With the development of the Starlink network, satellite communication has become a primary means of Internet access in remote areas. Leveraging the ubiquitous downlink from Low-Earth-Orbit (LEO) satellites and the portability of ground stations, this paper explores the feasibility of using Starlink downlink signals as an illuminating signal for Ambient Backscatter Communication (AmBC). We propose an AmBC system that operates in the Ku-band, utilizing Starlink downlink signals. Furthermore, we analyze the impact factors for AmBC performance and derive its analytical expression. Numerical simulation results demonstrate that the AmBC based on the Starlink downlink achieves a Bit Error Rate (BER) on the order of 10(-2), highlighting its potential for practical applications.
As a pivotal enabling technology for 6G wireless networks, extra-large scale multiple-input multiple-output (XL-MIMO) systems present unique hybrid-field channel character-istics that necessitate joint consideration of near-field and far-field components. While existing solutions predominantly focus on pure near-field scenarios, the critical challenge in hybrid-field environments lies in effectively decoupling these spatially diverse channel components under practical constraints. This paper proposes a novel low overhead channel estimation frame-work for hybrid-field XL-MIMO systems. Our approach begins by reconstructing the angular domain channel representation through optimized hybrid beamforming architecture. Specifically, we develop a phase shift matrix optimization strategy that enables accurate angular domain recovery under stringent pilot constraints. The proposed spatial filtering technique then achieves effective field separation: far-field components are extracted through joint analysis of beamwidth characteristics and amplitude distributions, while residual near-field components are sub-sequently recovered via compressed sensing-based reconstruction. The proposed algorithm can reconstruct hybrid-field channel estimation with low pilot overhead.
This paper investigates content uploading in spatialaerial integrated low altitude networks (SALAN), where the distributed low altitude terminals (LATs) are mobile and far away from terrestrial communication infrastructures. In the SALAN, LATs can upload their data to a satellite access point through multiple flexibly deployable aerial relays (ARs). To minimize the maximum uploading latency of LATs, we formulate the AR trajectory planning and resource allocation (ARTPRA) problem and solve it by decomposing it into two sub-problems, i.e., an upload rate control (URC) sub-problem, and an AR trajectory planning and frequency band management (ARTPFM) sub-problem. We prove that the optimal solution to the URC sub-problem can be achieved by solving a series of linear programming problems and propose a multi-agent deep reinforcement learning (DRL)-based framework to solve the ARTPFM sub-problem. Extensive experimental results show that our approach achieves very low offline training complexity for the ARTPFM framework and outperforms the benchmark approaches in terms of latency for uploading tasks, empirical success rate, and online computational time for content uploading.
Remote inference, where sensors acquire data and transmit compressed features to a remote server for inference, plays a key role in numerous applications. A common remote inference task is direction of arrival (DoA) estimation, where the sensed data is used for localizing multiple sources. Traditional methods require the sensor to downstream raw wideband data, leading to increased latency and spectral inefficiency. In this work, we propose Remote SubspaceNet, a deep neural network (DNN)-aided remote inference framework that enables interpretable, low-latency, and progressively refined DoA estimation at the server. Remote SubspaceNet integrates DNN-based feature extraction with learned vector quantization and subspace-based inference techniques, learning a single quantization codebook that supports successively refined DoA recovery. We demonstrate that Remote SubspaceNet accurately estimates DoAs across varying bit budgets, significantly reducing communication latency while maintaining interpretability and robustness.
In this paper, we investigate the problem of spatial non-stationary channel estimation for near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Specifically, we propose a mixed model- and data-driven deep learning (DL) approach that achieves accurate near-field channel estimation under spatial non-stationary effects while maintaining low computational complexity. Our approach first exploits the polar-domain sparsity of the near-field channel to develop an unfolding network for efficient on-grid sparse channel recovery. This network is designed by unfolding the inverse-free variational Bayesian inference (IF-VBI) algorithm, referred to as VBI-Net. Moreover, to mitigate the energy leakage effect inherent in ongrid methods and the estimation error introduced by spatial non-stationary effects, we further enhance channel estimation accuracy by utilizing a data-driven deep learning framework. Simulation results validate that our proposed mixed model- and data-driven DL approach significantly outperforms existing state-of-the-art schemes, validating its effectiveness and superiority.
