Hierarchically Tunable 6DMA for Wireless Communication and Sensing: Modeling and Performance Optimization
원문 초록
This paper proposes a new hierarchically tunable six-dimensional movable antenna (HT-6DMA) architecture for base station (BS) in future wireless networks. The HT-6DMA BS consists of multiple antenna arrays that can flexibly move on a spherical surface, with their three-dimensional (3D) positions and 3D rotations/orientations efficiently characterized in the global spherical coordinate system (SCS) and their individual local SCSs, respectively. As a result, the 6DMA system is hierarchically tunable in the sense that each array's global position and local rotation can be separately adjusted in a sequential manner with the other being fixed, thus greatly reducing their design complexity and improving the achievable performance. In particular, we consider an HT-6DMA BS serving multiple single-antenna users in the uplink communication or sensing potential unmanned aerial vehicles (UAVs)/drones in a given airway area. Specifically, for the communication scenario, we aim to maximize the average sum rate of communication users in the long term by optimizing the positions and rotations of all 6DMA arrays at the BS. While for the airway sensing scenario, we maximize the minimum received sensing signal power along the airway by optimizing the 6DMA arrays' positions and rotations along with the BS's transmit covariance matrix. Despite that the formulated problems are both non-convex, we propose efficient solutions to them by exploiting the hierarchical tunability of positions/rotations of 6DMA arrays in our proposed model. Numerical results show that the proposed HT-6DMA design significantly outperforms not only the traditional BS with fixed-position antennas (FPAs), but also the existing 6DMA scheme. Furthermore, it is unveiled that the performance gains of HT-6DMA mostly come from the arrays' global position adjustments on the spherical surface, rather than their local rotation adjustments.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
SeqPE: Transformer with Sequential Position Encoding
We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems
JoFormer (Journey-based Transformer): Theory and Empirical Analysis on the Tiny Shakespeare Dataset
POSS: Position Specialist Generates Better Draft for Speculative Decoding
ComRoPE: Scalable and Robust Rotary Position Embedding Parameterized by Trainable Commuting Angle Matrices
토론
이 논문과 연결된 포럼 글이 아직 없습니다.