Channel estimation is fundamental to wireless communications, yet it becomes increasingly challenging in massive multiple-input multiple-output (MIMO) systems where base stations employ hundreds of antennas. Traditional least-squares methods require prohibitive pilot overhead that scales with antenna count, while sparse estimation methods depend on precise channel models that may not always be practical. This paper proposes a model-free approach combining deep autoencoders and LSTM networks. The method first learns low-dimensional channel representations preserving temporal correlation through augmenting a channel charting-inspired loss function, then tracks these features to recover full channel information from limited pilots. Simulation results using ray-tracing datasets show that the proposed approach achieves up to 9 dB improvement in normalized mean square error compared to the least-squares methods under ill-conditioned scenarios, while maintaining scalability across MIMO configurations.

This paper presents the concept, design, channel modeling, beamforming algorithm development, 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 by adjusting the position and/or orientation of array elements, 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. Based on this ER-FAS channel model, 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. These results confirm that the proposed ER-FAS significantly enhances spectral efficiency in both near-field and far-field scenarios compared to conventional antenna arrays. To further validate this design, we fabricate 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 validated through prototype measurements conducted in an anechoic chamber. Additionally, several indoor communication experiments using a pair of software-defined radios demonstrate the superior received power and bit error rate performance of the ER-FAS prototype. This paper offers a comprehensive demonstration of a liquid-based ER-FAS array for wireless communication, incorporating a novel electromagnetically reconfigurable design, channel modeling, and beamforming, supported by simulation, hardware implementation, and experimental validation.

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 accurately acquiring channel state information 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 Cramér-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 address the limited link budgets and communication rates of single-satellite communication, we develop a multi-LEO cooperative beamforming strategy for downlink transmission that leverages cluster-wise satellite cooperation while maintaining reduced complexity. Theoretical analyses and numerical results confirm the effectiveness of the proposed framework in facilitating high-precision downlink positioning under practical imperfections, facilitating uplink channel estimation, and enabling efficient downlink communication.

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 communications 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.

This work studies the problems of channel estimation and beamforming for active reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) communication, incorporating the mutual coupling (MC) effect through an electromagnetically consistent model. We first demonstrate that MC can be incorporated into a compressed sensing (CS) 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) for the MC-aware estimation, effectively reducing the dimensionality of the sensing matrices. This method achieves estimation accuracy close to the direct MC-aware CS method with less overall computational complexity. Furthermore, we consider the joint optimization of RIS configuration, base station precoding, and user combining in a single-user MIMO system. We employ an alternating optimization strategy to optimize these three beamformers. 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 expansion. 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.

In this work, a novel design of electromagnetically reconfigurable antennas (ERAs) based on a fluid antenna system (FAS) is proposed, and the corresponding wireless channel model is established. Different from conventional antenna arrays with static elements, the electromagnetic characteristics of each array element in the proposed ERA can be flexibly reconfigured into various states, introducing electromagnetic degrees of freedom to enhance wireless system performance. Based on the proposed ERA design, the corresponding channel model is developed. Finally, full-wave simulations are conducted to validate the overall design concept. The results reveal that a gain enhancement of 2.5 dB is achieved at a beamforming direction.

The growing availability of low-Earth orbit (LEO) satellites, coupled with the anticipated widespread deployment of reconfigurable intelligent surfaces (RISs), opens up promising prospects for new localization paradigms. This paper studies RIS-aided localization using LEO satellite signals. The Cramér-Rao bound of the considered localization problem is derived, based on which an optimal RIS beamforming design that minimizes the derived bound is proposed. Numerical results demonstrate the superiority of the proposed beamforming scheme over benchmark alternatives, while also revealing that the synergy between LEO satellites and RISs holds the promise for localization.

High-frequency communication systems bring extremely large aperture arrays (ELAA) and large bandwidths, integrating localization and (bi-static) sensing functions without extra infrastructure. Such systems are likely to operate in the near-field (NF), where the performance of localization and sensing is degraded if a simplified far-field channel model is considered. However, when taking advantage of the additional geometry information in the NF, e.g., the encapsulated information in the wavefront, localization and sensing performance can be improved. In this work, we formulate a joint synchronization, localization, and sensing problem in the NF. Considering the array size could be much larger than an obstacle, the effect of partial blockage (i.e., a portion of antennas are blocked) is investigated, and a blockage detection algorithm is proposed. The simulation results show that blockage greatly impacts performance for certain positions, and the proposed blockage detection algorithm can mitigate this impact by identifying the blocked antennas.

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 three-dimensional (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.

Positioning is expected to support communication and location-based services in the fifth/sixth generation (5G/6G) networks. With the advent of reflective reconfigurable intelligent surfaces (RISs), radio propagation channels can be controlled, making high-accuracy positioning and extended service coverage possible. However, the passive nature of the RIS requires a signal source such as a base station (BS), which limits the positioning service in extreme situations, such as tunnels, dense urban areas, or complicated indoor scenarios where 5G/6G BSs are not accessible. In this work, we show that with the assistance of (at least) two RISs and sidelink communication between two user equipments (UEs), the absolute positions of these UEs can be estimated in the absence of BSs. A two-stage 3D sidelink positioning algorithm is proposed, benchmarked by the derived Cramér-Rao bounds. The effects of multipath and RIS profile designs on positioning performance are evaluated, and localization analyses are performed for various scenarios. Simulation results demonstrate the promising positioning accuracy of the proposed BS-free sidelink communication system. Additionally, we propose and evaluate several solutions to eliminate potential blind areas where positioning performance is poor, such as removing clock offset via round-trip communication, adding geometrical prior or constraints, as well as introducing more RISs.

As a key enabler of Terahertz (THz)-based wireless technologies, large-scale multiple-input-multiple-output systems are well known for their advantages in both communication and localization. Contrary to existing works that mostly focus on planar arrays, this paper first explores the potential of three-dimensional (3D) spatial array structures in joint localization and communication coverage enhancement. We consider a THz-band wireless system where a user is equipped with a 3D array receiving downlink far-field signals from multiple base stations with known positions and orientations over Rician fading channels. First, we derive the constrained Cramér-Rao bound (CCRB) for the localization (i.e., position and orientation estimation) performance, based on which we define the localization coverage metrics. Then, we derive the communication key performance indicators (KPIs) including instantaneous signal-to-noise ratio, outage probability, and ergodic capacity, and define the corresponding coverage metrics. To facilitate localization applications using 3D arrays, a maximum likelihood-based algorithm for joint user equipment (UE) position and orientation estimation is proposed, which is initialized by a least squares-based solution. Our numerical results show that the 3D array configuration offers overall higher coverage than the planar array w.r.t. both localization and communication KPIs, although with minor performance loss in certain UE positions and orientations. The proposed localization algorithm is also verified to be efficient in simulations as it attains the derived CCRB.

Reconfigurable intelligent surface (RIS)-assisted localization has attracted extensive attention as it can enable and enhance localization services in extreme scenarios. However, most existing works treat RISs as anchors with known positions and orientations, which is not realistic in applications with mobile or uncalibrated RISs. This work considers thejoint RIS calibration and user positioning(JrCUP) problem with an active RIS. We propose a novel two-stage method to solve the considered JrCUP problem. The first stage comprises a tensor-estimation of signal parameters via rotational invariance techniques (tensor-ESPRIT), followed by a channel parameters refinement using least-squares. In the second stage, a two-dimensional search algorithm is proposed to estimate the three-dimensional user and RIS positions, one-dimensional RIS orientation, and clock bias from the estimated channel parameters. The Cramér-Rao lower bounds of the channel parameters and localization parameters are derived to verify the effectiveness of the proposed tensor-ESPRIT-based algorithms. In addition, simulation results reveal that the active RIS can significantly improve the localization performance compared to the passive case under the same system power supply in practical regions. Moreover, we observe the presence of blind areas with limited JrCUP localization performance, which can be mitigated by either leveraging more prior information or deploying extra base stations.

Mutual coupling is increasingly important in reconfigurable intelligent surface (RIS)-aided communications, particularly when RIS elements are densely integrated in applications such as holographic communications. This paper experimentally investigates the mutual coupling effect among RIS elements using a mutual coupling-aware communication model based on scattering matrices. Utilizing a fabricated 1-bit quasi-passive RIS prototype operating in the mmWave band, we propose a practical model training approach based on a single 3D full-wave simulation of the RIS radiation pattern, which enables the estimation of the scattering matrix among RIS unit cells. The formulated estimation problem is rigorously convex with a limited number of unknowns un-scaling with RIS size. The trained model is validated through both full-wave simulations and experimental measurements on the fabricated RIS prototype. Compared to the conventional communication model that does not account for mutual coupling in RIS, the mutual coupling-aware model incorporating trained scattering parameters demonstrates improved prediction accuracy. Benchmarked against the full-wave simulated RIS radiation pattern, the trained model can reduce prediction error by up to approximately 10.7%. Meanwhile, the S-parameter between the Tx and Rx antennas is measured, validating that the trained model exhibits closer alignment with the experimental measurements. These results affirm the accuracy of the adopted model and the effectiveness of the proposed model training method.

This paper studies the sidelink positioning problem in a near-field wireless communication scenario with a single active reconfigurable intelligent surface (RIS). In this task, the positions of the transmitter and the receiver, as well as the clock bias between them are estimated jointly. To solve this problem, a time-orthogonal random codebook is adopted, which makes different channels separable. Then, we propose a coarse positioning method based on the sub-RIS division and the far-field approximation, followed by a least-squares-based refinement. The Cramér-Rao lower bound (CRLB) is derived, which verifies the efficiency of the proposed algorithms. The simulation studies reveal: (i) positioning performance is largely affected by the RIS size and the transmission power; (ii) the power allocation between the transmitter and the active RIS has an impact on the positioning accuracy, and there exists an optimal power allocation ratio that minimizes the CRLB.

In 5G/6G wireless systems, reconfigurable intelligent surfaces (RIS) can play a role as a passive anchor to enable and enhance localization in various scenarios. However, most existing RIS-aided localization works assume that the geometry of the RIS is perfectly known, which is not realistic in practice due to calibration errors. In this work, we derive the misspecified Cramér-Rao bound (MCRB) for a single-input-single-output RIS-aided localization system with RIS geometry mismatch. Specifically, unlike most existing works that use numerical methods, we propose a closed-form solution to the pseudo-true parameter determination problem for MCRB analysis. Simulation results demonstrate the validity of the derived pseudo-true parameters and MCRB, and show that the RIS geometry mismatch causes performance saturation in the high signal-to-noise ratio regions.

This paper considers the problem of estimating the position and orientation of a user equipped with a three-dimensional (3D) array receiving downlink far-field THz signals from multiple base stations with known positions and orientations. We derive the Cramér-Rao Bound for the localization problem and define the coverage of the considered system. We compare the error lower bound distributions of the conventional planar array and the 3D array configurations at different user equipment (UE) positions and orientations. Our numerical results obtained for array configurations with an equal number of elements show very limited coverage of the planar-array configuration, especially across the UE orientation range. Conversely, a 3D array configuration offers an overall higher coverage with minor performance loss in certain UE positions and orientations.