Electromagnetically Reconfigurable Fluid Antenna System for Wireless Communications: Design, Modeling, Algorithm, Fabrication, and Experiment

This paper presents the concept, design, channel modeling, beamforming algorithm, prototype fabrication, and experimental measurement of an electromagnetically reconfigurable fluid antenna system (ER-FAS), in which each FAS array element features electromagnetic (EM) reconfigurability. Unlike most existing FAS works that investigate spatial reconfigurability, the proposed ER-FAS enables direct control over the EM characteristics of each element, allowing for dynamic radiation pattern reconfigurability. Specifically, a novel ER-FAS architecture leveraging software-controlled fluidics is proposed, and corresponding wireless channel models are established. A low-complexity greedy beamforming algorithm is developed to jointly optimize the analog phase shift and the radiation state of each array element. The accuracy of the ER-FAS channel model and the effectiveness of the beamforming algorithm are validated through (i) full-wave EM simulations and (ii) numerical spectral efficiency evaluations. Simulation results confirm that the proposed ER-FAS significantly enhances spectral efficiency compared to conventional antenna arrays. To further validate this design, we fabricate hardware prototypes for both the ER-FAS element and array, using Galinstan liquid metal alloy, fluid silver paste, and software-controlled fluidic channels. The simulation results are experimentally verified through prototype measurements conducted in an anechoic chamber. Additionally, indoor communication trials are conducted via a pair of software-defined radios which demonstrate superior received power and bit error rate performance of the ER-FAS prototype. This work presents the first demonstration of a liquid-based ER-FAS in array configuration for enhancing communication systems.

Positioning-Aided Channel Estimation for Multi-LEO Satellite Downlink Communications

We investigate a multi-low Earth orbit (LEO) satellite system that simultaneously provides positioning and communication services to terrestrial user terminals. To address the challenges of channel estimation in LEO satellite systems, we propose a novel two-timescale positioning-aided channel estimation framework, exploiting the distinct variation rates of position-related parameters and channel gains inherent in LEO satellite channels. Using the misspecified Cramer-Rao bound (MCRB) theory, we systematically analyze positioning performance under practical imperfections, such as inter-satellite clock bias and carrier frequency offset. Furthermore, we theoretically demonstrate how position information derived from downlink positioning can enhance uplink channel estimation accuracy, even in the presence of positioning errors, through an MCRB-based analysis. To overcome the constraints of limited link budgets and communication rates associated with single-satellite-based communication, we develop a distributed beamforming strategy for downlink communication. This strategy allows LEO satellites to independently optimize their beamformers using local channel state information, eliminating the need for centralized processing while preserving the advantages of multi-satellite cooperative communication. Theoretical analyses and numerical results confirm the effectiveness of the proposed framework in achieving high-precision downlink positioning under practical imperfections, facilitating uplink channel estimation, and enabling efficient downlink communication.

Optimized Beamforming for Joint Bistatic Positioning and Monostatic Sensing

We investigate the performance tradeoff between \textit{bistatic positioning (BP)} and \textit{monostatic sensing (MS)} in a multi-input multi-output orthogonal frequency division multiplexing scenario. We derive the Cram\'er-Rao bounds (CRBs) for BP at the user equipment and MS at the base station. To balance these objectives, we propose a multi-objective optimization framework that optimizes beamformers using a weighted-sum CRB approach, ensuring the weak Pareto boundary. We also introduce two mismatch-minimizing approaches, targeting beamformer mismatch and variance matrix mismatch, and solve them distinctly. Numerical results demonstrate the performance tradeoff between BP and MS, revealing significant gains with the proposed methods and highlighting the advantages of minimizing the weighted-sum mismatch of variance matrices.

Privacy Preservation in MIMO-OFDM Localization Systems: A Beamforming Approach

We investigate an uplink MIMO-OFDM localization scenario where a legitimate base station (BS) aims to localize a user equipment (UE) using pilot signals transmitted by the UE, while an unauthorized BS attempts to localize the UE by eavesdropping on these pilots, posing a risk to the UE's location privacy. To enhance legitimate localization performance while protecting the UE's privacy, we formulate an optimization problem regarding the beamformers at the UE, aiming to minimize the Cram\'er-Rao bound (CRB) for legitimate localization while constraining the CRB for unauthorized localization above a threshold. A penalty dual decomposition optimization framework is employed to solve the problem, leading to a novel beamforming approach for location privacy preservation. Numerical results confirm the effectiveness of the proposed approach and demonstrate its superiority over existing benchmarks.

Integrated Positioning and Communication via LEO Satellites: Opportunities and Challenges

Low Earth orbit (LEO) satellites, as a prominent technology in the 6G non-terrestrial network, offer both positioning and communication capabilities. While these two applications have each been extensively studied and have achieved substantial progress in recent years, the potential synergistic benefits of integrating them remain an underexplored yet promising avenue. This article comprehensively analyzes the integrated positioning and communication (IPAC) systems on LEO satellites. By leveraging the distinct characteristics of LEO satellites, we examine how communication systems can enhance positioning accuracy and, conversely, how positioning information can be exploited to improve communication efficiency. In particular, we present two case studies to illustrate the potential of such integration. Finally, several key open research challenges in the LEO-based IPAC systems are discussed.

6G RIS-aided Single-LEO Localization with Slow and Fast Doppler Effects

6G networks aim to enable applications like autonomous driving by providing complementary localization services through key technologies such as non-terrestrial networks (NTNs) with low Earth orbit (LEO) satellites and reconfigurable intelligent surfaces (RIS). Prior research in 6G localization using single LEO, multi-LEO, and multi-LEO multi-RIS setups has limitations: single LEO lacks the required accuracy, while multi-LEO/RIS setups demand many visible satellites and RISs, which is not always feasible in practice. This paper explores the novel problem of localization with a single LEO satellite and a single RIS, bridging these research areas. We present a comprehensive signal model accounting for user carrier frequency offset (CFO), clock bias, and fast and slow Doppler effects. Additionally, we derive a low-complexity estimator that achieves theoretical bounds at high signal-to-noise ratios (SNR). Our results demonstrate the feasibility and accuracy of RIS-aided single-LEO localization in 6G networks and highlight potential research directions.

Mutual Coupling-Aware Channel Estimation and Beamforming for RIS-Assisted Communications

This work studies the problems of channel estimation and beamforming for active reconfigurable intelligent surface~(RIS)-assisted communication, incorporating the mutual coupling~(MC) effect through an electromagnetically consistent model based on scattering parameters. We first demonstrate that MC can be incorporated into a compressed sensing~(CS) estimation formulation, albeit with an increase in the dimensionality of the sensing matrix. To overcome this increased complexity, we propose a two-stage strategy. Initially, a low-complexity MC-unaware CS estimation is performed to obtain a coarse channel estimate, which is then used to implement a dictionary reduction (DR) technique, effectively reducing the dimensionality of the sensing matrices. This method achieves low complexity comparable to the conventional MC-unaware approach while providing estimation accuracy close to that of the direct MC-aware CS method. We then consider the joint optimization of RIS configuration and base station (BS) combining in an uplink single-input multiple-output system. We employ an alternating optimization strategy where the BS combiner is derived in closed form for a given RIS configuration. The primary challenge lies in optimizing the RIS configuration, as the MC effect renders the problem non-convex and intractable. To address this, we propose a novel algorithm based on the successive convex approximation (SCA) and the Neumann series. Within the SCA framework, we propose a surrogate function that rigorously satisfies both convexity and equal-gradient conditions to update the iteration direction. Numerical results validate our proposal, demonstrating that the proposed channel estimation and beamforming methods effectively manage the MC in RIS, achieving higher spectral efficiency compared to state-of-the-art approaches.

Calibration in RIS-aided Integrated Sensing, Localization and Communication Systems

Reconfigurable intelligent surfaces (RISs) are key enablers for integrated sensing and communication (ISAC) systems in the 6G communication era. With the capability of dynamically shaping the channel, RISs can enhance communication coverage. Additionally, RISs can serve as additional anchors with high angular resolution to improve localization and sensing services in extreme scenarios. However, knowledge of anchors' states such as position, orientation, and hardware impairments are crucial for localization and sensing applications, requiring dedicated calibration, including geometry and hardware calibration. This paper provides an overview of various types of RIS calibration, their impacts, and the challenges they pose in ISAC systems.

A Statistical Evaluation of Coherence Time for Non-Terrestrial Communications

Non-terrestrial networks (NTNs) are recognized as essential components of the next-generation communication systems. This letter evaluates the coherence time for non-terrestrial channels, revealing that the rapid mobility of non-terrestrial base stations (BSs) substantially diminishes channel coherence time. Our results demonstrate that the existence and enhancement of the line-of-sight channel play a crucial role in extending coherence time. Furthermore, unlike terrestrial networks, adjustments to receiver beamwidth seldom affect coherence time with a highspeed motion of the BS in NTNs.

LEO- and RIS-Empowered User Tracking: A Riemannian Manifold Approach

Low Earth orbit (LEO) satellites and reconfigurable intelligent surfaces (RISs) have recently drawn significant attention as two transformative technologies, and the synergy between them emerges as a promising paradigm for providing cross-environment communication and positioning services. This paper investigates an integrated terrestrial and non-terrestrial wireless network that leverages LEO satellites and RISs to achieve simultaneous tracking of the 3D position, 3D velocity, and 3D orientation of user equipment (UE). To address inherent challenges including nonlinear observation function, constrained UE state, and unknown observation statistics, we develop a Riemannian manifold-based unscented Kalman filter (UKF) method. This method propagates statistics over nonlinear functions using generated sigma points and maintains state constraints through projection onto the defined manifold space. Additionally, by employing Fisher information matrices (FIMs) of the sigma points, a belief assignment principle is proposed to approximate the unknown observation covariance matrix, thereby ensuring accurate measurement updates in the UKF procedure. Numerical results demonstrate a substantial enhancement in tracking accuracy facilitated by RIS integration, despite urban signal reception challenges from LEO satellites. In addition, extensive simulations underscore the superior performance of the proposed tracking method and FIM-based belief assignment over the adopted benchmarks. Furthermore, the robustness of the proposed UKF is verified across various uncertainty levels.