions can lead terrestrial base stations (BSs) to become overloaded and, consequently, some users to go unserved. Compounding this, users in urban areas can face severe shadowing and blockages, which means that some users do not receive a desired quality of service (QoS). Motivated by the energy and cost benefits of reconfigurable intelligent surfaces (RIS) and the advantages of high altitude platform stations (HAPS), including their wide footprint and strong line-of-sight (LoS) links, we propose a solution to service the stranded users using the RISaided HAPS. More specifically, we propose to service the stranded users by a dedicated control station (CS) via a HAPS equipped with RIS (HAPS-RIS). Through this approach, users are not restricted from being serviced by the cell they belong to; hence, we refer to this approach as beyond-cell communication. As we demonstrate in this paper, beyond-cell communication works in tandem with legacy terrestrial networks to support uncovered or unserved users. Optimal transmit power and RIS unit assignment strategies for the users based on different network objectives are introduced. Numerical results demonstrate the benefits of the proposed beyond-cell communication approach. Moreover, the results provide insights into the different optimization objectives and their interplay with minimum quality-of-service (QoS) and network resources, such as transmit power and the number of reflectors.

cs.IT↗

arXiv2021

Optimization of Quantized Phase Shifts for Reconfigurable Smart Surfaces Assisted Communications

Reconfigurable Smart Surface (RSS) is assumed to be a key enabler for future wireless communication systems due to its ability to control the wireless propagation environment and, thus, enhance communications quality. Although optimal and continuous phase-shift configuration can be analytically obtained, practical RSS systems are prone to both channel estimation errors, discrete control, and curse of dimensionality. This leads to relaying on a finite number of phase-shift configurations that is expected to degrade the system's performances. In this paper, we tackle the problem of quantized RSS phase-shift configuration, aiming to maximize the data rate of an orthogonal frequency division multiplexing (OFDM) point-to-point RSS-assisted communication. Due to the complexity of optimally solving the formulated problem, we propose here a sub-optimal greedy algorithm to solve it. Simulation results illustrate the performance superiority of the proposed algorithm compared to baseline approaches. Finally, the impact of several parameters ,e.g., quantization resolution and RSS placement, is investigated.

eess.SP↗

arXiv2021

Link Budget Analysis for Reconfigurable Smart Surfaces in Aerial Platforms

Non-terrestrial networks, including Unmanned Aerial Vehicles (UAVs), High Altitude Platform Station (HAPS) and Low Earth Orbiting (LEO) satellites, are expected to have a pivotal role in the sixth generation wireless networks. With their inherent features such as flexible placement, wide footprint, and preferred channel conditions, they can tackle several challenges in current terrestrial networks However, their successful and widespread adoption relies on energy-efficient on-board communication systems. In this context, the integration of Reconfigurable Smart Surfaces (RSS) into aerial platforms is envisioned as a key enabler of energy-efficient and cost-effective deployments of aerial platforms. Indeed, RSS consist of low-cost reflectors capable of smartly directing signals in a nearly passive way. We investigate in this paper the link budget of RSS-assisted communications under the two discussed RSS reflection paradigms in the literature, namely the specular and the scattering reflection paradigm types. Specifically, we analyze the characteristics of RSS-equipped aerial platforms and compare their communication performance with that of RSS-assisted terrestrial networks, using standardized channel models. In addition, we derive the optimal aerial platforms placements under both reflection paradigms. The obtained results provide important insights for the design of RSS-assisted communications. For instance, given that a HAPS has a large RSS surface, it provides superior link budget performance in most studied scenarios. In contrast, the limited RSS area on UAVs and the large propagation loss in LEO satellite communications make them unfavorable candidates for supporting terrestrial users. Finally, the optimal location of the RSS-equipped platform may depend on the platform's altitude, coverage footprint, and type of environment.

eess.SP↗

arXiv2021

A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).

cs.IT↗

arXiv2020

Aerial Platforms with Reconfigurable Smart Surfaces for 5G and Beyond

Aerial platforms are expected to deliver enhanced and seamless connectivity in the fifth generation (5G) wireless networks and beyond (B5G). This is generally achievable by supporting advanced onboard communication features embedded in heavy and energy-intensive equipment. Alternatively, reconfigurable smart surfaces (RSS), which smartly exploit/recycle signal reflections in the environment, are increasingly being recognized as a new wireless communication paradigm to improve communication links. In fact, their reduced cost, low power use, light weight, and flexible deployment make them an attractive candidate for integration with 5G/B5G technologies. In this article, we discuss comprehensive approaches to the integration of RSS in aerial platforms. First, we present a review of RSS technology, its operations and types of communication. Next, we describe how RSS can be used in aerial platforms, and we propose a control architecture workflow. Then, several potential use cases are presented and discussed. Finally, associated research challenges are identified.

eess.SP↗

arXiv2020

Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines

In this paper, we focus on energy-efficient UAV-based IoT data collection in sensor networks in which the sensed data have different time deadlines. In the investigated setting, the sensors are clustered and managed by cluster heads (CHs), and multiple UAVs are used to collect data from the CHs. The formulated problem is solved through a two-step approach. In the first step, an efficient method is proposed to determine the minimal number of CHs and their best locations. Subsequently, the minimal number of UAVs and their trajectories are obtained by solving the associated capacitated vehicle routing problem. Results show the efficiency of our proposed CHs placement method compared to baseline approaches, where bringing the CHs closer to the dockstation allows significant energy savings. Moreover, among different UAV trajectory planning algorithms, Tabu search achieves the best energy consumption. Finally, the impact of the battery capacity and time deadline are investigated in terms of consumed energy, number of visited CHs, and number of deployed UAVs.

eess.SP↗

arXiv2019

Multi-UAV Data Collection Framework for Wireless Sensor Networks

In this paper, we propose a framework design for wireless sensor networks based on multiple unmanned aerial vehicles (UAVs). Specifically, we aim to minimize deployment and operational costs, with respect to budget and power constraints. To this end, we first optimize the number and locations of cluster heads (CHs) guaranteeing data collection from all sensors. Then, to minimize the data collection flight time, we optimize the number and trajectories of UAVs. Accordingly, we distinguish two trajectory approaches: 1) where a UAV hovers exactly above the visited CH; and 2) where a UAV hovers within a range of the CH. The results of this include guidelines for data collection design. The characteristics of sensor nodes' K-means clustering are then discussed. Next, we illustrate the performance of optimal and heuristic solutions for trajectory planning. The genetic algorithm is shown to be near-optimal with only $3.5\%$ degradation. The impacts of the trajectory approach, environment, and UAVs' altitude are investigated. Finally, fairness of UAVs trajectories is discussed.

eess.SP↗