This study proposes an energy-efficient framework for non-terrestrial networks (NTNs) integrating a low Earth orbit (LEO) satellite, an unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surface (RIS), and a terrestrial user. The framework jointly optimizes the UAV’s 3D trajectory, satellite beamforming vectors, and RIS reflection coefficients to maximize energy efficiency (EE), accounting for UAV propulsion energy consumption and Quality of Service (QoS) constraints. The resulting non-convex fractional problem is solved using a low-complexity iterative algorithm combining successive convex approximation (SCA) and second-order cone programming (SOCP). Simulation results reveal up to 35% EE improvement over baseline schemes, highlighting the framework’s scalability and practicality for sustainable NTN systems.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have garnered considerable research interest in the fields of 5G and 6G wireless communication due to their remarkable flexibility and cost-effectiveness. However, the inherent openness of wireless communication environments renders these technologies vulnerable to eavesdropping. This paper presents a penalty-based successive convex approximation algorithm and a minorize–maximization algorithm to optimize the transmission beamforming vector, RIS beamforming vector, and UAV–RIS trajectory. The objective of this study was to enhance the physical layer security performance of wireless communication systems using UAVs and RISs. Our simulation results demonstrate that the proposed technique achieves a higher security transmission rate compared to existing techniques.

This study proposes an energy-efficient framework for non-terrestrial networks (NTNs) integrating a low Earth orbit (LEO) satellite, an unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surface (RIS), and a terrestrial user. The framework jointly optimizes the UAV’s 3D trajectory, satellite beamforming vectors, and RIS reflection coefficients to maximize energy efficiency (EE), accounting for UAV propulsion energy consumption and Quality of Service (QoS) constraints. The resulting non-convex fractional problem is solved using a low-complexity iterative algorithm combining successive convex approximation (SCA) and second-order cone programming (SOCP). Simulation results reveal up to 35% EE improvement over baseline schemes, highlighting the framework’s scalability and practicality for sustainable NTN systems.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have garnered considerable research interest in the fields of 5G and 6G wireless communication due to their remarkable flexibility and cost-effectiveness. However, the inherent openness of wireless communication environments renders these technologies vulnerable to eavesdropping. This paper presents a penalty-based successive convex approximation algorithm and a minorize–maximization algorithm to optimize the transmission beamforming vector, RIS beamforming vector, and UAV–RIS trajectory. The objective of this study was to enhance the physical layer security performance of wireless communication systems using UAVs and RISs. Our simulation results demonstrate that the proposed technique achieves a higher security transmission rate compared to existing techniques.

Along with the advent of a high-end SoC with multiple CPU clusters and many GPUs, the automotive industry has a strong motivation to consolidate multi-domain applications on such a single SoC for wiring harness reduction and space/weight saving. For this, it is essential to bound their mutual interferences among CPU+GPU clusters on the system memory since advanced automotive applications use huge size code/data like autonomous driving and multi-screen infotainment. In order to guarantee memory bandwidth to each cluster, this paper proposes a cluster-level memory access regulation that aggregates the total memory access amount by each cluster (by all CPU cores and GPUs within the cluster) and throttles the cluster if it exceeds the given threshold. Then, we propose a few-shot measurement based optimal memory bandwidth allocation that can find a near optimal solution with only a few measurements, which is practically essential to save the system development cost. Our extensive experiments on a real SoC board say that our proposed techniques successfully regulate each cluster's memory bandwidth usage within ± 10 % margin of the allocated bandwidth. Also, our optimization can achieve a near-optimal utility with less than 0.6 % loss on average compared to the real optimal, with only a few measurements (mostly three measurements) instead of 25 measurements needed for the real optimal.

정확하고 보편적인 위치 추정은 차세대 무선 통신 시스템을 안정적으로 서비스하기 위한 핵심 요소이다. 특히, 6G 및 그 이후의 통신 시스템에서는 밀리미터파(mmWave) 신호를 활용함으로써 제한된 인프라 환경에서도 정밀한 위치 추정이 가능하다. 그러나 센티미터 수준의 고정밀 위치 추정을 위해서는 채널 파라미터와 사용자 위치 간의 기하학적 관계를 기반으로 한 가시경로(Line-of-Sight, LOS)의 확보가 필수적이다. 이에 본 논문에서는 LOS 확보를 위한 방안으로, 높은 고도에서 기지국 또는 단말로 활용 가능한 비지상 네트워크(Non-Terrestrial Network, NTN)와 재구성 가능한 지능형 반사판(Reconfigurable Intelligent Surface, RIS) 기반의 시스템을 소개한다. 또한, 단일 기지국 환경에서도 정확한 위치 추정을 가능하게 하는 딥러닝 기반 보정 기법을 함께 제안한다. 제안 기법은 Transformer 기반 학습 네트워크를 활용하여, 기하학 기반 초기 위치 추정값을 정제함으로써 위치 추정 정확도를 크게 향상시킨다. 모의 실험 결과, 본 기법은 약 90% 이상의 사용자에 대해 서브미터 수준의 위치 정확도를 달성함을 확인하였다.

This paper proposes unmanned aerial vehicle (UAV)-reconfigurable intelligent surface (RIS) communication that takes energy efficiency into account in non-terrestrial networks through trajectory optimization of UAV-RIS that jointly considers communication throughput and energy consumption of UAV. The proposed algorithm optimizes the UAV-RIS trajectory, base station beamforming vector, and RIS beamforming vector by appropriately using successive convex approximation, Karush-Kuhn-Tucker conditions.

6G를 지원하기 위한 테라헤르츠 (THz) 대역의 높은 주파수는 풍부한 스펙트럼과 높은 데이터 전송률의 특징을 갖고 있으나 경로 손실이 크다는 문제점이 있다. 이를 보완하기 위해 높은 고도의 위성체를 기지국이나 단말로 활용한 비지상 네트워크(Non-Terrestrial Network, NTN)와 재구성 가능한 지능형 반사판 (Reconfigurable Intelligent Surface, RIS)에 관한 관심이 커지고 있다. 이에 따라, 본 논문에서는 통신 처리량과 무인항공기 (Unmanned Aerial Vehicle, UAV)의 에너지 소비를 공동으로 고려한 UAV-RIS의 궤적 최적화를 통해 비지상 네트워크에서 에너지 효율적인 UAV-RIS 통신을 제안한다. 제안 알고리즘은 SCA (Successive Convex Approximation), KKT (Karush-Kuhn-Tucker) 기반한 SOCP (Second-Order Cone Programming) 알고리즘을 적절히 사용해 UAV-RIS 궤적, 기지국 빔포밍 벡터와 RIS 빔포밍 벡터를 최적화함으로써 에너지 성능을 최대화한다. 모의실험 결과 제안 알고리즘이 기존 기법보다 더 높은 에너지 효율성을 달성하는 것을 확인할 수 있다.

최근 무인항공기 (Unmanned Aerial Vehicle, UAV) 와 재구성 가능한 지능형 표면 (Reconfigurable Intelligent Surface, RIS)은 높은 유동성 및 낮은 하드웨어 비용으로 5G, 6G 무선 통신에서 큰 관심을 받고 있다. 하지만 무선 통신 환경의 개방성으로 인해 도청에 취약다는 문제점이 여전히 남아있다. 이에 본 논문에서는 UAV와 RIS를 활용하는 무선통신 시스템에서 물리 계층 보안 성능을 높이기 위해, 송신 빔포밍 벡터, RIS 빔포밍 벡터와 UAV-RIS 궤적을 최적화하기 위해 페널티 (penalty) 기반의 SCA (Successive Convex Approximation) 알고리즘과 MM (Minorization-Maximization) 알고리즘을 소개한다. 모의실험결과 제안 기법이 기존의 기법에 비해 높은 보안 전송률을 가짐을 확인할 수 있다.

Unmanned Aerial Vehicle (UAV) is attracting attention as a key element of the New Radio (NR) system as it can exceed the limits of the ground network to prepare for the rapidly increasing data usage and create new application services. However, high-altitude UAVs have a high Line-Of-Sight (LOS) probability, resulting in interference problems between adjacent cells in multi-cell networks. Therefore, this paper proposes an algorithm that selects the optimal beam to reduce the impact of interference and ensure high transmission efficiency. Specifically, the proposed algorithm consists of the process of building a Fingerprint Database (fingerprint database) and the process of selecting a load balancing beam. Simulations were conducted based on the downlink system for performance analysis, and signal-to-interference-plus-noise cumulative distribution function (SINR CDF) is used as performance analysis indicators.