記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：IEEE Open Journal of Vehicular Technology  

The convergence of sixth-generation (6G) networks with unmanned aerial vehicles (UAVs) and satellites is poised to introduce substantial improvements to the landscape of wireless communication, paving the way for a unified and uninterrupted space-air-ground-sea network that ensures comprehensive global connectivity. At the heart of this transformative paradigm lies artificial intelligence (AI), which drives innovation across diverse sectors by enhancing decision-making autonomy, enabling real-time data processing, and optimizing network performance and coverage. This survey paper explores AI-enabled UAV-satellite communications for 6G applications, focusing on its challenges, potential, and future. This new system combines the strengths of 6G networks, UAVs (advanced drones), and satellites. It opens up new possibilities in precision agriculture, disaster management, enhanced telecommunication services, and remote sensing. Despite its promise, this field faces complex challenges. These include spectrum management, security risks, regulatory barriers, and integrating AI operations seamlessly. This paper comprehensively analyzes these challenges, offering innovative solutions and outlining future research directions to unlock the complete capabilities of 6G-enabled UAV-satellite communications. Furthermore, it includes a case study demonstrating the effectiveness of multi-armed bandit (MAB) algorithms in optimizing resource allocation and decision-making processes for UAV-low Earth orbit (LEO) satellite communication scenarios, showcasing significant improvements in network performance. This work lays the foundation for a new generation of ultra-connected, data-driven applications that will redefine global connectivity and technological advancement by addressing these critical aspects.

DOI： 10.1109/OJVT.2025.3587028

Web of Science

Scopus

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Institute of Electrical and Electronics Engineers (IEEE)  

This paper proposes two energy-efficient reinforcement learning (RL)-based algorithms for millimeter wave (mmWave)-enabled unmanned aerial vehicle (UAV) communications toward beyond-5G (B5G). This can be especially useful in ad-hoc communication scenarios within a neighborhood with main-network connectivity problems such as in areas affected by natural disasters. To improve the system’s overall sum-rate performance, the UAV-operated mobile base station (UAV-MBS) can harness non-orthogonal multiple access (NOMA) as an efficient protocol to grant ground devices access to fast downlink connections. Dynamic selection of suitable hovering spots within the target zone where the battery-constrained UAV needs to be positioned as well as calibrated NOMA power control with proper device pairing are critical for optimized performance. We propose cost-subsidized multiarmed bandit (CS-MAB) and double deep Q-network (DDQN)-based solutions to jointly address the problems of dynamic UAV path design, device pairing, and power splitting for downlink data transmission in NOMA-based systems. To verify that the proposed RL-based solutions support high sum-rates, numerical simulations are presented. In addition, exhaustive and random search benchmarks are provided as baselines for the achievable upper and lower sum-rate levels, respectively. The proposed DDQN agent achieves 96% of the sum-rate provided by the optimal exhaustive scanning whereas CS-MAB reaches 91.5%. By contrast, a conventional channel state sorting pairing (CSSP) solver achieves about 89.3%.

DOI： 10.1109/TMLCN.2024.3396438

Web of Science

CiNii Research

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Ieej Transactions on Electrical and Electronic Engineering  

Non-orthogonal multiple access (NOMA) allows multiple user equipment (UE) to simultaneously share the same resource blocks using varying levels of transmit power at the base station (BS) side. Proper allocation of transmission power and selection of candidate users for pairing over the same resource block are critical for an efficient utilization of the available resources. Optimal UE selection and power splitting among paired UEs can be made through an exhaustive search over the space of all possible solutions. However, the cost incurred by such approach can render it practically infeasible. Reinforcement learning (RL) deploying double deep-Q networks (DDQN) is a promising framework that can be adopted for tackling the problem. In this article, an RL-based DDQN scheme is proposed for user pairing in opportunistic access to downlink NOMA systems with capacity-limited backhaul link connections. The proposed algorithm relies on proactive data caching to alleviate the throttling caused by backhaul bottlenecks, and optimized UE selection and power allocation are accomplished through the continuous interaction between an RL agent and the NOMA environment to increase the overall system throughput. Simulation results are presented to showcase the near-optimal strategy achieved by the proposed scheme. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

DOI： 10.1002/tee.23560

Web of Science

Scopus

CiNii Research

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Electronics Switzerland  

Peak-to-average power ratio (PAPR) reduction in multiplexed signals in orthogonal frequency division multiplexing (OFDM) systems has been a long-standing critical issue. Clipping and filtering (CF) techniques offer good performance in terms of PAPR reduction at the expense of a relatively high computational cost that is inherent in the repeated application of fast Fourier transform (FFT) operations. The ever-increasing demand for low-latency operation calls for the development of low-complexity novel solutions to the PAPR problem. To address this issue while providing an enhanced PAPR reduction performance, we propose a synchronous neural network (NN)-based solution to achieve PAPR reduction performance exceeding the limits of conventional CF schemes with lower computational complexity. The proposed scheme trains a neural network module using hybrid collections of samples from multiple OFDM symbols to arrive at a signal mapping with desirable characteristics. The benchmark NN-based approach provides a comparable performance to conventional CF. However, it can underfit or overfit due to its asynchronous nature which leads to increased out-of-band (OoB) radiations, and deteriorating bit error rate (BER) performance for high-order modulations. Simulations’ results demonstrate the effectiveness of the proposed scheme in terms of the achieved cubic metric (CM), BER, and OoB emissions.

DOI： 10.3390/electronics10141708

Web of Science

Scopus

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）   出版者・発行元：IEEE Vehicular Technology Conference  

The integration of artificial intelligence (AI) with fog radio access networks (F-RANs) has garnered great interest, primarily motivated by the needs for efficient network operation and for ensuring high service availability. Fog access points (F-APs) can help with computation offloading and thereby alleviate the huge computational burdens of terminal devices in F-RANs. However, the overall system energy consumption must to be minimized. As described herein, we propose a computation offloading strategy for industrial internet-of-things (IIoT) devices that is centered around deep reinforcement learning (DRL) based user and F-AP association, which can learn high-dimensional data and which can respond to dynamic changes in the environment. The proposed DRL model adopts a framework that deploys the agent at the user side to address the challenge of high dimensionality in the action space. Specifically, each IIoT device is assigned a dedicated DRL model within the framework, facilitating the identification of an appropriate F-AP based on the environment state. Once the user and F-AP association process is completed, a computationally efficient greedy algorithm is used at each FAP, considering the limited capability, aiding in determining the subset of offloading requests that should be forwarded to the cloud for additional processing. The simulation results showcase the superior performance of the proposed DRL algorithm over traditional algorithms, including the random algorithm and the greedy algorithm, in terms of energy consumption. Under the same operation time, DRL also outperforms the genetic algorithm.

DOI： 10.1109/VTC2024-Fall63153.2024.10757816

Scopus