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Integrating low-altitude earth orbit (LEO) satellites (SATs) and unmanned aerial vehicles (UAVs) within a non-terrestrial network (NTN), we investigate the problem of forwarding packets between two faraway ground terminals through SAT and UAV relays using either radio-frequency (RF) or free-space optical (FSO) link. Towards maximizing the communication efficiency, the associations with orbiting SATs and the trajectories of UAVs should be optimized, which is challenging due to the time-varying ne

In this work, we address the trajectory optimization of a fixed-wing unmanned aerial vehicle (UAV) using free space optical communication (FSOC). Here, we focus on maximizing the flight time of the UAV by considering practical constraints for wireless UAV communication, including limited propulsion energy and required data rates. We find optimized trajectories in various atmospheric environments (e.g., moderate-fog and heavy-fog conditions), while also considering the channel characteristics of

A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. Integrating this with burgeoning unmanned aerial vehicle (UAV) assisted non-terrestrial networks will be a disruptive solution for beyond 5G systems provisioning large-scale three-dimensional connectivity. In this article, we study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation and

Integrating terrestrial networks with burgeoning high-altitude platform stations (HAPSs) will be a disruptive challenge for beyond-5G systems provisioning large-scale three-dimensional connectivity. Here, we study the problem of forwarding packets between terrestrial terminals and backhaul through multi-HAPS relaying. Considering the limited wireless backhaul, which is the practical constraint of HAPS relaying, dual-hop mixed radiofrequency/free-space optical (RF/FSO) networks are investigated,

A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. LEO SAT networks exhibit extremely long link distances of many users under time-varying SAT network topology. This makes existing multiple access protocols, such as random access channel (RACH) based cellular protocol designed for fixed terrestrial network topology, ill-suited. To overcome this issue, in this paper, we p

This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks’ HO procedures. DHO skips the Measurement Report (MR) in the HO procedure by leveraging its predictive capabilities after being trained with a pre-determined LEO satellite orbital pattern. This simplification eliminates the propagation delay incurred during the MR ph

Large-scale channel prediction, i.e., estimation of the pathloss from geographical/morphological/building maps, is an essential component of wireless network planning. Ray tracing (RT)-based methods have been widely used for many years, but they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in B5G/6G systems. In this paper, we propose a data-driven, model-free pathloss map prediction (PMP) method, ca

In this paper, we consider an unmanned aerial vehicle (UAV) aided mobile relaying system under a buffer constraint at the relay node. We propose a new relaying protocol employing mixed free-space optical/radio frequency (FSO/RF) communication, i.e., the source-relay and relay-destination link utilize FSO communication and RF communication, respectively, under the buffer constraint which is required to consider practical relay system. We study the trajectory optimization problem of buffer-constra

Pathloss prediction is an essential component of wireless network planning. While ray tracing based methods have been successfully used for many years, they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in 5G/BSG (beyond 5G) systems. In this paper, we propose and evaluate a data-driven and model-free pathloss prediction method, dubbed PMNet. This method uses a supervised learning approach: training a

In this work, we address the trajectory optimization of a fixed-wing UAV using free space optical communication (FSO). Here, we focus on maximizing the service time of FSO-based vertical wireless backhaul networks by considering practical constraints for wireless UAV communication, including limited energy and required data rates. To solve the optimization problem, we use a bisection method and sequential programming and introduce a new feasibility algorithm. Although we design suboptimal soluti

In this work, we propose a realistic semantic network called seq2seq-SC, designed to be compatible with 5G NR and capable of working with generalized text datasets using a pre- trained language model. The goal is to achieve unprecedented communication efficiency by focusing on the meaning of messages in semantic communication. We employ a performance metric called semantic similarity, measured by BLEU for lexical similarity and SBERT for semantic similarity. Our findings demonstrate that seq2seq

In this work, we address the optimization of vertical backhaul framework where multiple aerial base station (ABS) are deployed to conFigure the intermediate backhaul links for terrestrial base stations (TBS) in wireless networks. Here, we focus on maximizing the downlink network throughput in mixed RF/FSO backhaul networks by optimizing resource allocation and placement. Specifically, the ABS-TBS association, transmit power, and positions of ABSs are alternately and iteratively optimized. To sol

A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. Integrating this with burgeoning unmanned aerial vehicle (UAV) assisted non-terrestrial networks will be a disruptive solution for beyond 5G systems provisioning large scale three-dimensional connectivity. In this article, we study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation and

This paper describes our pathloss prediction system submitted to the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. We describe the architecture of PMNet, a neural network we specifically designed for pathloss prediction. Moreover, to enhance the prediction performance, we apply several machine learning techniques, including data augmentation, fine-tuning, and optimization of the network architecture. Our system achieves an RMSE of 0.02569 on the provided RadioMap3Dseer dataset, and

The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust, efficient, and generalizable communication frameworks. However, integrating these frameworks with existing wireless systems and effectively managing noise and bit errors pose significant challenges. In this work, we introduce a practical on-device AI communication framework, integrated with physical layer (PHY) communi