Realizing digital-twin services is one of promising applications in 6 G mobile communication and network scenarios. In addition, the use of unmanned aerial vehicles (UAVs) is essential for enabling the services even in the extreme areas where humans cannot reach. In this emerging scenario, it is necessary to design collaborative algorithms for autonomous UAV trajectory control and a centralized computing platform (e.g., cloud) in digital-twin networks. For this system, it is required to build energy-efficient algorithms due to the power-hungry nature in UAVs. Based on this requirements and system characteristics, this paper proposes autonomous UAV charging algorithms and systems where the UAVs are classified into two types, i.e., cluster UAVs (for main image recording operations in digital-twin services, and some of them take the roles of mobile edge computing) and charging UAVs (for charging the cluster UAVs). Our proposed charging should be (i) fully distributed for practical, scalable, and low-overhead operations and (ii) trustworthy for secure and privacy-preserving computation; where these are essential for collaborative operations. Therefore, a novel auction-based charging algorithm for UAV-based digital-twin networks is proposed in order to realize the distributed and truthful operations, which cannot be achieved by the convex optimization-based centralized algorithms in the literature. Our performance evaluation verifies that the proposed algorithm achieves performance improvements (at most 15.53%).

With the rapid advancement of autonomous vehicles, there is a growing demand for infotainment services that require high-quality and delay-sensitive video content. This paper proposes a multi-agent deep reinforcement learning (MADRL) approach for video cache replacement and delivery in mobility-aware vehicular networks. Unlike previous studies, our work focuses on videos of finite lengths and incorporates dynamic cache replacement, optimizing this alongside the delivery of individual video chunks. Considering the challenge of obtaining complete network state information at a central unit (e.g., macro base station), we adopt a MADRL framework to enable roadside units (RSUs) to autonomously decide on video caching and delivery strategies, leveraging partial information from neighboring RSUs. We evaluate the proposed method using various quality-of-service (QoS) metrics. Extensive simulation results demonstrate that our scheme consistently delivers high average video quality while reducing playback stalls, replacement costs, and backhaul usage.

Realizing digital-twin services is one of promising applications in 6 G mobile communication and network scenarios. In addition, the use of unmanned aerial vehicles (UAVs) is essential for enabling the services even in the extreme areas where humans cannot reach. In this emerging scenario, it is necessary to design collaborative algorithms for autonomous UAV trajectory control and a centralized computing platform (e.g., cloud) in digital-twin networks. For this system, it is required to build energy-efficient algorithms due to the power-hungry nature in UAVs. Based on this requirements and system characteristics, this paper proposes autonomous UAV charging algorithms and systems where the UAVs are classified into two types, i.e., cluster UAVs (for main image recording operations in digital-twin services, and some of them take the roles of mobile edge computing) and charging UAVs (for charging the cluster UAVs). Our proposed charging should be (i) fully distributed for practical, scalable, and low-overhead operations and (ii) trustworthy for secure and privacy-preserving computation; where these are essential for collaborative operations. Therefore, a novel auction-based charging algorithm for UAV-based digital-twin networks is proposed in order to realize the distributed and truthful operations, which cannot be achieved by the convex optimization-based centralized algorithms in the literature. Our performance evaluation verifies that the proposed algorithm achieves performance improvements (at most 15.53%).

With the rapid advancement of autonomous vehicles, there is a growing demand for infotainment services that require high-quality and delay-sensitive video content. This paper proposes a multi-agent deep reinforcement learning (MADRL) approach for video cache replacement and delivery in mobility-aware vehicular networks. Unlike previous studies, our work focuses on videos of finite lengths and incorporates dynamic cache replacement, optimizing this alongside the delivery of individual video chunks. Considering the challenge of obtaining complete network state information at a central unit (e.g., macro base station), we adopt a MADRL framework to enable roadside units (RSUs) to autonomously decide on video caching and delivery strategies, leveraging partial information from neighboring RSUs. We evaluate the proposed method using various quality-of-service (QoS) metrics. Extensive simulation results demonstrate that our scheme consistently delivers high average video quality while reducing playback stalls, replacement costs, and backhaul usage.

The development of urban-air-mobility (UAM) is rapidly progressing with spurs, and the demand for efficient transportation management systems is a rising need due to the multifaceted environmental uncertainties. Thus, this article proposes a novel air transportation service management algorithm based on multi-agent deep reinforcement learning (MADRL) to address the challenges of multi-UAM cooperation. Specifically, the proposed algorithm in this article is based on communication network (CommNet) method utilizing centralized training and distributed execution (CTDE) in multiple UAMs for providing efficient air transportation services to passengers collaboratively. Furthermore, this article adopts actual vertiport maps and UAM specifications for constructing realistic air transportation networks. By evaluating the performance of the proposed algorithm in data-intensive simulations, the results show that the proposed algorithm outperforms existing approaches in terms of air transportation service quality. Furthermore, there are no inferior UAMs by utilizing parameter sharing in CommNet and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">centralized critic</i> network in CTDE. Therefore, it can be confirmed that the research results in this article can provide a promising solution for autonomous air transportation management systems in city-wide urban areas.

Spurred by the limited availability of quantum resources, known as qubits, in recent quantum computers, quantum federated learning (QFL) is drawing attention. Due to its ability to fully utilize distributed qubits, QFL is suitable for developing quantum algorithms. QFL achieves local quantum gradients using the parameter-shift rule (P-S rule) and aggregates them, effectively coping with the limited number of qubits in each quantum computer. However, realizing QFL remains challenging due to the characteristics of the P-S rule, which requires two forward passes to compute the quantum gradients in each local quantum computer. These challenges become even more severe when the aggregation of the local quantum gradients occurs under heterogeneous channel conditions and data distributions. Motivated by this, this paper proposes the joint P-S rule, which eliminates the aggregation process in QFL and instead directly achieves the global quantum gradients. Furthermore, this paper proposes joint efficient quantum federated learning (Joint EQFL) that leverages successive interference cancellation and divergence-based clustering for achieving stability under heterogeneous channel conditions and robustness to heterogeneous data distributions. This paper analyzes the convergence and corroborates the superiority of Joint EQFL.

Polarization reconfigurable (PR) antennas provide a promising technology for next-generation wireless communications, due to their capability of reconfiguring wireless channel state. This paper pursues integrating PR antenna technology into massive multiple-input-multiple-output (MIMO) system in the scenario that channel state information (CSI) is not provided. We aim to jointly design the polarization and beamforming vectors on both transceivers for simultaneous channel reconfiguration and beam alignment to maximize the signal-to-noise-ratio (SNR). However, the joint optimization over the polarization and beamforming vectors without perfect CSI is a challenging task. Depolarization increases the dimension of CSI and massive MIMO systems typically have low-dimensional pilot measurement from limited number of radio frequency (RF) chains. This leads to pilot overhead because the transceivers can fulfill only the low-dimensional measurement of the high-dimensional channel. Substantial PR beamforming gain is achieved with significantly reduced pilot overhead, by employing a transformer-based framework on both transceivers to actively design the polarization and beamforming vectors for the pilot stage and transmission stage based on the sequence of received pilots. Numerical experiments demonstrate that the proposed framework remarkably outperforms the prevalent non-adaptive data-driven methods.

According to the advances in quantum computing and distributed learning, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. To successfully opeate QFL under various and dynamic channel conditions in Internet of Things (IoT) environments, this article develops a novel depth-controllable architecture of entangled slimmable QNNs (eSQNNs), and thus, proposes an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eSQNNs. Even though the proposed eSQNN-based eSQFL is superior, training the depth-controllable eSQNN architecture is challenging due to high-entanglement entropy and interdepth interference. Therefore, the proposed method in this article mitigates the interference using entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing interdepth quantum state differences, respectively. Furthermore, the proposed method optimizes the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. The novelty of this work is evaluated via extensive simulations in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.

Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> polar regions) and the energy efficiency limitations in CubeSats. To tackle these problems, high-altitude long-endurance unmanned aerial vehicles (HALE-UAVs) can complement these CubeSat shortcomings for providing cooperatively global access sustainability and energy efficiency. However, as the number of CubeSats and HALE-UAVs, increases, the scheduling dimension of each ground station (GS) increases. As a result, each GS can fall into the curse of dimensionality, and this challenge becomes one major hurdle for efficient global access. Therefore, this paper provides a quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency. The main reason why the QMARL-based scheduler can be beneficial is that the algorithm facilitates a logarithmic-scale reduction in scheduling action dimensions, which is one critical feature as the number of CubeSats and HALE-UAVs expands. Additionally, individual GSs have different traffic demands depending on their locations and characteristics, thus it is essential to provide differentiated access services. The superiority of the proposed scheduler is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings.

This paper introduces the concept of seamlessly embedding AI-native Radio Access Networks (RANs) and integrated device-network approaches, aiming to extend AI capabilities across diverse devices - e.g., smartphones, Internet of Things (IoT) sensors, autonomous vehicles - and network infrastructures. By embedding AI deeply within RANs and fostering tight integration between devices and network architectures, we unlock unprecedented levels of intelligence, efficiency, flexibility, and performance in wireless communication systems. This integration enables devices and networks to collaboratively process, adapt, and optimize data in real-time, creating a synergistic AI-enabled ecosystem that forms the backbone of AI-native 6G. Unlike traditional wireless communication systems, which rely on static configurations and manual interventions, AI-native networks dynamically adapt to changing conditions, autonomously optimize resource allocation, and respond to diverse user demands and environmental factors. The 6GARROW project explores these transformative approaches, demonstrating how AI-native RANs and integrated device-network solutions deliver higher throughput, lower latency, and improved reliability. Beyond enhanced performance, this paradigm shift supports a diverse range of innovative services, including spatio-temporal communications, critical and compute-AI applications, omnipresent IoT, immersive user experiences, and global broadband connectivity. This paper outlines the vision, methodologies, and potential impact of the project, highlighting its role in advancing toward a more connected, intelligent, and adaptive wireless ecosystem for the future.

본 논문은 동적 해양 환경에서 AUV (Autonomous Underwater Vehicle)의 자율 임무 수행 및 경로 최적화를 위해 AOPF (Autonomous Underwater Vehicle Optimal Path Finder) 알고리즘을 제안한다. 제안한 알고리즘은 PPO (Proximal Policy Optimization) 기반 강화학습 기법과 3-degree-of freedom (DOF) 모델을 적용하여 장애물 회피와 목표 도달의 균형을 달성하며, 기존 DDPG (Deep Deterministic Policy Gradient) 알고리즘 대비 빠른 수렴과 높은 임무 성능을 보인다. 실험 결과, 제안 기법은 안정적 학습과 효율적 경로 생성을 확인하였다. 본 연구는 동적 해양 환경에서 AUV의 자율 임무 수행을 위한 새로운 강화학습 기반 제어 방법을 제시하며, 실제 해양 적용 및 다중 AUV 협력 제어와 같은 확장 가능성에 관해 논의한다.