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.

ELAA Near-Field Localization and Sensing with Partial Blockage Detection

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.

LEO Satellite and RIS: Two Keys to Seamless Indoor and Outdoor Localization

The contemporary landscape of wireless technology underscores the critical role of precise localization services. Traditional global navigation satellite systems (GNSS)-based solutions, however, fall short when it comes to indoor environments, and existing indoor localization techniques such as electromagnetic fingerprinting methods face challenges of high implementation costs and limited coverage. This article explores an innovative solution that seamlessly blends low Earth orbit (LEO) satellites with reconfigurable intelligent surfaces (RISs), unlocking its potential for realizing uninterrupted indoor and outdoor localization with global coverage. By leveraging the strong signal reception of the LEO satellite signals and capitalizing on the radio environment-reshaping capability of RISs, the integration of these two technologies presents a vision of a future where localization services transcend existing constraints. After a comprehensive review of the distinctive attributes of LEO satellites and RISs, we evaluate the localization error bounds for the proposed collaborative system, showcasing their promising performance on simultaneous indoor and outdoor localization. To conclude, we engage in a discussion on open problems and future research directions for LEO satellite and RIS-enabled localization.

Mutual Coupling in RIS-Aided Communication: Experimental Validation and Performance Evaluation

This paper explores the mutual coupling in the reconfigurable intelligent surface (RIS)-aided communication. Despite the existence of several mutual coupling-aware models for RIS-aided communication, a notable gap remains due to the lack of experimental validation. This paper bridges this gap by first introducing a novel model training approach based on the 3D full-wave simulation and subsequently validating the obtained model via experimental measurements in a 1-bit quasi-passive RIS prototype operating in the mmWave band. Comparative analyses reveal precision in both the employed mutual coupling-aware model and the assessed model parameters, offering a realistic evaluation of mutual coupling in authentic RIS hardware. Utilizing the validated mutual coupling-aware communication model, we systematically examine the impact of mutual coupling on communication performance by adopting the achievable rate as a performance indicator. Our results reveal that the mutual coupling in RIS exhibits heightened significance with increased RIS amplitude gains and showcases a frequency-dependent effect.

Beamforming Design and Performance Evaluation for RIS-aided Localization using LEO Satellite Signals

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 Cram\'er-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 of achieving localization accuracy at the meter or even sub-meter level.

On the Impact of Mutual Coupling on RIS-Assisted Channel Estimation

Amid the demand for densely integrated elements in holographic reconfigurable intelligent surfaces (RISs), the mutual coupling effect has gained prominence. By performing a misspecified Cram\'er-Rao bound analysis within an electromagnetics-compliant communication model, this letter offers a quantitative evaluation of the impact of mutual coupling on RIS-assisted channel estimation. Our analysis provides insights into situations where mutual coupling can be disregarded safely. The numerical results reveal that within practical scenarios, closer integration of RIS elements or the enlargement of RIS size accentuates the impact of neglecting mutual coupling. In addition, even with mutual coupling-aware setups, excessively tight RIS element spacing can lead to substantial degradation in the channel estimation performance.