AI & ML
Dynamic-Aware LC-DMA Beam Tracking for LEO Satellite Communications
arXiv eess.SP0 views
arXiv:2610.41612v1 Announce Type: new Abstract: Liquid crystal (LC)-based dynamic metasurface antennas (DMAs) offer a power-efficient, low-cost, and scalable solution for low Earth orbit (LEO) satellite communications where large apertures are needed to compensate for large path loss in the Earth-satellite link. However, beam tracking with LC-DMAs is governed by two coupled dynamics: the required time-varying beam direction caused by user and satellite mobility, and the finite response time of the LC phase shifters. This raises the question of whether LC-DMAs can reconfigure sufficiently fast to support high-mobility LEO satellite links. To address this problem, we develop a dynamic-aware beam-tracking framework that explicitly accounts for both dynamics. In particular, we formulate an optimization problem to maximize the cumulative received signal-to-noise ratio (SNR) over a predictive tracking horizon subject to the physical dynamics of the LC unit cells as well as transmit power constraints. To solve this non-convex problem efficiently, we develop an alternating optimization framework combining a closed-form maximum ratio transmission (MRT) digital precoder and maximum ratio combining (MRC) with a parallel, closed-form dynamic-aware phase-shift update. Simulation results show that when the time response of LC-DMAs is properly accounted for, it can support practical mobility scenarios. Moreover, the proposed dynamic-aware algorithm maintains stable SNR and outperforms conventional designs, particularly at high vehicle velocities and low orbital azimuth angles.
Source:https://arxiv.org/abs/2610.41612PreviousLearning Joint Synchronization, Channel Estimation, and Decoding: Comparison with a Finite-Blocklength Achievability BoundNextLimitation of Bessel Beams in Near Field Communications: A Depth of Field Perspective
Related posts
Hypergraph Generation via Structured Stochastic Diffusion
0arXiv stat.CO
Sparse Probabilistic Richardson Extrapolation
0arXiv stat.CO
Dynamic Bayesian Predictive Stacking via Markovian Spatiotemporal Propagation
0arXiv stat.CO
Coherent forecast combination for linearly constrained multiple time series
0arXiv stat.CO
New optimisations for exact, scalable 2D Gaussian process inference, with applications to astronomical time-series
0arXiv stat.CO
A tractable transition function for the Wright-Fisher diffusion with selection via gamma duality
0arXiv stat.CO