arXiv subjects

Qingqing Wu

Publications and source records attributed to Qingqing Wu.

At least 91 records · Page 5Linked to original sources

arXiv2025

Joint Discrete Antenna Positioning and Beamforming Optimization in Movable Antenna Enabled Full-Duplex ISAC Networks

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power. Given the significant coupling of optimization variables, the formulated problem presents a non-convex optimization challenge that poses difficulties for direct resolution. To address this challenging issue, the discrete binary particle swarm optimization (BPSO) algorithm framework is employed to solve the formulated problem. Specifically, the discrete positions of MA elements are first obtained by iteratively solving the fitness function. The difference-of-convex (DC) programming and successive convex approximation (SCA) are used to handle non-convex and rank-1 terms in the fitness function. Once the BPSO iteration is complete, the discrete positions of MA elements can be determined, and we can obtain the solutions for beamforming vectors, sensing signal covariance matrix and user transmit power. Numerical results demonstrate the superiority of the proposed system in reducing the total transmit power consumption compared with fixed antenna arrays.

eess.SP↗

arXiv2025

Analysis and Algorithm for Multi IRS Collaborative Localization via Hybrid Time Angle Estimation

This paper proposes a novel multiple intelligent reflecting surfaces (IRSs) collaborative hybrid localization system, which involves deploying multiple IRSs near the target area and achieving target localization through joint time delay and angle estimation. Specifically, echo signals from all reflective elements are received by each sensor and jointly processed to estimate the time delay and angle parameters. Based on the above model, we derive the Fisher Information Matrix (FIM) for cascaded delay, Angle of Arrival (AOA), and Angle of Departure (AOD) estimation in semi passive passive models, along with the corresponding Cramer Rao Bound (CRB). To achieve precise estimation close to the CRB, we design efficient algorithms for angle and location estimation. For angle estimation, reflective signals are categorized into three cases based on their rank, with different signal preprocessing. By constructing an atomic norm set and minimizing the atomic norm, the joint angle estimation problem is transformed into a convex optimization problem, and low-complexity estimation of multiple AOA and AOD pairs is achieved using the Alternating Direction Method of Multipliers (ADMM). For location estimation, we propose a three-stage localization algorithm that combines weighted least squares, total least squares, and quadratic correction to handle errors in the coefficient matrix and observation vector, thus improving accuracy. Numerical simulations validate the superiority of the proposed system, demonstrating that the system's collaboration, hybrid localization, and distributed deployment provide substantial benefits, as well as the accuracy of the proposed estimation algorithms, particularly in low signal to noise ratio (SNR) condition.

eess.SP↗

arXiv2025

Design and Analysis of Phase Conjugation-Based Self-Alignment Beamforming for RIS-Assisted Terahertz SWIPT

Terahertz (THz) simultaneous wireless information and power transfer (SWIPT) is a promising technology for enabling ultra-high-rate and low-latency communications in massive battery-free Internet of Things (IoT) deployments for 6G networks. However, conventional THz systems rely on narrow directional beams that necessitate precise alignment, typically achieved through high-overhead beam scanning procedures, which fundamentally at odds with the energy constraints of battery-free IoT devices. In this paper, we propose a novel self-alignment architecture for THz SWIPT leveraging a reconfigurable intelligent surface (RIS) to eliminate complex beam scanning. By integrating phase conjugate circuits at both the base station and user equipment, the RIS facilitates a resonance-based bidirectional retro-reflection mechanism, enabling the system to autonomously converge to an aligned state without manual intervention. We develop an analytical channel transfer model and a power cycle model to characterize the resonance-assisted beam alignment process and power transfer efficiency. Simulation results demonstrate that the RIS-enabled system achieves effective spatial power concentration with significant sidelobe suppression, leading to a communication capacity of 127.84 Gbit/s and a received power of 13.62 mW over a 2.2-meter link.

eess.SP↗

arXiv2025

Low-Altitude UAV-Carried Movable Antenna for Joint Wireless Power Transfer and Covert Communications

The proliferation of Internet of Things (IoT) networks has created an urgent need for sustainable energy solutions, particularly for the battery-constrained spatially distributed IoT nodes. While low-altitude uncrewed aerial vehicles (UAVs) employed with wireless power transfer (WPT) capabilities offer a promising solution, the line-of-sight channels that facilitate efficient energy delivery also expose sensitive operational data to adversaries. This paper proposes a novel low-altitude UAV-carried movable antenna-enhanced transmission system joint WPT and covert communications, which simultaneously performs energy supplements to IoT nodes and establishes transmission links with a covert user by leveraging wireless energy signals as a natural cover. Then, we formulate a multi-objective optimization problem that jointly maximizes the total harvested energy of IoT nodes and sum achievable rate of the covert user, while minimizing the propulsion energy consumption of the low-altitude UAV. To address the non-convex and temporally coupled optimization problem, we propose a mixture-of-experts-augmented soft actor-critic (MoE-SAC) algorithm that employs a sparse Top-K gated mixture-of-shallow-experts architecture to represent multimodal policy distributions arising from the conflicting optimization objectives. We also incorporate an action projection module that explicitly enforces per-time-slot power budget constraints and antenna position constraints. Simulation results demonstrate that the proposed approach significantly outperforms some baseline approaches and other state-of-the-art deep reinforcement learning algorithms.

cs.NI↗

arXiv2025

Six-Dimensional Movable Antenna Enabled Wideband THz Communications

In this paper, we investigate a six-dimensional movable antenna (6DMA)-enabled wideband terahertz (THz) communication system with sub-connected hybrid beamforming architecture at the base station (BS). In particular, the three-dimensional (3D) position and 3D rotation of each 6DMA surface can be flexibly reconfigured to mitigate the beam squint effects instead of introducing costly true-time-delay devices. We first analyze the normalized array gain in the 6DMA-enabled wideband THz systems based on the beam squint effects. Then, we formulate a sum-rate maximization problem via jointly optimizing 3D positions, 3D rotations, and hybrid analog/digital beamforming. To solve the non-convex problem, an alternating optimization algorithm is developed that decomposes the original problem into three subproblems, which are solved alternately. Specifically, given the positions and rotations of 6DMA surfaces, we first reformulate the objective function and design a semidefinite relaxation-based alternating minimization scheme to obtain the hybrid analog/digital beamforming. Then, the positions and rotations of the 6DMA surfaces are further optimized through a feasible gradient descent procedure. The final solutions are obtained by repeating the above procedure until convergence. Numerical results demonstrate the superior performance of the proposed scheme compared with conventional fixed-position antenna architectures.

physics.comp-ph↗

arXiv2025

Joint Beamforming Design and Resource Allocation for IRS-Assisted Full-Duplex Terahertz Systems

Intelligent reflecting surface (IRS)-assisted full-duplex (FD) terahertz (THz) communication systems have emerged as a promising paradigm to satisfy the escalating demand for ultra-high data rates and spectral efficiency in future wireless networks. However, the practical deployment of such systems presents unique technical challenges, stemming from severe propagation loss, frequency-dependent molecular absorption in the THz band, and the presence of strong residual self-interference (SI) inherent to FD communications. To tackle these issues, this paper proposes a joint resource allocation framework that aims to maximize the weighted minimum rate among all users, thereby ensuring fairness in quality of service. Specifically, the proposed design jointly optimizes IRS reflecting phase shifts, uplink/downlink transmit power control, sub-band bandwidth allocation, and sub-band assignment, explicitly capturing the unique propagation characteristics of THz channels and the impact of residual SI. To strike an balance between system performance and computational complexity, two computationally efficient algorithms are developed under distinct spectrum partitioning schemes: one assumes equal sub-band bandwidth allocation to facilliate tractable optimization, while the other introduces adaptive bandwidth allocation to further enhance spectral utilization and system flexibility. Simulation results validate the effectiveness of the proposed designs and demonstrate that the adopted scheme achieves significant spectral efficiency improvements over benchmark schemes.

cs.IT↗

arXiv2025

Multifunctional Wideband Digital Metasurface for Secure Electromagnetic Manipulation in S-Band

Digital metasurfaces have attracted significant attention in recent years due to their ability to manipulate electromagnetic (EM) waves for secure sensing and communication. However, most reported metasurfaces operate at relatively high frequencies, primarily due to the constraints imposed by the physical scale of the dielectric substrate, thus limiting their full-wave system applications. In this work, a wideband digital reflective metasurface is presented for capable of dynamically controlling EM waves, with multifunctional applications in the lower-frequency S-band. The metasurface is composed of electronically reconfigurable meta-atoms with wideband characteristics, and designed by using trapezoidal and M-shaped patches connected by a pin diode. Simulation results show that the proposed digital metasurface could achieve wideband 1-bit phase quantization with a stable phase difference within 180 degree +/- 25 degree and small reflection loss below 0.6 dB from 2.72 to 3.25 GHz. To validate the proposed design, a 20x20-unit metasurface array was designed, simulated and fabricated. By dynamically adjusting the coding sequence, the metasurface could enable multi-mode orbital angular momentum (OAM) beam generation, dynamic beam scanning, and precise direction finding. These capabilities support secure sensing and secure communications through high-resolution target detection and anti-jamming beam steering, as well as physical-layer security. The proposed wideband metasurface may serve as an effective candidate for enhancing spectral efficiency and security performance in radar and wireless systems.

eess.SP↗

arXiv2025

STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks

While low-altitude wireless networks (LAWNs) based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications, they face severe signal attenuation in dense environments due to obstructions. To address this critical issue, we consider introducing collaborative beamforming (CB) of UAVs and omnidirectional reconfigurable beamforming (ORB) of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) to enhance the signal quality and directionality. On this basis, we formulate a joint rate and energy optimization problem (JREOP) to maximize the transmission rate of the overall system, while minimizing the energy consumption of the UAV swarm. Due to the non-convex and NP-hard nature of JREOP, we propose a heterogeneous multi-agent collaborative dynamic (HMCD) optimization framework, which has two core components. The first component is a simulated annealing (SA)-based STAR-RIS control method, which dynamically optimizes reflection and transmission coefficients to enhance signal propagation. The second component is an improved multi-agent deep reinforcement learning (MADRL) control method, which incorporates a self-attention evaluation mechanism to capture interactions between UAVs and an adaptive velocity transition mechanism to enhance training stability. Simulation results demonstrate that HMCD outperforms various baselines in terms of convergence speed, average transmission rate, and energy consumption. Further analysis reveals that the average transmission rate of the overall system scales positively with both UAV count and STAR-RIS element numbers.

cs.NI↗

arXiv2025

Energy-Efficient UAV-Enabled MEC Systems: NOMA, FDMA, or TDMA Offloading?

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems can use different multiple access schemes to coordinate multi-user task offloading. However, it is still unknown which scheme is the most energy-efficient, especially when the offloading blocklength is finite. To answer this question, this paper minimizes and compares the MEC-related energy consumption of non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA)-based offloading schemes within UAV-enabled MEC systems, considering both infinite and finite blocklength scenarios. Through theoretically analysis of the minimum energy consumption required by these three schemes, two novel findings are presented. First, TDMA consistently achieves lower energy consumption than FDMA in both infinite and finite blocklength cases, due to the degrees of freedom afforded by sequential task offloading. Second, NOMA does not necessarily achieve lower energy consumption than FDMA when the offloading blocklength is finite, especially when the channel conditions and the offloaded task data sizes of two user equipments (UEs) are relatively symmetric. Furthermore, an alternating optimization algorithm that jointly optimizes the portions of task offloaded, the offloading times of all UEs, and the UAV location is proposed to solve the formulated energy consumption minimization problems. Simulation results verify the correctness of our analytical findings and demonstrate that the proposed algorithm effectively reduces MEC-related energy consumption compared to benchmark schemes that do not optimize task offloading portions and/or offloading times.

cs.IT↗

arXiv2025

Outage-Aware Sum Rate Maximization in Movable Antennas-Enabled Systems

In this paper, we investigate the movable antennas (MAs)-enabled multiple-input-single-output (MISO) systems, where the base station (BS) equipped with multiple MAs serves multiple single-antenna user. The delay-sensitive scenario is considered, where users refrain from periodically sending training signals to the BS for channel estimations to avoid additional latency. As a result, the BS relies solely on the statistical channel state information (CSI) to transmit data with a fixed rate. Under this setup, we aim to maximize the outage-aware sum rate of all users, by jointly optimizing antenna positions and the transmit beamforming at the BS, while satisfying the given target outage probability requirement at each user. The problem is highly non-convex, primarily because the exact cumulative distribution function (CDF) of the received signal-to-interference-plus-noise ratio (SINR) of each user is difficult to derive. To simplify analysis and without comprising performance, we adopt the statistical CSI based zero-forcing beamforming design. We then introduce one important lemma to derive the tight mean and variance of the SINR. Leveraging these results, we further exploit the Laguerre series approximation to successfully derive the closedform and tight CDF of the SINR. Subsequently, the outageaware sum rate expression is presented but still includes complex structure with respect to antenna positions. Facing this challenge, the projected gradient ascent (PGA) method is developed to iteratively update antenna positions until convergence. Numerical results demonstrate the effectiveness of our proposed schemes compared to conventional fixed-position antenna (FPA) and other competitive benchmarks.

cs.IT↗

arXiv2025

Rotatable Antenna Meets UAV: Towards Dual-Level Channel Reconfiguration Paradigm for ISAC

Integrated sensing and communication (ISAC) is viewed as a key enabler for future wireless networks by sharing the hardware and wireless resources between the functionalities of sensing and communication (S&C). Due to the shared wireless resources for both S&C, it is challenging to achieve a critical trade-off between these two integrated functionalities. To address this issue, this paper proposes a novel dual-level channel reconfiguration framework for ISAC by deploying rotatable antennas at an unmanned aerial vehicle (UAV), where both the large-scale path loss and the correlation of S&C channels can be proactively controlled, thereby allowing a flexible trade-off between S&C performance. To characterize the S&C tradeoff, we aim to maximize the communication rate by jointly optimizing the RA rotation, the transmit beamforming, and the UAV trajectory, subject to the given requirement of sensing performance. For the typical scenario of static UAV deployment, we introduce the concept of subspace correlation coefficient to derive closed-form solutions for the optimal RA rotation, transmit beamforming, and UAV hovering location. For the scenario of a fully mobile UAV, we prove that the optimal trajectory of a UAV follows a hover-fly-hover (HFH) structure, thereby obtaining its global optimal solution. Simulation results show that the proposed design significantly improves the achievable S&C trade-off region compared to benchmark schemes.

cs.IT↗

arXiv2025

Subverting Flexible Multiuser Communications via Movable Antenna-Enabled Jammer

Movable antenna (MA) is an emerging technology which can reconfigure wireless channels via adaptive antenna position adjustments at transceivers, thereby bringing additional spatial degrees of freedom for improving system performance. In this paper, from a security perspective, we exploit the MAenabled legitimate jammer (MAJ) to subvert suspicious multiuser downlink communications consisting of one suspicious transmitter (ST) and multiple suspicious receivers (SRs). Specifically, our objective is to minimize the benefit (the sum rate of all SRs or the minimum rate among all SRs) of such suspicious communications, by jointly optimizing antenna positions and the jamming beamforming at the MAJ. However, the key challenge lies in that given the MAJ's actions, the ST can reactively adjust its power allocations to instead maximize its benefit for mitigating the unfavorable interference. Such flexible behavior of the ST confuses the optimization design of the MAJ to a certain extent. Facing this difficulty, corresponding to the above two different benefits: i) we respectively determine the optimal behavior of the ST given the MAJ's actions; ii) armed with these, we arrive at two simplified problems and then develop effective alternating optimization based algorithms to iteratively solve them. In addition to these, we also focus on the special case of two SRs, and reveal insightful conclusions about the deployment rule of antenna positions at the MAJ. Furthermore, we analyze the ideal antenna deployment scheme at the MAJ for achieving the globally performance lower bound. Numerical results demonstrate the effectiveness of our proposed schemes compared to conventional fixed-position antenna (FPA) and other competitive benchmarks.

cs.IT↗

arXiv2025

Movable Antenna Enhanced Networked Integrated Sensing and Communication System

Integrated sensing and communication (ISAC) is a key technology for future 6G networks. Most existing studies focus on monostatic and/or bistatic setups with limited coverage and capabilities. Networked ISAC systems with distributed base stations (BSs) can overcome these limitations. Moreover, movable antenna (MA) architectures offer improved ISAC performance over fixed-position antennas (FPAs) by enabling adaptable antenna movement. In this paper, we utilize the MA to promote communication capability with guaranteed sensing performance via jointly designing beamforming, power allocation, receiving filters and position configuration of transmit/receive MA towards maximizing the sum rate for both downlink (DL) and uplink (UL) users. The optimization problem is highly difficult due to the unique channel model derived from the position coefficient of the MA. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) method, we develop an efficient solution that optimizes all variables via convex optimization techniques. Extensive simulation results verify the effectiveness of our proposed algorithms and demonstrate the substantial performance promotion by deploying the MA framework in the networked ISAC system.

eess.SP↗

arXiv2025

Active IRS Assisted Joint Uplink and Downlink Communications

In this paper, we investigate an intelligent reflecting surface (IRS) aided wireless communication system, where active IRSs (AIRSs) are deployed to assist communication between a base station (BS) and users of both the uplink (UL) and downlink (DL). We aim to maximize the weighted sum rate (WSR) of UL and DL communications through joint optimization of BS, AIRS beamforming, and AIRS element allocation. First, we study three deployment schemes, namely distributed AIRSs, BS-side AIRS, and user-side AIRS. For distributed AIRSs, both optimal and near-optimal solutions are derived in closed form. To draw useful insights, we analytically compare the deployment schemes in terms of the rate performance under the single-user setup. For the multi-user case, we consider two beamforming setups at the distributed AIRSs to balance performance and complexity tradeoffs. Regarding the user-adaptive AIRS beamforming, different AIRS beamforming vectors are adopted for each user; while for the static AIRS beamforming, all users share the same beamforming vectors, with identical phase shifts but different amplitudes for UL and DL. With the user-adaptive AIRS beamforming, we focus on the optimization of element allocation for rate maximization. With static AIRS beamforming, we solve the rate maximization problem by optimizing the BS transmit/receive beamformers, user beamforming, and AIRS beamforming. Despite its non-convexity, we develop an efficient alternating optimization (AO) based algorithm that solves each sub-problem optimally. Numerical results validate the practical advantages of distributed AIRSs compared to passive IRS (PIRS), BS-side AIRS, and user-side AIRS, and highlight the benefits of dynamic IRS beamforming.

eess.SP↗

arXiv2025

Cooperative Multi-Static ISAC Networks: A Unified Design Framework for Active and Passive Sensing

Multi-static cooperative sensing emerges as a promising technology for advancing integrated sensing and communication (ISAC), enhancing sensing accuracy and range. In this paper, we develop a unified design framework for joint active and passive sensing (JAPS). In particular, we consider a JAPSbased cooperative multi-static ISAC system for coexisting downlink (DL) and uplink (UL) communications. An optimization problem is formulated for maximizing the sum rate of both the DL and UL transmissions via jointly optimizing beamforming, receive filters and power allocation, while guaranteeing the sensing requirements and transmission power constraints. However, the formulated problem is a non-convex optimization problem that is challenging to solve directly due to the tight coupling among optimization variables. To tackle this complicated issue, we employ an efficient algorithm architecture leveraging alternating optimization (AO). Specifically, with the given receive filters and transmission power for UL communication, the transmit beamforming subproblem is addressed by successive convex approximation (SCA)-based and penalty-based algorithms. A fractional programming (FP)-based algorithm is developed to tackle the receive filters and transmission power for UL communication optimization subproblem. Extensive numerical results validate the performance improvement of our proposed JAPS scheme and demonstrate the effectiveness of our proposed algorithms.

eess.SP↗

arXiv2025

Rotatable Antenna Enabled Spectrum Sharing: Joint Antenna Orientation and Beamforming Design

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.

eess.SP↗

arXiv2025

Hierarchical Federated Learning for Social Network with Mobility

Federated Learning (FL) offers a decentralized solution that allows collaborative local model training and global aggregation, thereby protecting data privacy. In conventional FL frameworks, data privacy is typically preserved under the assumption that local data remains absolutely private, whereas the mobility of clients is frequently neglected in explicit modeling. In this paper, we propose a hierarchical federated learning framework based on the social network with mobility namely HFL-SNM that considers both data sharing among clients and their mobility patterns. Under the constraints of limited resources, we formulate a joint optimization problem of resource allocation and client scheduling, which objective is to minimize the energy consumption of clients during the FL process. In social network, we introduce the concepts of Effective Data Coverage Rate and Redundant Data Coverage Rate. We analyze the impact of effective data and redundant data on the model performance through preliminary experiments. We decouple the optimization problem into multiple sub-problems, analyze them based on preliminary experimental results, and propose Dynamic Optimization in Social Network with Mobility (DO-SNM) algorithm. Experimental results demonstrate that our algorithm achieves superior model performance while significantly reducing energy consumption, compared to traditional baseline algorithms.

cs.LG↗

arXiv2025

Wireless Sensing with Movable Intelligent Surface

Future wireless networks are envisioned to deliver not only gigabit communications but also ubiquitous sensing. Reconfigurable intelligent surfaces (RISs) have emerged to reshape radio propagation, recently showing considerable promise for wireless sensing. Still, their per-element electronic tuning incurs prohibitive hardware cost and power consumption. Motivated by the concept of fluid antenna system (FAS), this paper introduces a low-cost movable intelligent surface (MIS) for wireless sensing, which replaces element-wise electronic phase tuning with panel-wise mechanical reconfiguration. The MIS stacks a large fixed and a smaller movable pre-phased metasurface layers, whose differential position shifts synthesize distinct composite phase patterns, enabling multiple beam patterns for multi-target detection. We characterize a MIS-enabled multi-hop echo signal model with multi-target interference and then formulate a worst-case sensing signal-to-interference-plus-noise ratio (SINR) maximization problem that jointly designs MIS phase shifts and schedules MS2's position. A Riemannian Augmented Lagrangian Method (RALM)-based algorithm is developed to solve the formulated mixed-integer non-convex problem. We also derive a heuristic MIS beam steering design with closed-form phase distribution and position scheduling. Simulations validate MIS's beam pattern reconfiguration capability, show that the RALM-based scheme significantly outperforms the closed-form scheme in improving sensing SINR, and uncover a gain-diversity trade-off in beam patterns that informs the optimal choice of MIS configuration.

eess.SP↗