Perceptive mobile networks (PMNs) implement sensing and communication by reusing existing cellular infrastructure. Thanks to the cooperation among distributed access points, cell-free multiple-input multiple-output (MIMO) systems support the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, in a cell-free MIMO system, distributed access points (APs) communicate over fronthaul links with a central processing unit (CPU) in the cloud. This work proposes four different types of PMN uplink solutions based on cell-free MIMO systems, in which the sensing and decoding functionalities are carried out at either the cloud or the edge. Accordingly, we develop and compare joint cloud-based decoding and sensing (CDCS), hybrid cloud-based decoding and edge-based sensing (CDES), hybrid edge-based decoding and cloud-based sensing (EDCS) and edge-based decoding and sensing (EDES). In all cases, we target a unified design problem formulation for fronthaul quantization to maximize the achievable rate under sensing and fronthaul capacity constraints. Via numerical results, the four implementation scenarios are compared as a function of the available fronthaul resources by highlighting the relative merits of edge- and cloud-based sensing and communications. This study provides guidelines on the optimal functional allocation in fronthaul-constrained networks implementing integrated sensing and communications.

This letter proposes a downlink cooperative rate splitting (CRS) system for massive Internet of Things (IoT) to mitigate inter-user interference of high-frequency channels and improve sum-rate performance. Specifically, we mitigate inter-user interference by dynamically adjusting user grouping, which reshapes interference patterns and enhances cooperative link data rates. To achieve sum-rate maximization, we introduce a CRS framework that leverages cross-entropy optimization (CEO) for user grouping and weighted minimum mean square error (WMMSE)-based beamforming to effectively mitigate interference. Simulation results demonstrate that the proposed approach outperforms conventional user grouping and multiple access schemes in maximizing the sum-rate in massive IoT networks.

In this correspondence, we propose hierarchical high-altitude platform (HAP)-low-altitude platform (LAP) networks with the aim of maximizing the sum-rate of ground user equipments (UEs). The multiple aerial radio units mounted on HAPs and LAPs are managed by the central unit (CU) via constrained fronthaul links. The limitation of fronthaul capacity can be addressed through quantization, employing the network multiple-input multiple-output (MIMO) architecture. For spectral efficiency, we adopt the rate-splitting multiple access (RSMA), leveraging the advantages of both space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA). To achieve this, we jointly optimize multiple-antenna rate-splitting transmission, fronthaul quantization design and UAV placement using an alternating optimization (AO) approach coupled with successive convex approximation (SCA) and the weighted minimum mean square error (WMMSE) method. Numerical results validate the superior performance of the proposed method compared to benchmark schemes, including partial optimizations or those without the assistance of LAPs.

In this correspondence, we propose a joint mechanical and electrical adjustment of the intelligent reflecting surface (IRS) to improve the performance of low Earth orbit (LEO) satellite multiple-input multiple-output (MIMO) communications. In particular, we construct a three-dimensional (3D) MIMO channel model for the mechanically-tilted IRS in general deployment, and consider two types of scenarios with and without a direct path of LEO-ground user link due to the orbital flight. With the aim of maximizing the end-to-end performance, we jointly optimize tilting angle and phase shift of IRS along with the transceiver beamforming, whose performance superiority is verified via simulations with a real orbit data generated according to the Orbcomm LEO satellite specifications.

In this letter, we propose a AAV-enabled integrated sensing and communication (ISAC) system for secrecy enhancement, where a full-duplex AAV with a uniform planar array serves multiple ground users while sensing and jamming a ground eavesdropper. To maximize the sum secrecy rate under sensing SINR and operational constraints, we jointly optimize the transceiver beamforming and AAV trajectory. An algorithmic solution based on block coordinate descent (BCD) and semidefinite programming (SDP) relaxation technique is developed, whose performance gains are emphasized in simulations with sufficient mission periods.

In this correspondence, we propose to use an intelligent reflective surface (IRS) installed on autonomous aerial vehicle (AAV), referred to as aerial IRS (AIRS), for vehicular networks, where simultaneous wireless information and power transfer (SWIPT) receivers to concurrently allow information decoding (ID) and energy harvesting (EH) are equipped at the battery-limited vehicles. For efficiently supporting the multiple moving vehicles, we adopt rate-splitting multiple access (RSMA) technique. With the aim of maximizing the sum rate of vehicles, we jointly optimize trajectory and phase shift design of AIRS, transmit power and rate allocation for RSMA along with power splitting ratio for SWIPT implementation. Via simulations, the superior performances of the proposed algorithm are validated compared to the conventional partial optimizations.

물리 계층의 특성을 이용하는 정보이론적 보안 (Information-Theoretic or Perfect Security) 개념인 물리 계층보안 (Physical-Layer Security) 기술이 차세대 무선 네트워크 구현을 위한 핵심기술로 중요성이 증대되고 있다. 본논문에서는 협력 무인 항공기 (Unmanned Aerial Vehicle, UAV) 재머 (Jammer)를 고려한 다중 셀 (Multiple Cell)에서의 보안량 (Secrecy Rate) 향상을 위한 물리 계층 보안 기술을 제안한다. 제안 알고리즘은 네트워크를 구성하는 모든 노드에서의 불완전한 채널 가정을 고려하여, 보안량 향상을 최대화하는 무인 항공기 전력 할당 및 비행 궤적을 개발함으로써, 실효성을 향상시키고자 하였다. 이를 위해, 관련 최적화 문제를 형성하고, 이에 대한 최적의 솔루션을 찾기 위해 BCD (Block Coordinate Descent) Method에 기초한 반복 알고리즘을 제안한다. 시뮬레이션 결과를 통해, 무인항공기의 비행 궤적 혹은 전력 할당만을 최적화한 경우에 비해, 제안된 알고리즘이 우수한보안량 성능을 달성하는 것을 증명하였다.

This paper presents the derivation of exact and asymptotic closed-form formulas of the secrecy outage probability (SOP) for space shift keying (SSK) systems over Rayleigh fading channels, especially in the presence of imperfect channel state information (CSI) at a legitimate receiver. Comprehensive analyses of the achievable secrecy performance limit for the SSK systems were performed by judiciously exploiting the derived SOP expressions, which enable us to obtain explicit and useful insights into the effect of channel estimation errors on the secrecy performance for the considered systems. The exactness of our theoretical analyses was validated numerically.

최근 5G/6G 등장과 함께 높은 컴퓨팅 능력을 요구하는 어플리케이션이 급증함에 따라, 한정된 처리 기능을 가진 사용자 기기에게 컴퓨팅 자원을 제공함으로써 원활한 서비스를 지원하는 엣지 컴퓨팅 기술이 많은 관심을 받고 있다. 특히, 사용자 기기에 가까이 엣지 노드를 설치하여 컴퓨팅 자원을 제공하는 엣지 컴퓨팅의 장점을 극대화하기 위해, 이동성이 자유로운 지상 차량 또는 무인 항공기 (UAV: Unmmaned Aerial Vehicle)와 같은 항공 차량들이 함께 고려되어 활발히 연구가 진행되고 있는 실정이다. 본 논문에서는 차량을 고려한 대표적인 두 가지 엣지 컴퓨팅 시나리오, 차량 엣지 컴퓨팅 (VEC: Vehicular Edge Computing) 시스템과 모바일 엣지 컴퓨팅 (MEC: Mobile Edge Computing) 시스템에 대해 연구한다. 차량 엣지 컴퓨팅 시스템은 오프로딩 기기가 차량인 시스템으로써, 오프로딩을 제공하는 엣지 노드, 즉 RSU (Road Side Unit)가 차량의 높은 컴퓨팅 능력을 요구하는 차량 서비스의 원활한 지원을 제공한다. 또한, 모바일 엣지 시스템에서는 무인 항공기에 장착된 클라우드렛 (cloudlet)을고려하여, 제한된 로컬 처리 기능을 가진 모바일 장치에 오프로딩을 제공하는 시나리오를 연구한다. 두 시스템 모두 한정된 배터리 기기, 즉 VEC 용 차량과 MEC용 모바일 기기가 높은 계산 능력을 필요로 하는 어플리케이션을 수행함에 따라, 많은 양의 에너지 소모를 야기한다. 이를 해결하기 위해, 본 논문에서는 RSU 또는 무인 항공기에 장착된 클라우드렛을 고려하여, 차량 혹은 모바일 기기의 총 에너지 소모량을 최소화하는 것을 목표로 하는최적의 엣지 컴퓨팅 시스템을 연구한다. 제안 기술의 실용성을 높이고자, 직교 다중 접속 방식과 비직교 다중 접속 방식 모두를 고려하여, VEC향 최적의 비트 할당 및 오프 로딩 기술과 MEC향 최적의 비트 할당과 오프로딩디자인 및 무인 항공기 궤적 기술을 연구한다. 시뮬레이션 결과를 통해, 로컬 컴퓨팅 대비, 차량과 모바일 장치의상당한 에너지 절감을 확인할 수 있었고, 이를 통해 다양한 미래 엣지 컴퓨팅 기반 시스템의 발전 가능성을 확인하였다.