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.

Unveiling Wireless Users' Locations via Modulation Classification-based Passive Attack

The broadcast nature of the wireless medium and openness of wireless standards, e.g., 3GPP releases 16-20, invite adversaries to launch various active and passive attacks on cellular and other wireless networks. This work identifies one such loose end of wireless standards and presents a novel passive attack method enabling an eavesdropper (Eve) to localize a line of sight wireless user (Bob) who is communicating with a base station or WiFi access point (Alice). The proposed attack involves two phases. In the first phase, Eve performs modulation classification by intercepting the downlink channel between Alice and Bob. This enables Eve to utilize the publicly available modulation and coding scheme (MCS) tables to do pesudo-ranging, i.e., the Eve determines the ring within which Bob is located, which drastically reduces the search space. In the second phase, Eve sniffs the uplink channel, and employs multiple strategies to further refine Bob's location within the ring. Towards the end, we present our thoughts on how this attack can be extended to non-line-of-sight scenarios, and how this attack could act as a scaffolding to construct a malicious digital twin map.

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.

Asymptotic Analysis of One-bit Quantized Box-Constrained Precoding in Large-Scale Multi-User Systems

This paper addresses the design of multi-antenna precoding strategies, considering hardware limitations such as low-resolution digital-to-analog converters (DACs), which necessitate the quantization of transmitted signals. The typical approach starts with optimizing a precoder, followed by a quantization step to meet hardware requirements. This study analyzes the performance of a quantization scheme applied to the box-constrained regularized zero-forcing (RZF) precoder in the asymptotic regime, where the number of antennas and users grows proportionally. The box constraint, initially designed to cope with low-dynamic range amplifiers, is used here to control quantization noise rather than for amplifier compatibility. A significant challenge in analyzing the quantized precoder is that the input to the quantization operation does not follow a Gaussian distribution, making traditional methods such as Bussgang's decomposition unsuitable. To overcome this, the paper extends the Gordon's inequality and introduces a novel Gaussian Min-Max Theorem to model the distribution of the channel-distorted precoded signal. The analysis derives the tight lower bound for the signal-to-distortion-plus-noise ratio (SDNR) and the bit error rate (BER), showing that optimal tuning of the amplitude constraint improves performance.

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.

Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching

Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectivity that delivers real-time and high-quality videos to the VR users. This paper proposes a field of view (FoV) aware caching for mobile edge computing (MEC)-enabled wireless VR network. In particular, the FoV of each VR user is cached/prefetched at the base stations (BSs) based on the caching strategies tailored to each BS. Specifically, decentralized and personalized federated learning (DP-FL) based caching strategies with guarantees are presented. Considering VR systems composed of multiple VR devices and BSs, a DP-FL caching algorithm is implemented at each BS to personalize content delivery for VR users. The utilized DP-FL algorithm guarantees a probably approximately correct (PAC) bound on the conditional average cache hit. Further, to reduce the cost of communicating gradients, one-bit quantization of the stochastic gradient descent (OBSGD) is proposed, and a convergence guarantee of $\mathcal{O}(1/\sqrt{T})$ is obtained for the proposed algorithm, where $T$ is the number of iterations. Additionally, to better account for the wireless channel dynamics, the FoVs are grouped into multicast or unicast groups based on the number of requesting VR users. The performance of the proposed DP-FL algorithm is validated through realistic VR head-tracking dataset, and the proposed algorithm is shown to have better performance in terms of average delay and cache hit as compared to baseline algorithms.

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.