Searcharxiv⌕ Search

arXiv subjects

Qingqing Wu

Publications and source records attributed to Qingqing Wu.

At least 55 records · Page 3Linked to original sources
arXiv2026

Movable IRS-Aided ISAC Systems: Joint Beamforming and Position Optimization

Driven by intelligent reflecting surface (IRS) and movable antenna (MA) technologies, movable IRS (MIRS) has been proposed to improve the adaptability and performance of conventional IRS, enabling flexible adjustment of the IRS reflecting element positions. This paper investigates MIRS-aided integrated sensing and communication (ISAC) systems. The objective is to minimize the power required for satisfying the quality-of-service (QoS) of sensing and communication by jointly optimizing the MIRS element positions, IRS reflection coefficients, transmit beamforming, and receive filters. To balance the performance-cost trade-off, we proposed two MIRS schemes: element-wise control and array-wise control, where the positions of individual reflecting elements and arrays consisting of multiple elements are controllable, respectively. To address the joint beamforming and position optimization, a product Riemannian manifold optimization (PRMO) method is proposed, where the variables are updated over a constructed product Riemannian manifold space (PRMS) in parallel via penalty-based transformation and Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm. Simulation results demonstrate that the proposed MIRS outperforms conventional IRS in power minimization with both element-wise control and array-wise control. Specifically, with different system parameters, the minimum power is achieved by the MIRS with the element-wise control scheme, while suboptimal solution and higher computational efficiency are achieved by the MIRS with array-wise control scheme.

eess.SP↗
arXiv2026

Fundamental Tradeoff in Movable Antenna Systems: How Long to Move Before Transmission?

The movable antenna (MA) technology enables flexible reconfiguration of wireless channels through adaptive antenna deployment, offering significant potential for enhancing communication performance. However, antenna movement requires a certain duration within which communication may be compromised due to factors such as channel fluctuation and Doppler effect. This leads to a fundamental tradeoff: A longer movement duration allows antennas to reach more favorable positions for better channel conditions, but it inevitably reduces the time available for data transmission. To characterize the aforementioned tradeoff, we focus on the MAs-enabled multiuser downlink scenario, and jointly optimize the movement duration and antenna deployment at the base station to maximize the effective throughput. The formulated problem is highly non-convex. The general solutions require an one-dimensional search over movement durations, each with optimized antenna deployment. To reduce complexity, we propose a fitting method that samples only a few rate-duration pairs, yielding a closed-form expression that captures the rate trend and enables a favorable solution immediately. We further derive a closed-form condition on the maximum antenna movement speed: When the speed is below a certain threshold, the optimal strategy is to keep antennas stationary throughout the transmission period. The fundamental tradeoff and the effectiveness of the proposed solutions are examined in a special case with two MAs and two users. Finally, numerical simulations validate the efficacy of the proposed schemes.

cs.IT↗
arXiv2026

Two-Timescale Design for Rotatable-Antenna Systems With Imperfect CSI: Rate Analysis and Orientation Optimization

This paper studies uplink multiuser MIMO with a rotatable antenna (RA) array under imperfect channel state information (CSI), where each base-station antenna can adjust its boresight direction within an angular region. To balance performance and control overhead, we propose a two-timescale design: RA orientations are optimized from statistical CSI on a large timescale, while linear receive combiners are updated per coherence block from linear minimum-mean-squared-error (LMMSE) channel estimates. Under this framework, we derive a closed-form use-and-then-forget (UatF)-based rate expression for maximum-ratio combining (MRC) and a closed-form statistical rate surrogate for weighted zero-forcing (wZF) under imperfect CSI, revealing how RA rotation influences useful signal strength, estimation-error-induced self-interference, and multiuser interference. The analysis shows that the orientation minimizing channel-estimation error differs from the rate-maximizing one, and that MRC and wZF prefer different rotation configurations due to their distinct mechanisms of signal aggregation and error-aware user separation. For the resulting non-convex rotation design problems, we develop a projected-gradient algorithm over a product of spherical caps with explicit derivatives of the required channel statistics and rate metrics. Numerical results verify the accuracy of the large-timescale surrogates and show substantial performance gains from RA optimization.

eess.SP↗
arXiv2026

Cell-Free MIMO with Rotatable Antennas: When Macro-Diversity Meets Antenna Directivity

Cell-free networks leverage distributed access points (APs) to achieve macro-diversity, yet their performance is often constrained by large disparities in channel quality arising from user geometry and blockages. To address this, rotatable antennas (RAs) add a lightweight hardware degree of freedom by steering the antenna boresight toward dominant propagation directions to strengthen unfavorable links, thereby enabling the network to better exploit macro-diversity for higher and more uniform performance. This paper investigates an RA-enabled cell-free downlink network and formulates a max-min rate problem that jointly optimizes transmit beamforming and antenna orientations. To tackle this challenging problem, we develop an alternating-optimization-based algorithm that iteratively updates the beamformers via a second-order cone program (SOCP) and optimizes the antenna orientations using successive convex approximation. To reduce complexity, we further propose an efficient two-stage scheme that first designs orientations by maximizing a proportional-fair log-utility using manifold-aware Frank-Wolfe updates, and then computes the beamformers using an SOCP-based design. Simulation results demonstrate that the proposed orientation-aware designs achieve a substantially higher worst-user rate than conventional beamforming-only benchmarks. Furthermore, larger antenna directivity enhances fairness with proper orientation but can degrade the worst-user performance otherwise.

eess.SP↗
arXiv2026

Sensing-Assisted Secure Communication in MA-Aided ISAC: CRB Analysis and Robust Design

A core challenge in physical-layer security is the difficulty of obtaining the channel state information (CSI) of potential eavesdroppers. The inherent sensing functionality of integrated sensing and communication (ISAC) systems offers a promising solution by enabling the estimation of key parameters, such as the eavesdropper's angles of departure (AoDs). Capitalizing on this capability, we propose a sensing-assisted secure communication scheme for a movable antenna (MA)-aided ISAC system. The scheme comprises two stages: eavesdropper AoD sensing and secure communication. In the first stage, the base station (BS) optimizes the positions of its transmit and receive MAs to enhance sensing accuracy. We derive the closed-form Cramer-Rao bound (CRB) for the estimated AoDs to fundamentally characterize how MA positions influence the estimation uncertainty. In the second stage, the BS ensures secure communication by designing a robust beamforming vector that accounts for the AoD uncertainty region and by further optimizing the transmit MAs' positions to maximize the secrecy rate. To manage the end-to-end design, we formulate a joint optimization problem. This intractable non-convex problem is decomposed into two subproblems. For the first subproblem, we develop an alternating optimization (AO) algorithm to solve the CRB minimization problem. For the second subproblem, we solve the worst-case secrecy rate maximization problem using a method based on backward induction, convex hull construction, and AO. Finally, simulation results are provided to demonstrate the significant advantages of the proposed scheme compared to various benchmarks.

cs.IT↗
arXiv2026

Trajectory Design for Fairness Enhancement in Movable Antennas-Aided Communications

Through adaptive antenna repositioning, the movable antenna (MA) technology enables on-demand reconfiguration of wireless channels, thereby creating an additional spatial degree of freedom in improving communication performance. This paper investigates a multiuser uplink communication system aided by MAs, where a base station (BS) equipped with multiple MAs serves multiple single-antenna users. Specifically, given that an optimized array geometry cannot guarantee rate fairness, we focus on designing antenna trajectory at the BS to maximize the minimum achievable rate among all users over a finite time period. The resulting optimization problem is fundamentally challenging to solve due to the continuous-time nature. To address it, we first examine an ideal case with infinitely fast MA movement and demonstrate that the relaxed problem can be optimally solved via the Lagrangian dual method. The obtained trajectory solution reveals that the BS should employ a finite set of MA deployment patterns, each allocated an optimal time duration. Building on this, we then study the general case with limited MA movement speed and propose a heuristic trajectory design inspired by the optimal patterns identified in the ideal scenario. Several insights are also gained by examining the simplified special case. Finally, numerical results are provided to validate the effectiveness of the proposed designs compared to competitive benchmarks.

cs.IT↗
arXiv2026

Continuous Wavefront Design via Virtual Point Sources: A Holographic Paradigm for Near-Field XL-MIMO

Beamforming design for extremely large-scale multiple-input multiple-output (XL-MIMO) systems is challenging due to prohibitive computational complexity and complex near-field propagation effects. To address this, this paper introduces a holographic beamforming paradigm that reformulates the design from optimizing variables at spatially discrete antenna locations to shaping a continuous electromagnetic wave function over the array aperture, effectively mitigating the growth of algorithmic complexity as the array scale increases. We apply this paradigm to the challenging dual near-field (DNF) scenario, where strong transceiver coupling severely degrades conventional iterative algorithms. In this case, we propose a novel Virtual Point Source (VPS) method, which approximates the ideal wave function with a single and analytically tractable spherical-wave. A rigorous geometric-optical analysis is provided to show that the optimal VPS location can be determined in a fully non-iterative manner, thus decoupling the coupled DNF problem. The proposed method is demonstrated in an intelligent reflecting surfaces (IRS)-assisted system, where simulation results show that our non-iterative approach achieves performance comparable to converged alternating-optimization (AO) algorithms, while incurring significantly lower complexity and avoiding convergence uncertainty. This work offers a new theoretical framework for holographic beamforming design in XL-MIMO systems.

cs.IT↗
arXiv2026

Rotatable IRS Aided Wireless Communication

Rotatable intelligent reflecting surface (IRS) introduces a new spatial degree of freedom (DoF) by dynamically adjusting orientations without the need of changing its elements' positions in real time. To unleash the full potential of rotatable IRSs for wireless communications, this paper investigates the joint optimization of IRS rotation angles to maximize the minimum expected signal-to-noise ratio (SNR) over all locations within a given target area. We first propose an angle-dependent channel model that accurately characterizes the reception and reflection of each IRS element. Different from the conventional cosine-law assumption, the proposed model captures the practical electromagnetic characteristics of the IRS, including the effective reception area and reflection efficiency. For the single target location case, a particle swarm optimization (PSO)-based algorithm is developed to solve the SNR maximization problem, and a closed-form expression for a near-optimal solution is derived to provide useful insights. For the general area coverage enhancement case, the optimal rotation is obtained through a two-loop PSO-based iterative algorithm with null-point detection. In this algorithm, the outer loop updates the global rotation angles to maximize the minimum SNR over the target area, whereas the inner loop evaluates the SNR distribution within the area to identify the location corresponding to the minimum SNR through null-point detection. Numerical results demonstrate significant SNR improvement achieved by the proposed rotatable IRS design over various benchmark schemes under different system setups.

eess.SP↗
arXiv2026

Low-Altitude ISAC with Rotatable Active and Passive Arrays

This paper investigates a low-altitude integrated sensing and communication (ISAC) system that leverages cooperative rotatable active and passive arrays. We consider a downlink scenario where a base station (BS) with an active rotatable array serves multiple communication users and senses low-altitude targets, assisted by a rotatable reconfigurable intelligent surface (RIS). A rotation-aware geometry-based multipath model is developed to capture the impact of three-dimensional (3D) array orientations on both steering vectors and direction-dependent element gains. On this basis, we formulate a new optimization problem that maximizes the downlink sum rate subject to a transmit power budget, RIS unit-modulus constraints, mechanical rotation limits, and a sensing beampattern mean-squared-error (MSE) constraint. To address the resulting highly non-convex problem, we propose a penalty-based alternating-optimization (AO) framework that alternately updates the BS precoder, RIS phase shifts, and BS/RIS array rotation angles. The three blocks are efficiently handled via a convex optimization method based on quadratic-transform (QT) and majorization-minorization (MM), Riemannian conjugate gradient (RCG) on the unit-modulus manifold, and projected gradient descent (PGD) with Barzilai-Borwein step sizes, respectively. Numerical results in low-altitude geometries demonstrate that the proposed jointly rotatable BS-RIS architecture achieves significant sum-rate gains over fixed or partially rotatable baselines while guaranteeing sensing requirements, especially with directional antennas and in interference-limited regimes.

eess.SP↗
arXiv2026

Low-Altitude Reflection via UAV-Mounted Rotatable IRS

Low-altitude network is a key enabler for extending coverage and recovering connectivity in 6G systems, especially when terrestrial infrastructure is unavailable. This paper studies a uncrewed aerial vehicle (UAV)-mounted rotatable intelligent reflecting surface (IRS) as a low-altitude reflector between a blocked base station (BS) and a ground terminal (GT). Unlike the conventional isotropic-element assumption, each IRS element is modeled with a hemispherical directive radiation pattern, whose boresight can be adjusted via element rotations. We formulate a new optimization problem that jointly designs IRS phase shifts, per-element rotation vectors, and UAV placement to maximize the received signal-to-noise ratio (SNR). Leveraging the problem structure, we derive closed-form solutions for phase alignment and element rotations, showing that the optimal boresight points are along the internal angular bisector between the BS-IRS and GT-IRS directions. With these closed forms, the design reduces to a placement optimization problem over a box-constrained airspace; we solve it using an efficient projected gradient algorithm with majorization-minimization update and a global Lipschitz constant. Numerical results demonstrate substantial SNR gains from directive elements and reveal a fundamental trade-off between directional gain and path loss, yielding useful insights into low-altitude deployment of UAV-mounted IRSs.

eess.SP↗
arXiv2026

DRL-Enabled Trajectory Planing for UAV-Assisted VLC: Optimal Altitude and Reward Design

Recently, the integration of unmanned aerial vehicle (UAV) and visible light communication (VLC) technologies has emerged as a promising solution to offer flexible communication and efficient lighting. This letter investigates the three-dimensional trajectory planning in a UAV-assisted VLC system, where a UAV is dispatched to collect data from ground users (GUs). The core objective is to develop a trajectory planning framework that minimizes UAV flight distance, which is equivalent to maximizing the data collection efficiency. This issue is formulated as a challenging mixed-integer non-convex optimization problem. To tackle it, we first derive a closed-form optimal flight altitude under specific VLC channel gain threshold. Subsequently, we optimize the UAV horizontal trajectory by integrating a novel pheromone-driven reward mechanism with the twin delayed deep deterministic policy gradient algorithm, which enables adaptive UAV motion strategy in complex environments. Simulation results validate that the derived optimal altitude effectively reduces the flight distance by up to 35% compared to baseline methods. Additionally, the proposed reward mechanism significantly shortens the convergence steps by approximately 50%, demonstrating notable efficiency gains in the context of UAV-assisted VLC data collection.

cs.LG↗
arXiv2026

Low-altitude Multi-UAV-assisted Data Collection and Semantic Forwarding for Post-Disaster Relief

The low-altitude economy (LAE) is an emerging economic paradigm which fosters integrated development across multiple fields. As a pivotal component of the LAE, low-altitude uncrewed aerial vehicles (UAVs) can restore communication by serving as aerial relays between the post-disaster areas and remote base stations (BSs). However, conventional approaches face challenges from vulnerable long-distance links between the UAVs and remote BSs, and data bottlenecks arising from massive data volumes and limited onboard UAV resources. In this work, we investigate a low-altitude multi-UAV-assisted data collection and semantic forwarding network, in which multiple UAVs collect data from ground users, form clusters, perform intra-cluster data aggregation with semantic extraction, and then cooperate as virtual antenna array (VAAs) to transmit the extracted semantic information to a remote BS via collaborative beamforming (CB). We formulate a data collection and semantic forwarding multi-objective optimization problem (DCSFMOP) that jointly maximizes both the user and semantic transmission rates while minimizing UAV energy consumption. The formulated DCSFMOP is a mixed-integer nonlinear programming (MINLP) problem that is inherently NP-hard and characterized by dynamically varying decision variable dimensionality. To address these challenges, we propose a large language model-enabled alternating optimization approach (LLM-AOA), which effectively handles the complex search space and variable dimensionality by optimizing different subsets of decision variables through tailored optimization strategies. Simulation results demonstrate that LLM-AOA outperforms AOA by approximately 26.8\% and 22.9\% in transmission rate and semantic rate, respectively.

cs.NI↗
arXiv2026

Joint Antenna Rotation and IRS Beamforming for Multi-User Uplink Communications

Rotatable antenna (RA) enhances wireless coverage through directional gain steering, yet suffers from performance degradation under physical blockages. Intelligent reflecting surface (IRS) establishes reflective paths to bypass obstacles, but suffers from angular mismatch when deployed in the side-lobe region of base station (BS) antennas. To address this issue, we propose a new RA-enabled IRS-assisted multi-user uplink system, in which the BS antennas are capable of flexibly adjusting their 3D orientations to align their boresights with the IRS. We formulate a sum rate maximization problem by jointly optimizing the antenna 3D rotations, receive beamforming and IRS phase shifts. To tackle this non-convex problem, we propose an efficient alternating optimization (AO) algorithm. Specifically, we iteratively update the antenna rotations via projected gradient ascent (PGA), compute the receive beamforming via a closed-form solution, and optimize the IRS phase shifts via fractional programming (FP). Numerical results demonstrate that the proposed system yields significant performance gains over conventional fixed-antenna systems, especially under large angular misalignments.

cs.IT↗
arXiv2026

Two-Scale Spatial Deployment for Cost-Effective Wireless Networks via Cooperative IRSs and Movable Antennas

This paper proposes a two-scale spatial deployment strategy to ensure reliable coverage for multiple target areas, integrating macroscopic intelligent reflecting surfaces (IRSs) and fine-grained movable antennas (MAs). Specifically, IRSs are selectively deployed from candidate sites to shape the propagation geometry, while MAs are locally repositioned among discretized locations to exploit small-scale channel variations. The objective is to minimize the total deployment cost of MAs and IRSs by jointly optimizing the IRS site selection, MA positions, transmit precoding, and IRS phase shifts, subject to the signal-to-noise ratio (SNR) requirements for all target areas. This leads to a challenging mixed-integer non-convex optimization problem that is intractable to solve directly. To address this, we first formulate an auxiliary problem to verify the feasibility. A penalty-based double-loop algorithm integrating alternating optimization and successive convex approximation (SCA) is developed to solve this feasibility issue, which is subsequently adapted to obtain a suboptimal solution for the original cost minimization problem. Finally, based on the obtained solution, we formulate an element refinement problem to further reduce the deployment cost, which is solved by a penalty-based SCA algorithm. Simulation results demonstrate that the proposed designs consistently outperform benchmarks relying on independent area planning or full IRS deployment in terms of cost-efficiency. Moreover, for cost minimization, MA architectures are preferable in large placement apertures, whereas fully populated FPA architectures excel in compact ones; for worst-case SNR maximization, MA architectures exhibit a lower cost threshold for feasibility, while FPA architectures can attain peak SNR at a lower total cost.

eess.SP↗
arXiv2026

Coverage and Spectral Efficiency of NOMA-Enabled LEO Satellite Networks with Ordering Schemes

This paper investigates an analytical model for low-earth orbit (LEO) multi-satellite downlink non-orthogonal multiple access (NOMA) networks. The satellites transmit data to multiple NOMA user terminals (UTs), each employing successive interference cancellation (SIC) for decoding. Two ordering schemes are adopted for NOMA-enabled LEO satellite networks, i.e., mean signal power (MSP)-based ordering and instantaneous signal-to-inter-satellite-interference-plus-noise ratio (ISINR)-based ordering. For each ordering scheme, we derive the analytical expression for the coverage probability of each typical UT. Moreover, we discuss how coverage is influenced by SIC, main-lobe gain, and tradeoffs between the number of satellites and their altitudes. Additionally, two user fairness-based power allocation (PA) schemes are considered, and PA coefficients with the optimal number of UTs that maximize their sum spectral efficiency (SE) are studied. Simulation results show that there exists a maximum effective signal-to-inter-satellite-interference-plus-noise ratio (SINR) threshold for each PA scheme that ensures the operation of NOMA in LEO satellite networks, and NOMA provides performance gains only when the target SINR is below a certain threshold. Compared with orthogonal multiple access (OMA), NOMA increases UTs' sum SE by as much as 35%. Furthermore, for most SINR thresholds, the sum SE increases with the number of UTs to the highest value, whilst the maximum sum SE is obtained when there are two UTs.

eess.SP↗
arXiv2026

Two-Timescale Design for Movable Antenna-Enabled Multiuser MIMO Systems

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.

eess.SP↗
arXiv2026

Integrating Movable Antennas and Intelligent Reflecting Surfaces (MA-IRS): Fundamentals, Practical Solutions, and ISAC

Movable antennas (MAs) and intelligent reflecting surfaces (IRSs) enable active antenna repositioning and passive phase-shift tuning for channel reconfiguration, respectively. Integrating MAs and IRSs boosts spatial degrees of freedom, significantly enhancing wireless network capacity, coverage, and reliability. In this article, we first present the fundamentals of MA-IRS integration, involving clarifying the key design issues, revealing performance gain, and identifying the conditions where MA-IRS synergy persists. Then, we examine practical challenges and propose pragmatic design solutions, including optimization schemes, hardware architectures, deployment strategies, and robust designs for hardware impairments and mobility management. In addition, we highlight how MA-IRS architectures uniquely support advanced integrated sensing and communication, enhancing sensing performance and dual-functional flexibility. Overall, MA-IRS integration emerges as a compelling approach toward next-generation reconfigurable wireless systems.

eess.SP↗
arXiv2025

Dual-IRS Aided Near-/Hybrid-Field SWIPT: Passive Beamforming and Independent Antenna Power Splitting Design

This paper proposes a novel dual-intelligent reflecting surface (IRS) aided interference-limited simultaneous wireless information and power transfer (SWIPT) system with independent power splitting (PS), where each receiving antenna applies different PS factors to offer an advantageous trade-off between the useful information and harvested energy. We separately establish the near- and hybrid-field channel models for IRS-reflected links to evaluate the performance gain more precisely and practically. Specifically, we formulate an optimization problem of maximizing the harvested power by jointly optimizing dual-IRS phase shifts, independent PS ratio, and receive beamforming vector in both near- and hybrid-field cases. In the near-field case, the alternating optimization algorithm is proposed to solve the non-convex problem by applying the Lagrange duality method and the difference-of-convex (DC) programming. In the hybrid-field case, we first present an interesting result that the AP-IRS-user channel gains are invariant to the phase shifts of dual-IRS, which allows the optimization problem to be transformed into a convex one. Then, we derive the asymptotic performance of the combined channel gains in closed-form and analyze the characteristics of the dual-IRS. Numerical results validate our analysis and indicate the performance gains of the proposed scheme that dual-IRS-aided SWIPT with independent PS over other benchmark schemes.

eess.SP↗