paper-with-me

홈 › Papers

Movable Antennas Enabled Wireless-Powered NOMA: Continuous and Discrete Positioning Designs

2024-09-30 · Ying Gao, Qingqing Wu, Wen Chen

This paper investigates a movable antenna (MA)-enabled wireless-powered communication network (WPCN), where multiple wireless devices (WDs) first harvest energy from the downlink (DL) signal broadcast by a hybrid access point (HAP) and then transmit information in the uplink (UL) using non-orthogonal multiple access. Unlike conventional WPCNs with fixed-position antennas (FPAs), this MA-enabled WPCN allows the MAs at the HAP and the WDs to adjust their positions twice: once before DL wireless power transfer and once before DL wireless information transmission. Our goal is to maximize the system sum throughput by jointly optimizing the MA positions, the time allocation, and the UL power allocation. Considering the characteristics of antenna movement, we explore both continuous and discrete positioning designs, which, after formulation, are found to be non-convex optimization problems. Before tackling these problems, we rigorously prove that using identical MA positions for both DL and UL is the optimal strategy in both scenarios, thereby greatly simplifying the problems and enabling easier practical implementation of the system. We then propose alternating optimization-based algorithms for the resulting simplified problems. Simulation results show that: 1) the proposed continuous MA scheme can enhance the sum throughput by up to 395.71% compared to the benchmark with FPAs, even when additional compensation transmission time is provided to the latter; 2) a step size of one-quarter wavelength for the MA motion driver is generally sufficient for the proposed discrete MA scheme to achieve over 80% of the sum throughput performance of the continuous MA scheme; 3) when each moving region is large enough to include multiple optimal positions for the continuous MA scheme, the discrete MA scheme can achieve comparable sum throughput without requiring an excessively small step size.

📄 PDF Abstract BibTeX arXiv:2409.20485

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

MAS This optimizer mix ADAM and SGD creating the MAS optimizer.

Similar Papers 제목 키워드 기반

Enabling Secure Wireless Communications via Movable Antennas

2023-12-21 · Zhenqiao Cheng, Nanxi Li, Jianchi Zhu, Xiaoming She 외

A pioneering secure transmission scheme is proposed, which harnesses movable antennas (MAs) to optimize antenna positions for augmenting the physical layer security. Particularly, an MA-enabled secure wireless system is …

Position

RIS-aided Wireless Communication with Movable Elements Geometry Impact on Performance

2024-04-30 · Yan Zhang, Indrakshi Dey, Nicola Marchetti

Reconfigurable Intelligent Surfaces (RIS) are known as a promising technology to improve the performance of wireless communication networks, and have been extensively studied. Movable Antennas (MA) are a novel technology…

Position

UAV-Enabled Wireless Networks with Movable-Antenna Array: Flexible Beamforming and Trajectory Design

2024-05-31 · Wenchao Liu, Xuhui Zhang, Huijun Xing, Jinke Ren 외

Recently, movable antenna (MA) array becomes a promising technology for improving the communication quality in wireless communication systems. In this letter, an unmanned aerial vehicle (UAV) enabled multi-user multi-inp…

Position

Sum Rate Maximization for Movable Antenna Enabled Uplink NOMA

2024-08-13 · Nianzu Li, Peiran Wu, Boyu Ning, Lipeng Zhu

Movable antenna (MA) has been recently proposed as a promising candidate technology for the next generation wireless communication systems due to its significant capability of reconfiguring wireless channels via antenna …

Position

Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach

2024-09-26 · Ali Amhaz, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine

This paper analyzes a downlink C-NOMA scenario where a base station (BS) is deployed to serve a pair of users equipped with movable antenna (MA) technology. The user with better channel conditions with the BS will be abl…

Reinforcement Learning (RL)