This paper proposes a novel IRS-aided framework for secure sensing, which aims to minimize the worst-case Cram´er-Rao Bound (WC-CRB) within an entire surveillance area by optimizing the IRS reflecting beamforming, enabling reliable and secure localization of arbitrary and unknown targets. Specifically, we first establish a general IRS-aided localization coverage model and derive the closed-form expression for the CRB of an arbitrary point, which reveals the relationship between the localization error bound and the Fisher information of the angle of arrival (AOA), angle of departure (AOD) and delay. To solve this challenging min-max optimization problem, we design efficient algorithms for different area types. For sector area, we first represent the Fisher information as trigonometric polynomials, then construct the WC-CRB coverage constraint as a non-negativity problem of these polynomials, and finally approximate it as an efficiently solvable semidefinite program (SDP). For the more challenging case of arbitrarily shaped area, we propose a two-tiered solution comprising a low-complexity heuristic algorithm based on geometric approximation and a high-performance detailed design that accurately solves the problem by decomposing the irregular boundary into multiple continuous segments. Numerical simulations validate the superiority of the proposed framework, demonstrating that our designs significantly outperform various benchmark schemes in terms of robustness and performance uniformity. The results show that the framework not only effectively reduces the WC-CRB but also achieves a highly uniform performance coverage across the entire area, providing a reliable and efficient solution for practical localization security applications.

Simultaneous wireless information and power transfer (SWIPT) has been envisioned as a promising technology to support ubiquitous connectivity and reliable sustainability in Internet-of-Things (IoT) networks, which, however, generally suffers from severe attenuation caused by long distance propagation, leading to inefficient wireless power transfer (WPT) for energy harvesting receivers (EHRs). This paper proposes to introduce emerging intelligent reflecting surface (IRS) and movable antenna (MA) technologies into SWIPT systems aiming at enhancing information transmission for information decoding receivers (IDRs) and improving receive power of EHRs. We consider to maximize the weighted sum-rate of IDRs via jointly optimizing the active and passive beamforming at the base station (BS) and IRS, respectively, together with the positions of MAs, while guaranteeing the individual requirement of each EHR. To tackle this challenging task due to the non-convexity of associated optimization, we develop an efficient algorithm combining weighted minimal mean square error (WMMSE), block coordinate descent (BCD), majorization-minimization (MM), and penalty duality decomposition (PDD) frameworks. Besides, we present a feasibility characterization method to examine the achievability of EHRs’ requirements. Simulation results demonstrate the significant benefits of our proposed solutions. Particularly, the optimized IRS configuration may exhibit higher performance gain than MA counterpart under our considered scenario.

This paper investigates an intelligent reflecting surface (IRS)-aided movable antenna (MA) system, where multiple IRSs cooperate with a multi-MA base station to extend wireless coverage to multiple target areas. The objective is to maximize the worst-case signal-to-noise ratio (SNR) across all locations within these areas through joint optimization of MA positions, IRS phase shifts, and transmit beamforming. To achieve this while balancing the performance-cost trade-off, we propose three coverage-enhancement schemes: thearea-adaptive MA-IRSscheme, where both the MA positions and IRS phase shifts are adaptively adjusted for each target area; thearea-adaptive MA-staIRSscheme, where only the MA positions are adjusted, while the IRS phase shifts remain unchanged after initial configuration (withstaIRSdenoting static IRSs); and theshared MA-staIRSscheme, where a common MA placement and static IRS configuration are applied across all areas. These schemes lead to challenging non-convex optimization problems with implicit objective functions, which are difficult to solve optimally. To address these problems, we propose a general algorithmic framework that can be applied to solve each problem efficiently albeit suboptimally. Simulation results demonstrate that: 1) the proposed MA-based schemes consistently outperform their fixed-position antenna (FPA)-based counterparts under both area-adaptive and static IRS configurations, with the area-adaptive MA-IRS scheme achieving the highest worst-case SNR; 2) as transmit antennas are typically far fewer than IRS elements, the area-adaptive MA-staIRS scheme may underperform the baseline FPA scheme with area-adaptive IRSs in terms of the worst-case SNR, but a modest increase in antenna number can reverse this trend; 3) under a fixed total cost, the optimal MA-to-IRS-element ratio for the worst-case SNR maximization is empirically found to be proportional to the reciprocal of their unit cost ratio.

Multi-satellite cooperative transmission (MSCT) is a promising paradigm to enhance spectral efficiency in Low Earth Orbit constellations. However, heterogeneous Doppler shifts and coupled multi-satellite interference hinder the capacity improvement of conventional precoding in multiple-input multiple-output (MIMO). Although orthogonal time-frequency space (OTFS) modulation exhibits robustness against doubly-selective channels, its direct application in massive MIMO faces prohibitive computational complexity. To address these challenges, this paper proposes a joint precoding and link scheduling (JPL) design for OTFS-based MSCT systems. Specifically, we formulate a sum-rate maximization problem that couples continuous precoding matrices with discrete link indicators, and decompose it into two tractable subproblems. For precoder design, based on regularized zero-forcing (RZF)-criterion, we present single-satellite precoding (SSP) and multi-satellite precoding (MSP) in the delay-Doppler domain for different MSCT modes, and optimize per-satellite regularization coefficients to maximize the sum-rate. Leveraging the quasi-banded MIMO-OTFS structure, a low-complexity RZF algorithm is developed to reduce the cubic complexity to quadratic order without performance loss. Based on random matrix theory, we conduct asymptotic analysis and derive the deterministic equivalents of sum-rate for SSP and MSP. For link scheduling, a two-stage heuristic algorithm is designed based on tabu search to iteratively optimize link indicators for maximizing the sum-rate. By alternately optimizing precoding matrices with each scheduling iteration, the JPL algorithm is proposed to achieve near-optimal sum-rate performance with polynomial computational complexity. Numerical results demonstrate the proposed schemes significantly improve the sum-rate performance compared to existing works.

Conventional antenna arrays rely primarily on digital beamforming for spatial control. While adding more elements can narrow beamwidth and suppress interference, such scaling incurs prohibitive hardware and power costs. Rotatable antennas (RAs), which allow mechanical or electronic adjustment of element orientations, introduce a new degree of freedom to exploit spatial flexibility without enlarging the array. By dynamically optimizing orientations, RAs can substantially improve desired link alignment and interference suppression. This paper investigates RA-enabled multiple-input single-output (MISO) interference channels under co-channel spectrum sharing and formulates a weighted sum-rate maximization problem that jointly optimizes transmit beamforming and antenna orientations. To tackle this nonconvex problem, we develop an alternating optimization (AO) framework that integrates weighted minimum mean-square error (WMMSE)-based beamforming with Frank-Wolfe-based orientation updates. To reduce complexity, we further study orientation optimization under maximum-ratio transmission (MRT) and zero-forcing (ZF) beamforming schemes. For finite-resolution actuators, we construct spherical Fibonacci codebooks and design a cross-entropy method (CEM)-based algorithm for discrete orientation selection. Simulations show that integrating RAs with conventional beamforming markedly increases weighted sum-rate, with gains rising with element directivity. Under discrete orientation control, the proposed CEM algorithm consistently outperforms the nearest-projection baseline.

Reconfigurable intelligent surfaces (RISs) enhance wireless systems by reshaping propagation environments. However, dynamic metasurfaces (MSs) with numerous phase-shift elements may incur undesired hardware costs and control overhead. In contrast, static MSs (SMSs), configured with static phase shifts that are pre-designed for specific communication demands, offer a cost-effective alternative by eliminating electronic element-wise tuning. Nevertheless, SMSs typically support only a single beam pattern, limiting flexibility in dynamic and multi-user scenarios. In this paper, we propose a novel Movable Intelligent Surface (MIS) technology that enables dynamic beamforming while maintaining static phase shifts. Specifically, we design a MIS architecture comprising two closely stacked transmissive MSs: a larger fixed-position MS 1 and a smaller movable MS 2. By differentially shifting MS 2’s position relative to MS 1, the MIS synthesizes distinct desired beam patterns, overcoming the SMSs’ single-pattern limitation. Then, we model the interaction between MS 2 and MS 1 using binary selection matrices and padding vectors, which allow us to formulate a new optimization problem that jointly designs the MIS phase shifts and selects shifting positions for worst-case signal-to-noise ratio (SNR) maximization. This position selection, equal to beam pattern scheduling, offers a new degree of freedom for RIS-aided systems. To solve the intractable problem, we develop an efficient algorithm that handles unit-modulus and binary constraints and employs manifold optimization methods. Finally, extensive validation results are provided, including both experimental and numerical analysis. We first implement a MIS prototype and perform proof-of-concept experiments, demonstrating the MIS’s ability to synthesize desired beam patterns that achieve beam steering. Numerical results further validate our theoretical modeling and the proposed algorithm. Encouragingly, by introducing a movable MS 2 with a few elements, MIS effectively offers beamforming flexibility for significantly improved performance compared to SMSs. We also draw insights into the optimal MIS configuration and element allocation strategy.

Satellite communication (SatCom) is regarded as a key enabler for bridging connectivity and capacity gaps in sixth-generation (6G) networks. However, the proliferation of Low Earth Orbit (LEO) satellites raises significant intersystem interference risks with Geostationary Earth Orbit (GEO) systems. This paper introduces a cooperative multi-satellite multi-reconfigurable intelligent surface (RIS) transmission framework to mitigate such interference while enhancing LEO SatCom performance. Specifically, cooperative beamforming is designed under a non-coherent cell-free paradigm, considering both adaptive and max ratio (MR) precoding, as well as statistical and two-timescale channel state information (CSI), aiming to synthesize the advantages of cell-free and RIS into SatCom in a practical way. Firstly, an alternating optimization (AO)-based design leveraging statistical CSI with adaptive precoding is proposed. Then, we propose a power allocation algorithm under MR precoding with given RIS phase shifts obtained from the former, along with a direct two-stage design bypassing prior results. Additionally, we extend derived closed-form expressions and proposed algorithms to exploit two-timescale CSI. Numerical results demonstrate the impact of intersystem interference mitigation constraints, compare the performance of proposed algorithms, draw insights into the effects of transmit power, interference threshold, and Rician factors, validate SatCom performance enhancements achieved by RISs, and discuss the advantages of multi-satellite cooperation.

Movable antennas (MAs), which can be swiftly repositioned within a defined region, offer a promising solution to the limitations of fixed-position antennas (FPAs) in adapting to spatial variations in wireless channels, thereby improving channel conditions and communication between transceivers. However, frequent MA position adjustments based on instantaneous channel state information (CSI) incur high operational complexity, making real-time CSI acquisition impractical, especially in fast-fading channels. To address these challenges, we propose a two-timescale transmission framework for MA-enabled multiuser multiple-input-multiple-output (MU-MIMO) systems. In the large timescale, statistical CSI is exploited to optimize MA positions for long-term ergodic performance, whereas, in the small timescale, beamforming vectors are designed using instantaneous CSI to handle short-term channel fluctuations. Within this new framework, we analyze the ergodic sum rate and develop efficient MA position optimization algorithms for both maximum-ratio-transmission (MRT) and zero-forcing (ZF) beamforming schemes. These algorithms employ alternating optimization (AO), successive convex approximation (SCA), and majorization-minimization (MM) techniques, iteratively optimizing antenna positions and refining surrogate functions that approximate the ergodic sum rate. Numerical results show significant ergodic sum rate gains with the proposed two-timescale MA design over conventional FPA systems, particularly under moderate to strong line-of-sight (LoS) conditions. Notably, MA with ZF beamforming consistently outperforms MA with MRT, highlighting the synergy between beamforming and MAs for superior interference management in environments with moderate Rician factors and high user density, while MA with MRT can offer a simplified alternative to complex beamforming designs in strong LoS conditions.

The rapid expansion of low-Earth orbit (LEO) constellations has greatly intensified the difficulty of mitigating interference among them. Beam hopping (BH) technology is widely considered as one of the key technologies to achieve on-demand services while avoiding interference among these constellation systems. However, the majority of existing BH schemes neglect to address the interference among separate constellations. This paper proposes a novel BH scheme to tackle interference challenges in multi-LEO constellations coexistence scenarios. In contrast to existing BH schemes that predominantly target intra-system interference mitigation, our proposed BH scheme has the capability to dynamically adjust the beam bandwidth to avoid both intra-system interference and inter-system interference. We formulate an optimization problem that maximizes throughput, guarantees delay fairness, minimizes transmission power for LEO constellations, and avoids inter-system interference. To tackle this formidable non-convex and non-linear problem, the optimization problem is interpreted as a sequential decision-making problem, modeled by a Markov Decision Process (MDP). By utilizing the Advantage Actor-Critic (A2C) algorithm, the BH pattern is dynamically designed to avoid interference between beams from multiple LEO constellations in a time-varying network environment. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of long-term throughput and delay fairness.

The growing interest in deploying satellite communication (SatCom) systems results in a proliferation of both Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites, leading to the risk of inter-system interference between GEO and LEO systems, which can result in degraded communication performance or even complete system failure. Under this context, this paper investigates Reconfigurable Intelligent Surface (RIS)-aided LEO SatCom with LEO-GEO inter-system interference mitigation. Specifically, multiple satellites and multiple RISs are operated in a cooperative manner with properly designed joint beamforming. We formulate a minimum signal-to-interference-plus-noise (SINR) maximization problem exploiting statistical channel state information (CSI), subject to LEO-GEO interference mitigation constraint. We propose an alternating optimization (AO)-based algorithm, combined with quadratic transform and manifold optimization techniques, to design the cooperative multi-satellite multi-RIS beamforming iteratively. Numerical results show that the proposed scheme ensures the mitigation of LEO-GEO interference while effectively improving the performance of LEO SatCom.

Full frequency reuse combined with precoding is a promising solution for multibeam satellite systems (MSSs) to meet the evergrowing capacity demand. However, line-of-sight-dominant satellite-ground channels will cause severe channel correlation among the geographically clustered hotspot users (HUs), which restricts multiuser capacity over HUs. In this paper, we propose the reconfigurable intelligent surface (RIS)-aided hotspot capacity enhancement scheme for MSSs. We formulate a hotspot sum rate maximization problem with SINR constraints added on a different user set and present an alternating optimization (AO)-based algorithm for its solution. To reduce computational complexity, we propose a two-stage algorithm that sequentially optimizes RIS phase shift with manifold optimization and satellite precoding, no longer resorting to AO. The RIS phase shift design utilizes semi-orthogonal subspace maximization and pairwise channel decorrelation. This design effectively formulates the interplay between RIS phase shifts and transmit beamforming related to the SINR constraint. To circumvent high channel estimation overhead, we extend the algorithms to low-cost designs exploiting statistical channel state information. Simulation results demonstrate that our proposed RIS-aided MSS designs substantially enhance HUs’ sum rate, attributed to the RIS-enabled channel refinement mechanism. Moreover, the two-stage algorithm achieves a comparable performance to the AO-based algorithm.

Precoding techniques combined with aggressive full frequency reuse (FFR) are a promising solution to meet the evergrowing capacity demand for multibeam satellite systems (MSSs). However, LOS-dominant satellite-ground propagation environments will cause strong channel correlation among the geographically clustered hotspot users (HUs), and the consequent degradation of spatial multiplexing gain severely restricts multiuser multiple-input multiple-output (MU-MIMO) capacity. In this paper, we propose a reconfigurable intelligent surface (RIS)-aided scheme for MSSs to enhance the hotspot capacity of HUs via RIS-improved spatial-multiplexed transmission. Specifically, we propose to employ a RIS in the MSS's hotspot area formed by HUs for channel decorrelation and then jointly optimize the RIS passive beamforming and satellite precoding to reap the benefits of the spatial multiplexing. In this context, we formulate a novel HUs' sum rate maximization problem, subject to the transmit power constraint, the quality-of-service constraints for non-hotspot users, and the unit-modulus constraint for the RIS. The formulated problem is non-convex, and we propose an efficient iterative algorithm based on quadratic transform, semi-definite relaxation, and alternating optimization methods to solve it. Simulation results show that in our proposed RIS-aided scheme with optimized precoding and passive beamforming, significant sum rate enhancement is achieved for HUs in the MSS, owing to channel reconfigurable capability brought by the RIS.

Low Earth Orbit (LEO) satellite communication (SatCom) is considered a promising solution to supplement terrestrial networks. However, line-of-sight (LoS) links between the satellite and terrestrial terminals are possibly blocked by objects on Earth (e.g., mountains, tall buildings, etc.), leading to communication interruptions. This paper introduces Reconfigurable Intelligent Surface (RIS) into the LEO SatCom system to extend satellite coverage in blockage scenarios. Specifically, in contrast to the signal transmission model under the far-field assumption in usual studies, we present a model combining the near-field and far-field of the RIS, which can characterize the operational differences of each reflecting element more accurately. Next, a user-received power maximization problem is formulated and we solve it by jointly optimizing the RIS phase shifts and orientation. Simulation results demonstrate the effectiveness of the proposed optimization algorithm. Remarkably, it is shown that the scheme based on the proposed algorithm can assist the satellite in extending its coverage and providing higher power than that before blockage occurs compared to the baseline schemes.