相关论文: Multi-User Continuous-Aperture Array Communication…
A DeepCAPA (Deep Learning for Continuous Aperture Array (CAPA)) framework is proposed to learn beamforming in CAPA systems. The beamforming optimization problem is firstly formulated, and it is mathematically proved that the optimal…
A continuous aperture array (CAPA)-based secure communication system is investigated, where a base station equipped with a CAPA transmits signals to a legitimate user under the existence of an eavesdropper. For improving the secrecy…
Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial DoFs and achieve the theoretical limits of channel capacity. This paper investigates the performance gain…
A general fading model for multipath channels between two non-parallel continuous-aperture arrays (CAPAs) is proposed. Building on this model, the performance of diversity and multiplexing achieved by CAPAs over fading channels is analyzed.…
A continuous aperture array (CAPA)-based multi-group multicast communication system is investigated. An integral-based CAPA multi-group multicast beamforming design is formulated for the maximization of the system energy efficiency (EE),…
A continuous-aperture array (CAPA)-based secure transmission framework is proposed to enhance physical layer security. Continuous current distributions, or beamformers, are designed to maximize the secrecy transmission rate under a power…
We propose a novel low-complexity receiver design for multicarrier continuous aperture array (CAPA) systems operating over doubly-dispersive (DD) channels. The receiver leverages a Gaussian Belief Propagation (GaBP)-based framework that…
Continuous aperture arrays (CAPAs) provide a theoretical upper bound on the performance of densely packed antenna arrays, but their analysis is limited by the lack of closed-form signal-to-noise ratio (SNR) distributions under realistic…
The performance of continuous aperture array (CAPA)-based wireless communications is analyzed in an uplink scenario. An analytical framework is proposed to characterize uplink CAPA-based transmission using electromagnetic field theories. On…
Ensuring both feasibility and efficiency in optimal power flow (OPF) operations has become increasingly important in modern power systems with high penetrations of renewable energy and energy storage. While deep neural networks (DNNs) have…
Despite a lack of theoretical understanding, deep neural networks have achieved unparalleled performance in a wide range of applications. On the other hand, shallow representation learning with component analysis is associated with rich…
A novel electromagnetic (EM) structure termed flexible continuous aperture array (FCAPA) is proposed, which incorporates inherent surface flexibility into typical continuous aperture array (CAPA) systems, thereby enhancing the…
The optimal beamforming design for multi-user continuous aperture array (CAPA) systems is proposed. In contrast to conventional spatially discrete array (SPDA), the beamformer for CAPA is a continuous function rather than a discrete vector…
Exploring the intrinsic interconnections between the knowledge encoded in PRe-trained Deep Neural Networks (PR-DNNs) of heterogeneous tasks sheds light on their mutual transferability, and consequently enables knowledge transfer from one…
The beamforming optimization in continuous aperture array (CAPA)-based multi-user communications is studied. In contrast to conventional spatially discrete antenna arrays, CAPAs can exploit the full spatial degrees of freedom (DoFs) by…
A concise tutorial is provided for analysis of the spatial degrees of freedom (DoFs) in continuous-aperture array (CAPA)-based continuous electromagnetic (EM) channels. First, a simplified spatial model is introduced using the Fresnel…
Channel state information (CSI) at transmitter is crucial for massive MIMO downlink systems to achieve high spectrum and energy efficiency. Existing works have provided deep learning architectures for CSI feedback and recovery at the…
Modern multi-access 5G+ networks provide mobile terminals with additional capacity, improving network stability and performance. However, in highly mobile environments such as vehicular networks, supporting multi-access connectivity remains…
Deep neural networks (DNNs) struggle to learn in dynamic environments since they rely on fixed datasets or stationary environments. Continual learning (CL) aims to address this limitation and enable DNNs to accumulate knowledge…
Multimodal Graph Neural Networks (MGNNs) have shown strong potential for learning from multimodal attributed graphs, yet most existing approaches rely on tightly coupled architectures that suffer from prohibitive computational overhead. In…