THz-Band Near-Field RIS Channel Modeling for Linear Channel Estimation

Reconfigurable intelligent surface (RIS)-aided terahertz (THz)-band communications are promising enablers for future wireless networks. However, array densification at high frequencies introduces significant challenges in accurate channel modeling and estimation, particularly with THz-specific fading, mutual coupling (MC), spatial correlation, and near-field effects. In this work, we model THz outdoor small-scale fading channels using the mixture gamma (MG) distribution, considering absorption losses, spherical wave propagation, MC, and spatial correlation across large base stations and RISs. We derive the distribution of the cascaded RIS-aided channel and investigate linear channel estimation techniques, analyzing the impact of various channel parameters. Numerical results based on precise THz parameters reveal that accounting for spatial correlation, MC, and near-field modeling substantially enhances estimation accuracy, especially in ultra-massive arrays and short-range scenarios. These results underscore the importance of incorporating these effects for precise, physically consistent channel modeling.

Tri-Hybrid Multi-User Precoding Using Pattern-Reconfigurable Antennas: Fundamental Models and Practical Algorithms

The integration of pattern-reconfigurable antennas into hybrid multiple-input multiple-output (MIMO) architectures presents a promising path toward high-efficiency and low-cost transceiver solutions. Pattern-reconfigurable antennas can dynamically steer per-antenna radiation patterns, enabling more efficient power utilization and interference suppression. In this work, we study a tri-hybrid MIMO architecture for multi-user communication that integrates digital, analog, and antenna-domain precoding using pattern-reconfigurable antennas. For characterizing the reconfigurability of antenna radiation patterns, we develop two models -- Model~I and Model~II. Model~I captures realistic hardware constraints through limited pattern selection, while Model~II explores the performance upper bound by assuming arbitrary pattern generation. Based on these models, we develop two corresponding tri-hybrid precoding algorithms grounded in the weighted minimum mean square error (WMMSE) framework, which alternately optimize the digital, analog, and antenna precoders under practical per-antenna power constraints. Realistic simulations conducted in ray-tracing generated environments are utilized to evaluate the proposed system and algorithms. The results demonstrate the significant potential of the considered tri-hybrid architecture in enhancing communication performance and hardware efficiency. However, they also reveal that the existing hardware is not yet capable of fully realizing these performance gains, underscoring the need for joint progress in antenna design and communication theory development.

Tri-Hybrid Multi-User Precoding Based on Electromagnetically Reconfigurable Antennas

The tri-hybrid precoding architecture based on electromagnetically reconfigurable antennas (ERAs) is a promising solution for overcoming key limitations in multiple-input multiple-output communication systems. Aiming to further understand its potential, this paper investigates the tri-hybrid multi-user precoding problem using pattern reconfigurable ERAs. To reduce model complexity and improve practicality, we characterize each antenna's radiation pattern using a spherical harmonics decomposition. While mathematically tractable, this approach may lead to over-optimized patterns that are physically unrealizable. To address this, we introduce a projection step that maps the optimized patterns onto a realizable set. Simulation results demonstrate that spherical harmonics-based radiation pattern optimization significantly enhances sum rate performance. However, after projection onto a realizable set obtained from real ERA hardware, the performance gain is notably reduced or even negligible, underscoring the need for more effective projection techniques and improved reconfigurable antenna hardware.

Diversity Analysis for Indoor Terahertz Communication Systems under Small-Scale Fading

Harnessing diversity is fundamental to wireless communication systems, particularly in the terahertz (THz) band, where severe path loss and small-scale fading pose significant challenges to system reliability and performance. In this paper, we present a comprehensive diversity analysis for indoor THz communication systems, accounting for the combined effects of path loss and small-scale fading, with the latter modeled as an $\alpha-\mu$ distribution to reflect THz indoor channel conditions. We derive closed-form expressions for the bit error rate (BER) as a function of the reciprocal of the signal-to-noise ratio (SNR) and propose an asymptotic expression. Furthermore, we validate these expressions through extensive simulations, which show strong agreement with the theoretical analysis, confirming the accuracy and robustness of the proposed methods. Our results show that the diversity order in THz systems is primarily determined by the combined effects of the number of independent paths, the severity of fading, and the degree of channel frequency selectivity, providing clear insights into how diversity gains can be optimized in high-frequency wireless networks.

Performance and Complexity Analysis of Terahertz-Band MIMO Detection

Achieving terabit-per-second (Tbps) data rates in terahertz (THz)-band communications requires bridging the complexity gap in baseband transceiver design. This work addresses the signal processing challenges associated with data detection in THz multiple-input multiple-output (MIMO) systems. We begin by analyzing the trade-offs between performance and complexity across various detection schemes and THz channel models, demonstrating significant complexity reduction by leveraging spatial parallelizability over subspaces of correlated THz MIMO channels. We derive accurate detection error probability bounds by accounting for THz-specific channel models and mismatches introduced by subspace decomposition. Building on this, we propose a subspace detector that integrates layer sorting, QR decomposition, and channel-matrix puncturing to balance performance loss and parallelizability. Furthermore, we introduce a channel-matrix reuse strategy for wideband THz MIMO detection. Simulations over accurate, ill-conditioned THz channels show that efficient parallelizability achieves multi-dB performance gains, while wideband reuse strategies offer computational savings with minimal performance degradation.

Joint Bistatic Positioning and Monostatic Sensing: Optimized Beamforming and Performance Tradeoff

We investigate joint bistatic positioning (BP) and monostatic sensing (MS) within a multi-input multi-output orthogonal frequency-division system. Based on the derived Cram\'er-Rao Bounds (CRBs), we propose novel beamforming optimization strategies that enable flexible performance trade-offs between BP and MS. Two distinct objectives are considered in this multi-objective optimization problem, namely, enabling user equipment to estimate its own position while accounting for unknown clock bias and orientation, and allowing the base station to locate passive targets. We first analyze digital schemes, proposing both weighted-sum CRB and weighted-sum mismatch (of beamformers and covariance matrices) minimization approaches. These are examined under full-dimension beamforming (FDB) and low-complexity codebook-based power allocation (CPA). To adapt to low-cost hardwares, we develop unit-amplitude analog FDB and CPA schemes based on the weighted-sum mismatch of the covariance matrices paradigm, solved using distinct methods. Numerical results confirm the effectiveness of our designs, highlighting the superiority of minimizing the weighted-sum mismatch of covariance matrices, and the advantages of mutual information fusion between BP and MS.

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.