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Qingqing Wu

Publications and source records attributed to Qingqing Wu.

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

Sensing for Reliable UAV Communication: Robust Trajectory and Resource Optimization in Low-Altitude Networks

In low-altitude wireless networks, sensing-aided communication has emerged as a promising integrated sensing and communication (ISAC) paradigm for unmanned aerial vehicle (UAV) tracking and communication. This paper investigates reliable sensing-aided communication for multiple cellular-connected UAVs under mobility uncertainties. Specifically, we maximize the minimum outage capacity among UAVs by jointly optimizing their real-time predicted positions, as well as the base station (BS) transmit power and bandwidth allocations. To address the non-convex and intractable maximum tolerable outage probability (OP) constraints, two robust optimization schemes are proposed based on a continuous confidence ellipse (CE) and discretized inverse-whitened sectors (IWSs), respectively. For the CE-based scheme, an efficient algorithm is proposed to optimize the predicted UAV positions individually via block successive convex approximation, followed by convex resource allocation. For the IWS-based scheme, an IWS-based OP approximation is proposed to facilitate the robust optimization, based on which a low-complexity IWS selection method is proposed to decouple the optimization variables. Then, a similar sequential optimization algorithm is proposed based on the projected gradient descent approach. The two algorithms are further unified into a common trajectory-resource optimization framework, revealing a low-complexity structure for robust UAV trajectory and resource management. Simulation results validate the effectiveness of our proposed OP approximation, demonstrate the significant outage capacity improvement of the proposed robust optimization schemes over benchmark schemes, and illustrate the superiority of the IWS-based scheme over the CE-based scheme.

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arXiv2026

Fundamentals of NOMA in Low-Earth Orbit Coordinated Multi-Satellite Networks

Coordinated multi-satellite (CoMS) transmission and non-orthogonal multiple access (NOMA) are envisioned to jointly enhance coverage, capacity, and spectrum efficiency for satellite networks. Their integration into a unified CoMS-NOMA framework will allow more efficient, reliable, and energy-efficient multi-user access. This paper investigates the downlink performance of CoMS-NOMA networks from a system-level perspective, in which multiple satellites cooperatively serve multiple users via NOMA. Leveraging tools from stochastic geometry, related angles and distances in CoMS-NOMA are first derived as intermediate results. Then, we obtain the combined signal power distributions and analyze coverage and spectrum performance under both inter- and intra-satellite interference, accounting for potential imperfect successive interference cancellation (SIC). The analytical model is validated across a range of system parameters, including the number of satellites, service region angle, error-propagation factor, and power allocation coefficients. Numerical results indicate that increasing the number of cooperative satellites does not always improve coverage and spectrum efficiency. Additionally, while a higher main-lobe gain improves coverage, a near-perfect SIC provides only slightly greater benefits than a reasonably good SIC. With properly selected power allocation coefficients, CoMS-NOMA achieves up to a 270% improvement in coverage and a 56% gain in sum spectral efficiency, compared with conventional orthogonal and single-satellite schemes, indicating potential for green, energy-efficient satellite networking.

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arXiv2026

Rotatable Antenna-Enabled Mobile Edge Computing

In the evolving landscape of mobile edge computing (MEC), enhancing communication reliability and computation efficiency to support increasingly stringent low-latency services remains a fundamental challenge. Rotatable antenna (RA) is a promising technology that introduces new spatial degrees of freedom (DoFs) to tackle this challenge. In this letter, we investigate an RA-enabled MEC system where antenna boresight directions can be independently adjusted to proactively improve wireless channel conditions for latency-critical users. We aim to minimize the maximum computation latency by jointly optimizing the MEC server computing resource allocation, receive beamforming, and the deflection angles of all RAs. To address the resulting non-convex problem, we develop an efficient alternating optimization (AO) framework. Specifically, the optimal edge computing resource allocation is derived based on the Karush-Kuhn-Tucker (KKT) conditions. Given the computing resources, the receive beamforming is optimized using semidefinite relaxation (SDR) combined with a bisection search. Furthermore, the RA deflection angles are optimized via fractional programming (FP) and successive convex approximation (SCA). Simulation results verify that the proposed RA-enabled MEC scheme significantly reduces the maximum computation latency compared with conventional benchmark methods.

cs.IT↗
arXiv2026

Advancing Fluid Antenna-Assisted Non-Terrestrial Networks in 6G and Beyond: Fundamentals, State of the Art, and Future Directions

With the surging demand for ultra-reliable, low-latency, and ubiquitous connectivity in Sixth-Generation (6G) networks, Non-Terrestrial Networks (NTNs) emerge as a key complement to terrestrial networks by offering flexible access and global coverage. Despite the significant potential, NTNs still face critical challenges, including dynamic propagation environments, energy constraints, and dense interference. As a key 6G technology, Fluid Antennas (FAs) can reshape wireless channels by reconfiguring radiating elements within a limited space, such as their positions and rotations, to provide higher channel diversity and multiplexing gains. Compared to fixed-position antennas, FAs can present a promising integration path for NTNs to mitigate dynamic channel fading and optimize resource allocation. This paper provides a comprehensive review of FA-assisted NTNs. We begin with a brief overview of the classical structure and limitations of existing NTNs, the fundamentals and advantages of FAs, and the basic principles of FA-assisted NTNs. We then investigate the joint optimization solutions, detailing the adjustments of FA configurations, NTN platform motion modes, and resource allocations. We also discuss the combination with other emerging technologies and explore FA-assisted NTNs as a novel network architecture for intelligent function integrations. Furthermore, we delve into the physical layer security and covert communication in FA-assisted NTNs. Finally, we highlight the potential future directions to empower broader applications of FA-assisted NTNs.

cs.NI↗
arXiv2026

Engineering Favorable Propagation: Near-Field IRS Deployment for Spatial Multiplexing

In intelligent reflecting surface IRS assisted multiple input multiple output MIMO systems, a strong line of sight LoS link is required to compensate for the severe cascaded path loss. However, such a link renders the effective channel highly rank deficient and fundamentally limits spatial multiplexing. To overcome this limitation, this paper leverages the large aperture of sparse arrays to harness near field spherical wavefronts, and establishes a deterministic deployment criterion that strategically positions the IRS in the near field of a base station BS. This placement exploits the spherical wavefronts of the BS IRS link to engineer decorrelated channels, thereby fundamentally overcoming the rank deficiency issue in far field cascaded channels. Based on a physical channel model for the sparse BS array and the IRS, we characterize the rank properties and inter user correlation of the cascaded BS IRS user channel. We further derive a closed form favorable propagation metric that reveals how the sparse array geometry and the IRS position can be tuned to reduce inter user channel correlation. The resulting geometry driven deployment rule provides a simple guideline for creating a favorable propagation environment with enhanced effective degrees of freedom. The favorable channel statistics induced by our deployment criterion enable a low complexity maximum ratio transmission MRT precoding scheme. This serves as the foundation for an efficient algorithm that jointly optimizes the IRS phase shifts and power allocation based solely on long term statistical channel state information CSI. Simulation results validate the effectiveness of our deployment criterion and demonstrate that our optimization framework achieves significant performance gains over benchmark schemes.

cs.IT↗
arXiv2026

Multi-User MIMO with Rotatable Antennas and IRS: Joint Antenna Boresight and IRS Orientation Design

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted multi-user system, where the base station (BS) employs rotatable antennas (RAs) and the IRS can adjust the panel orientation.To alleviate the severe multiplicative path loss of the cascaded channel, the IRS is deployed near the BS, while the user-BS and user-IRS links remain in the far field. We formulate a sum-rate maximization problem by jointly optimizing the receive beamforming, IRS phase shifts, BS antenna boresights, and IRS panel orientation. To tackle the resulting highly coupled and non-convex problem, we first study a single-user case to reveal the structure of the dual-rotation gain, which is shown to be multiplicatively separable in the far field but coupled in the near field. For the general multi-user case, we develop an alternating optimization algorithm, where the receive beamforming is updated in closed form, the IRS phase shifts are optimized by an FP-assisted Riemannian conjugate gradient method, and the BS antenna boresights and IRS panel orientation are updated via projected gradient methods. Simulation results demonstrate the significant sum-rate gains achieved by the proposed coordinated rotation design over fixed-orientation and single-rotation benchmark schemes, and provide useful insights into near-field dual-rotation design.

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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.

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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.

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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.

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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.

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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.

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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.

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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↗