Fluid antenna systems (FAS) have emerged as a promising paradigm for wireless communications, enabling channel reconfigurability that offers a novel spatial degree of freedom. Nevertheless, efficiently acquiring accurate and high-resolution channel state information (CSI) in FAS remains challenging, primarily due to its dynamic spatial structure and limited coherence time. This paper proposes a novel diffusion framework that takes the partially observed CSI matrix as the terminal state of the Markov chain and operates exclusively on the unobserved ports. Built upon this framework, we design a UNet-based architecture, termed the channel extrapolation UNet (CEUNet), that integrates modified MaxViT (mMaxViT) blocks and residual blocks (ResBlocks) to jointly capture local and global channel dependencies for accurate CSI extrapolation. Extensive experiments on the Jakes’ channel model are conducted to evaluate CEUNet. Numerical results show that CEUNet consistently outperforms state-of-the-art deep learning models in estimation accuracy across all signal-to-noise ratios and observation ratios, even with only two sampling steps. Furthermore, a comprehensive complexity analysis is conducted to compare the computational efficiency of CEUNet with that of the baseline models, while ablation studies are carried out to quantitatively evaluate the contribution of each component integrated into the proposed CEUNet.
Xue-Feng Wang, Yu-Hang Li, Yang Lu et al.· IEEE Transactions on Wireles...· 0 citations
Emerging technologies including wireless power transfer (WPT), integrated sensing and communication (ISAC), and fluid antennas (FAs), have significantly advanced the capabilities and performance of modern satellite communication systems. This paper investigates an FA-assisted integrated sensing, communication, and power transfer (ISCPT) framework for low Earth orbit (LEO) satellite networks, which operates in two phases: 1) an energy-transfer and target-sensing phase (Phase I), and 2) an information-transmission phase (Phase II). Specifically, in Phase I, a space solar power satellite (SSPS) transmits a dual-functional waveform to simultaneously charge multiple LEO satellites and illuminate a sensing target, while in Phase II, these LEO satellites coordinately serve multiple ground user equipments (UEs) leveraging the harvested energy. We formulate a sum-rate maximization problem subject to the SSPS’s transmit power constraint, LEO satellites’ energy harvesting and sensing requirements, UEs’ information rate demands, and the FAs’ movable regions. To tackle the highly-coupled and non-convex optimization problem, a three-stage alternating optimization (AO) algorithm is proposed, which decomposes it into resource allocation, SSPS-side FA placement, and LEO-side FA placement subproblems. In particular, the resource allocation subproblem is reformulated by adopting the Cauchy–Schwarz inequality and semidefinite relaxation (SDR), and is efficiently tackled via the successive convex approximation method. The two FA placement subproblems are addressed leveraging trust-region-based optimization. Simulation results validate the superior performance gains of the proposed algorithm over seven benchmarks and demonstrate that FAs can enhance multi-functional wireless services by adjusting inter-channel diversity according to service types. Notably, a non-trivial trade-off arises among FAs-enabled multi-functional services requiring distinct channel characteristics, as FAs cannot simultaneously provide optimal channel conditions for all services.
Weihao Mao, Yang Lu, Dong Yang et al.· IEEE Journal on Selected Are...· 1 citation
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