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Tuo Wu

Publications and source records attributed to Tuo Wu.

At least 37 records · Page 2Linked to original sources

A Gaussian Process Framework for Outage Analysis in Continuous-Aperture Fluid Antenna Systems

This paper develops a comprehensive analytical framework for the outage probability of fluid antenna system (FAS)-aided communications by modeling the antenna as a continuous aperture and approximating the Jakes (Bessel) spatial correlation with a Gaussian kernel $ρ_G(δ) = e^{-π^2δ^2}$. Three complementary analytical strategies are pursued. First, the Karhunen--Loève (KL) expansion under the Gaussian kernel is derived, yielding closed-form outage expressions for the rank-1 and rank-2 truncations and a Gauss--Hermite formula for arbitrary rank~$K$, with effective degrees of freedom $K_{\mathrm{eff}}^G \approx π\sqrt{2}\, W$. Second, rigorous two-sided outage bounds are established via Slepian's inequality and the Gaussian comparison theorem: by sandwiching the true correlation between equi-correlated models with $ρ_{\min}$ and $ρ_{\max}$, closed-form upper and lower bounds that avoid the optimistic bias of block-correlation models are obtained. Third, a continuous-aperture extreme value theory is developed using the Adler--Taylor expected Euler characteristic method and Piterbarg's theorem. The resulting outage expression $P_{\mathrm{out}} \approx 1 - e^{-x}(1 + π\sqrt{2}\, W\, x)$ depends only on the aperture~$W$ and threshold~$x$, is independent of the port count~$N$, and is identical for the Jakes and Gaussian models since both share the second spectral moment $λ_2 = 2π^2$. A Pickands-constant refinement for the deep-outage regime and a threshold-dependent effective diversity $N_{\mathrm{eff}} \approx 1 + π\sqrt{2}\, W\, x$ are further derived. Numerical results confirm that the Gaussian approximation incurs less than 10\% relative outage error for $W \leq 2$ and that the continuous-aperture formula converges with as few as $N \approx 10W$ ports.

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Physical Layer Security for FAS-Aided Short-Packet Systems: A Variable Block-Correlation Approach

This paper presents a comprehensive physical layer security (PLS) framework for fluid antenna system (FAS)-aided short-packet communications under the variable block-correlation model (VBCM). We consider a downlink wiretap scenario in which a base station transmits confidential short packets to a legitimate receiver user (RU) in the presence of an eavesdropper user (EU), where both the RU and EU are equipped with fluid antennas. Unlike existing FAS security analyses that rely on constant block-correlation models or infinite-blocklength assumptions, we incorporate the VBCM to accurately capture the non-uniform spatial correlation structure inherent in practical FAS deployments. By employing a piecewise linear approximation of the decoding error probability and Gauss-Chebyshev quadrature, we derive closed-form and asymptotic expressions for the average achievable secrecy throughput (AAST). We further prove that the AAST is monotonically non-decreasing in the number of RU ports, which reduces the three-dimensional joint optimization of transmit power, blocklength, and port number to a two-dimensional grid search (GS). Numerical results demonstrate that the FAS-aided system achieves up to an order-of-magnitude secrecy throughput improvement over conventional fixed-position antenna systems, and reveal that blocklength selection is the most critical design parameter in the joint optimization.

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Near-Field NLOS Localization via Position-Unknown HRIS:From Self-Localization to Target Positioning

Current reconfigurable intelligent surface (RIS)-aided near-field (NF) localization methods assume the RIS position is known a priori, and it has limited their practical applicability. This paper applies a hybrid RIS (HRIS) at an unknown position to locate non-line-of-sight (NLOS) NF targets. To this end, we first propose a two-stage gridless localization framework for achieving HRIS self-localization, and then determine the positions of the NF targets. In the first stage, we use the NF Fresnel approximation to convert the signal model into a virtual far-field model through delay-based cross-correlation of centrally symmetric HRIS elements. Such a conversion will naturally extend the aperture of the virtual array. A single-snapshot decoupled atomic norm minimization (DANM) algorithm is then proposed to locate an NF target relative to the HRIS, which includes a two-dimensional (2-D) direction of arrival (DOA) estimation with automatic pairing, the multiple signal classification (MUSIC) method for range estimation, and a total least squares (TLS) method to eliminate the Fresnel approximation error. In the second stage, we leverage the unique capability of HRIS in simultaneous sensing and reflection to estimate the HRIS-to-base station (BS) direction vectors using atomic norm minimization (ANM), and derive the three-dimensional (3-D) HRIS position with two BSs via the least squares (LS)-based geometric triangulation. Furthermore, we propose a semidefinite relaxation (SDR)-based HRIS phase optimization method to enhance the received signal power at the BSs, thereby improving the HRIS localization accuracy, which, in turn, enhances NF target positionings. The Cramer-Rao bound (CRB) for the NF target parameters and the position error bound (PEB) for the HRIS coordinates are derived as performance benchmarks.

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Artificial Noise Versus Artificial Noise Elimination: Redefining Scaling Laws of Physical Layer Security

Artificial noise (AN) is a key physical-layer security scheme for wireless communications over multiple-input multiple-output wiretap channels. Recently, artificial noise elimination (ANE) has emerged as a strategy to mitigate the impact of AN on eavesdroppers. However, the influence of ANE on the secrecy rate when counteracting AN has not been investigated. In this paper, we address this issue by establishing scaling laws for both average and instantaneous secrecy rates in the presence of AN and ANE. Based on the scaling laws, several derived corollaries provide insights into the mutual constraints between the number of transmit antennas, receive antennas, and antennas at eavesdroppers, revealing the interplay between these factors. A key corollary reveals that when the eavesdropper possesses more than twice as many antennas as the transmitter, secure communication may no longer be guaranteed. Additionally, by comparing scenarios where ANE counteracts AN with those where AN is not employed, this study identifies sufficient conditions under which AN remains effective. Finally, the derived secrecy rates provide guidelines for system design, even in the presence of advanced ANE countermeasures implemented by the eavesdropper.

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Fluid Antennas Meet Intelligent Surfaces: Security Analysis of NOMA Systems Under Hardware Impairments

The revolutionary convergence of fluid antenna systems (FAS) and reconfigurable intelligent surfaces (RIS) creates unprecedented opportunities for secure wireless communications, yet the practical implications of hardware impairments on this promising combination remain largely unexplored. This paper investigates the security performance of non-orthogonal multiple access (NOMA) systems when fluid antennas (FAs) meet intelligent surfaces under realistic hardware constraints. We develop a comprehensive analytical framework that captures the complex interplay between adaptive spatial diversity, intelligent signal reflection, and hardware-induced distortions in short-packet communications. Through novel piecewise linear approximations and block-correlation models, we derive tractable expressions for average secure block error rate (BLER) that reveal fundamental performance limits imposed by hardware impairments. Our analysis demonstrates that while the synergy between FAs and intelligent surfaces offers remarkable degrees of freedom for security enhancement, practical hardware imperfections create performance ceilings that persist regardless of spatial diversity gains. The theoretical framework exposes critical design trade-offs between system complexity and achievable security performance, showing that hardware quality becomes a decisive factor in realizing the full potential of FAS-RIS architectures. Extensive simulations validate our analytical insights and provide practical design guidelines for implementing secure NOMA systems that effectively balance the benefits of fluid-intelligent cooperation against the constraints of realistic hardware limitations.

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Taming the Bessel Landscape: Joint Antenna Position Optimization for Spatial Decorrelation in Fluid MIMO Systems

When the concept of fluid antenna system (FAS) is applied to multiple-input multiple-output (MIMO) systems, this gives rise to MIMO-FAS, a.k.a.~fluid MIMO. Under rich scattering, the spatial correlation matrices are governed by the zeroth-order Bessel function $J_0(\cdot)$ through the continuously adjustable antenna positions, creating a highly non-convex landscape for optimization with fluctuating local optima -- the \emph{Bessel landscape}. In this paper, we tackle the joint transmitter (TX) and receiver (RX) antenna position optimization problem in fluid MIMO to maximize the ergodic capacity by shaping this landscape. Using Kronecker channel decomposition, we firstly develop a suite of analytical results that expose the problem's intrinsic structure: (i) a high signal-to-noise ratio (SNR) capacity approximation that decomposes the objective into separable log-determinant terms of the TX and RX correlation matrices, $\mathbf{R}_T$ and $\mathbf{R}_R$, respectively, (ii) a closed-form capacity loss bound linking $\det(\mathbf{R}_T)\det(\mathbf{R}_R)$ to the performance gap relative to the independent and identically distributed (i.i.d.) ideal MIMO channel, and (iii) the globally optimal inter-element spacing when the number of fluid elements at the TX is $N=2$ at the first zero of $J_0$. Guided by these insights, we propose two algorithms within an alternating optimization (AO) framework. The first algorithm is AO with particle swarm optimization (PSO) which deploys a particle swarm to explore the Bessel landscape globally without gradient information. Then in the second method, we use successive convex approximation (SCA) to obtain the gradient in closed form via $J_1(\cdot)$ to construct convex surrogates for orders-of-magnitude faster convergence.

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Beyond $λ/2$: Can Arbitrary EMVS Arrays Achieve Unambiguous NLOS Localization?

Conventional radar array design mandates interelement spacing not exceeding half a wavelength ($λ/2$) to avoid spatial ambiguity, fundamentally limiting array aperture and angular resolution. This paper addresses the fundamental question: Can arbitrary electromagnetic vector sensor (EMVS) arrays achieve unambiguous reconfigurable intelligent surface (RIS)-aided localization when element spacing exceeds $λ/2$? We provide an affirmative answer by exploiting the multi-component structure of EMVS measurements and developing a synergistic estimation and optimization framework for non-line-of-sight (NLOS) bistatic multiple input multiple output (MIMO) radar. A third-order parallel factor (PARAFAC) model is constructed from EMVS observations, enabling natural separation of spatial, polarimetric, and propagation effects via the trilinear alternating least squares (TALS) algorithm. A novel phase-disambiguation procedure leverages rotational invariance across the six electromagnetic components of EMVSs to resolve $2π$ phase wrapping in arbitrary array geometries, allowing unambiguous joint estimation of two-dimensional (2-D) direction of departure (DOD), two-dimensional direction of arrival (DOA), and polarization parameters with automatic pairing. To support localization in NLOS environments and enhance estimation robustness, a reconfigurable intelligent surface (RIS) is incorporated and its phase shifts are optimized via semidefinite programming (SDP) relaxation to maximize received signal power, improving signal-to-noise ratio (SNR) and further suppressing spatial ambiguities through iterative refinement.

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UAV-Enabled Short-Packet Communication via Fluid Antenna Systems

This paper develops a framework for analyzing UAV-enabled short-packet communication, leveraging fluid antenna system (FAS)-assisted relaying networks. Operating in the short-packet regime and focusing on challenging urban environments, we derive novel, closed-form expressions for the block error rate (BLER). This is achieved by modeling the spatially correlated Nakagami-$m$ fading link via a tractable eigenvalue-based approach. A high-signal-to-noise ratio (SNR) asymptotic analysis is also presented, revealing the system's fundamental diversity order. Building on this analysis, we formulate a novel energy efficiency (EE) maximization problem that, unlike idealized models, uniquely incorporates the non-trivial time and energy overheads of FAS port selection. An efficient hierarchical algorithm is proposed to jointly optimize key system parameters. Numerical results validate our analysis, demonstrating that while FAS provides substantial power gains, the operational overhead creates a critical trade-off. This trade-off dictates an optimal number of FAS ports and a non-trivial optimal UAV deployment altitude, governed by the balance between blockage and path loss. This work provides key insights for FAS-aided UAV communications.

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From Optimization to Learning: Dual-Approach Resource Allocation for Over-the-Air Edge Computing Under Execution Uncertainty

The exponential proliferation of mobile devices and data-intensive applications in future wireless networks imposes substantial computational burdens on resource-constrained devices, thereby fostering the emergence of over-the-air computation (AirComp) as a transformative paradigm for edge intelligence.} To enhance the efficiency and scalability of AirComp systems, this paper proposes a comprehensive dual-approach framework that systematically transitions from traditional mathematical optimization to deep reinforcement learning (DRL) for resource allocation under execution uncertainty. Specifically, we establish a rigorous system model capturing execution uncertainty via Gamma-distributed computational workloads, resulting in challenging nonlinear optimization problems involving complex Gamma functions. For single-user scenarios, we design advanced block coordinate descent (BCD) and majorization-maximization (MM) algorithms, which yield semi-closed-form solutions with provable performance guarantees. However, conventional optimization approaches become computationally intractable in dynamic multi-user environments due to inter-user interference and resource contention. To this end, we introduce a Deep Q-Network (DQN)-based DRL framework capable of adaptively learning optimal policies through environment interaction. Our dual methodology effectively bridges analytical tractability with adaptive intelligence, leveraging optimization for foundational insight and learning for real-time adaptability. Extensive numerical results corroborate the performance gains achieved via increased edge server density and validate the superiority of our optimization-to-learning paradigm in next-generation AirComp systems.

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FAS-RIS for V2X: Unlocking Realistic Performance Analysis with Finite Elements

The synergy of fluid antenna systems (FAS) and reconfigurable intelligent surfaces (RIS) is poised to unlock robust Vehicle-to-Everything (V2X) communications. However, a critical gap persists between theoretical predictions and real-world performance. Existing analyses predominantly rely on the Central Limit Theorem (CLT), an assumption valid only for a large number of RIS elements, which fails to represent practical, finite-sized deployments constrained by cost and urban infrastructure. This paper bridges this gap by presenting a novel framework that unlocks a realistic performance analysis for FAS-RIS systems with finite elements. Leveraging a Gamma distribution approximation, we derive a new, tractable closed-form expression for the outage probability. Numerical results validate our approach, demonstrating that it offers a significantly more accurate performance characterization than conventional CLT-based methods, particularly in the practical regime of small-scale RIS. This work provides a crucial foundation for the design and deployment of reliable FAS-RIS-aided vehicular networks.

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An Fluid Antenna Array-Enabled DOA Estimation Method: End-Fire Effect Suppression

Direction of Arrival (DOA) estimation serves as a critical sensing technology poised to play a vital role in future intelligent and ubiquitous communication systems. Despite the development of numerous mature super-resolution algorithms, the inherent end-fire effect problem in fixed antenna arrays remains inadequately addressed. This work proposed a novel array architecture composed of fluid antennas. By exploiting the spatial reconfigurability of their positions to equivalently modulate the array steering vector and integrating it with the classical MUSIC algorithm, this approach achieved high-precision DOA estimation. Simulation results demonstrated that the proposed method delivers outstanding estimation performance even in highly challenging end-fire regions.

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Reimagining Wireless Connectivity: The FAS-RIS Synergy for 6G Smart Cities

Fluid antenna system (FAS) represents the concept of treating antenna as a reconfigurable physical-layer resource to broaden system design and network optimization and inspire next-generation reconfigurable antennas. FAS can unleash new degree of freedom (DoF) via antenna reconfigurations for novel spatial diversity. Reconfigurable intelligent surfaces (RISs) on the other hand can reshape wireless propagation environments but often face limitations from double path-loss and minimal signal processing capability when operating independently. This article envisions a transformative FAS-RIS integrated architecture for future smart city networks, uniting the adaptability of FAS with the environmental control of RIS. The proposed framework has five key applications: FAS-enabled base stations (BSs) for large-scale beamforming, FAS-equipped user devices with finest spatial diversity, and three novel RIS paradigms -- fluid RIS (FRIS) with reconfigurable elements, FAS-embedded RIS as active relays, and enormous FAS (E-FAS) exploiting surface waves on facades to re-establish line-of-sight (LoS) communication. A two-timescale control mechanism coordinates network-level beamforming with rapid, device-level adaptation. Applications spanning from simultaneous wireless information and power transfer (SWIPT) to integrated sensing and communications (ISAC), with challenges in co-design, channel modeling, and optimization, are discussed. This article concludes with simulation results demonstrating the robustness and effectiveness of the FAS-RIS system.

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Intelligent Sky Mirrors: SAC-Driven MF-RIS Optimization for Secure NOMA in Low-Altitude Economy

Low-altitude economy (LAE) has become a key driving force for smart cities and economic growth. To address spectral efficiency and communication security challenges in LAE, this paper investigates secure energy efficiency (SEE) maximization using intelligent sky mirrors, UAV-mounted multifunctional reconfigurable intelligent surfaces (MF-RIS) assisting nonorthogonal multiple access (NOMA) systems. These aerial mirrors intelligently amplify legitimate signals while simultaneously generating jamming against eavesdroppers. We formulate a joint optimization problem encompassing UAV trajectory, base station power allocation, RIS phase shifts, amplification factors, and scheduling matrices. Given the fractional SEE objective and dynamic UAV scenarios, we propose a two-layer optimization scheme: SAC-driven first layer for trajectory and power management, and channel alignment-based second layer for phase optimization. Simulations demonstrate that our proposed scheme significantly outperforms benchmark approaches.

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FAS-RSMA: Can Fluid Antennas Elevate RSMA Performance?

As 6G networks demand massive connectivity and stronger interference control, rate-splitting multiple access (RSMA) is attractive because it superposes a common stream and user-private streams and remains effective under imperfect CSIT and heterogeneous traffic. In practical multiuser deployments, two considerations arise: the common stream decoding constraint imposed by the weakest user, and residual inter-user interference can remain non-negligible, particularly in single-input single-output (SISO) broadcast settings and under an imperfect CSIT scenario. Motivated by prior advances of RSMA research, we investigate a complementary mechanism-fluid antenna systems (FAS), with dynamic port reconfiguration supplies adaptive spatial selectivity without altering the RSMA signaling structure. Can FAS help alleviate these considerations and enhance RSMA performance? We develop a tractable correlation-aware analytical framework based on block-correlation models, including constant block correlation (CBC) and variable block correlation (VBC), to capture realistic spatial dependence among ports. Closed-form expressions are derived for outage probability (OP) and average capacity (AC), revealing how port reconfiguration strengthens the weakest effective channel and improves SINR through higher channel gains and lower relative noise impact. Monte Carlo simulations verify the analysis and show that VBC matches simulations more tightly than CBC across all port configurations. Finally, FAS-RSMA provides clear gains over conventional antenna systems and NOMA, achieving lower OP and higher AC by combining RSMA interference management with FAS spatial diversity.

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Fluid Antenna System-Enabled UAV Communications in the Finite Blocklength Regime

This paper develops a comprehensive framework for the performance analysis of fluid antenna system (FAS)-enabled unmanned aerial vehicle (UAV) relaying networks operating in the finite blocklength regime. Our contribution lies in establishing a rigorous methodology for characterizing system reliability under diverse propagation environments. Closed-form expressions for the block error rate (BLER) are derived by employing a tractable eigenvalue-based approximation of the spatially correlated UAV-to-user link, whose underlying independent diversity components are modeled as Nakagami-$m$ fading. This approach addresses both line-of-sight (LoS) dominant rural and probabilistic non-line-of-sight (NLoS) urban scenarios. Furthermore, a high signal-to-noise ratio (SNR) asymptotic analysis is developed, revealing the fundamental diversity order of the UAV-to-user link. Based on this, we further address the practical issue of energy efficiency. A realistic energy efficiency maximization problem is formulated, which explicitly accounts for the time and energy overhead inherent in the FAS port selection process, a factor often omitted in idealized models. An efficient hierarchical algorithm is then proposed to jointly optimize the key system parameters. Extensive numerical results validate the analysis and illustrate that while FASs can yield substantial power gains, the operational overhead introduces a non-trivial trade-off. This trade-off leads to an optimal number of ports and fundamentally different UAV deployment strategies in rural versus urban environments. This work provides both foundational analysis and practical design guidelines for FAS-enabled UAV communications.

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On the Fundamental Scaling Laws of Fluid Antenna Systems

Fluid antenna systems (FAS) offer a promising paradigm for enhancing wireless communication by exploiting spatial diversity, yet a rigorous analytical framework for their error probability has been notably absent. To this end, this paper addresses this critical gap by unveiling the \textbf{fundamental scaling laws} that govern the symbol error rate (SER) of FAS in realistic, spatially correlated channels. To establish these laws, we derive a tight, closed-form asymptotic expression for the SER applicable to a general class of modulation schemes. This result is pivotal as it establishes the fundamental scaling law governing the relationship between SER and the channel's spatial correlation structure. Based on this framework, we provide a complete characterization of the diversity and coding gains. The analysis culminates in a definitive design directive: SER can be fundamentally improved by expanding the antenna's movement space to increase diversity, while merely increasing port density within a constrained space yields diminishing returns.

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Variable Block-Correlation Modeling and Optimization for Secrecy Analysis in Fluid Antenna Systems

Fluid antenna systems (FAS) are emerging as a transformative enabler for sixth-generation (6G) wireless communications, providing unprecedented spatial diversity through dynamic reconfiguration of antenna ports. However, the inherent spatial correlation among ports poses significant challenges for accurate analysis. Conventional models such as Jakes are analytically intractable, while oversimplified constant-correlation models fail to capture the true behavior. In this work, we address these challenges by applying the variable block-correlation model (VBCM) -- originally proposed by Ramírez-Espinosa \textit{et al.} in 2024 -- to FAS security analysis, and by developing comprehensive optimization methods to enhance analytical accuracy. We derive new closed-form expressions for average secrecy capacity (ASC) and secrecy outage probability (SOP), demonstrating that the VBCM framework achieves simulation-aligned accuracy, with relative errors consistently below $5\%$ (compared to $10$--$15\%$ for constant-correlation models). To maximize ASC, we further design two algorithms: a grid search (GS) method and a gradient descent (GD) method. Numerical results reveal that the VBCM-based approach not only provides reliable insights into FAS security performance, but also yields substantial gains -- ASC improvements exceeding $120\%$ in high-threat scenarios and $18$--$19\%$ performance enhancements for compact antenna configurations. These findings underscore the practical value of integrating VBCM into FAS security analysis and optimization, establishing it as a powerful tool for advancing 6G communication systems.

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Meta Fluid Antenna: Architecture Design, Performance Analysis, Experimental Examination

Fluid antenna systems (FAS) have recently emerged as a promising solution for sixth-generation (6G) ultra-dense connectivity. These systems utilize dynamic radiating and/or shaping techniques to mitigate interference and improve spectral efficiency without relying on channel state information (CSI). The reported improvements achieved by employing a single dynamically activated radiating position in fluid antenna multiple access (FAMA) are significant. To fully realize the potential of FAMA in multi-user multiplexing, we propose leveraging the unique fast-switching capabilities of a single radio-frequency (RF)-chain meta-fluid antenna structure to achieve multi-activation. This allows for a significantly larger set of independent radiating states without requiring additional signal processing. Simulations demonstrate that multi-activation FAMA enables robust multi-user multiplexing with a higher signal-to-interference ratio (SIR) under various Rayleigh-fading environments compared to other single RF-chain technologies. We further show that the SIR can be optimized within a 15~$μs$ timeframe under a multi-user Rayleigh-fading channel, making the proposed scheme highly suitable for fast-changing wireless environments. Verified through the theoretical Jakes' model, full three-dimensional (3D) electromagnetic (EM) simulations and experimental validation, multi-activation FAMA enables effective CSI-free, multi-user communication, offering a scalable solution for high-capacity wireless networks.

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