arXiv subjects

Junshan Luo

Publications and source records attributed to Junshan Luo.

10 recordsLinked to original sources

arXiv2026

A Survey of Physical-layer Authentication Enhanced by Emerging Spatial Domain Technologies

This article surveys spatial-domain-enhanced Physical-layer Authentication (PLA), with Dual-polarized Antennas (DPA), Massive Multiple-Input Multiple-Output (MIMO), and Reconfigurable Intelligent Surfaces (RIS) as the primary focus. With the rapid growth of wireless deployments, authentication mechanisms face stringent requirements for high security, low overhead, and low latency. PLA offers lightweight identity verification by exploiting physical-layer characteristics. However, the effectiveness of PLA critically depends on how physical observations are constructed and validated under wireless channels. Unlike existing surveys that mainly organize PLA by authentication modality, feature source, and evaluation metrics, this work emphasizes the connection between spatial-domain enhancement mechanisms, the resulting feature representation, and the authentication procedure. We review how DPA, Massive MIMO, and RIS reshape PLA feature representation, and we summarize newly introduced security threats along with representative defense strategies. Case studies further illustrate the practical impact, such as representative detection-probability trends across Signal-to-Noise Ratio regimes and quantitative comparisons among representative schemes. Finally, we outline promising future opportunities enabled by Dynamic Metasurface Antennas, Extra-large MIMO, and spatial configuration with artificial intelligence.

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arXiv2026

FAS-aided Robust Anti-Jamming Communications: Continuous and Discrete Positioning Designs

This paper investigates the joint optimization of beamforming and antenna positions in fluid antenna system (FAS)-aided anti-jamming communications. We consider a multi-user multiple-input multiple-output downlink scenario where multiple malicious jammers exist and the jammer channel state information is imperfect. The goal is to maximize the worst-case sum-rate under quality-of-service and transmit power constraints. To achieve this, we develop two distinct optimization frameworks for continuous and discrete antenna position designs, respectively. For continuous design, we propose an alternating optimization (AO) framework that integrates successive convex approximation and majorization minimization (MM) to handle the highly non-convex problem. For discrete design, based on the minimum mean squared error criterion and MM, we reformulate the problem as a sparse recovery task and propose a low-complexity block coordinate descent and simultaneous orthogonal matching pursuit, which enables joint design rather than AO. Through systematic comparison, we uncover a practical phenomenon: the discrete joint design yields superior sum-rate performance compared to the AO-based continuous counterpart under identical conditions. This superiority stems from the sparse recovery formulation which effectively circumvents the severe local optima. Our findings challenge the conventional view that continuous optimization is inherently superior, and reveal that discretization combined with sparse recovery can offer a more effective paradigm for exploiting spatial degrees-of-freedom in FAS-aided anti-jamming communications.

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arXiv2025

Specific Multi-emitter Identification: Theoretical Limits and Low-complexity Design

Specific emitter identification (SEI) distinguishes emitters by utilizing hardware-induced signal imperfections. However, conventional SEI techniques are primarily designed for single-emitter scenarios. This poses a fundamental limitation in distributed wireless networks, where simultaneous transmissions from multiple emitters result in overlapping signals that conventional single-emitter identification methods cannot effectively handle. To overcome this limitation, we present a specific multi-emitter identification (SMEI) framework via multi-label learning, treating identification as a problem of directly decoding emitter states from overlapping signals. Theoretically, we establish performance bounds using Fano's inequality. Methodologically, the multi-label formulation reduces output dimensionality from exponential to linear scale, thereby substantially decreasing computational complexity. Additionally, we propose an improved SMEI (I-SMEI), which incorporates multi-head attention to effectively capture features in correlated signal combinations. Experimental results demonstrate that SMEI achieves high identification accuracy with a linear computational complexity. Furthermore, the proposed I-SMEI scheme significantly improves identification accuracy across various overlapping scenarios compared to the proposed SMEI and other advanced methods.

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arXiv2025

Secure Short-Packet Communications for RIS-Assisted AAV Networks

Advancements toward 6G have intensified demands for ultra-reliable low-latency communication, positioning shortpacket communications as a critical technology for autonomous aerial vehicle (AAV) networks. However, the open broadcast nature introduces significant security vulnerabilities. Although physical-layer security offers a low-complexity solution by exploiting wireless channel randomness, the AAV communication performance severely degrades in weak-coverage or non-line-of sight scenarios. To overcome these limitations, this paper proposes a short-packet communications framework for AAV networks that leverages reconfigurable intelligent surfaces (RIS) with the aim of extending coverage and enhancing secrecy capabilities. Analytical frameworks are developed to evaluate the average secrecy throughput (AST) in finite blocklength constraints for both external and internal avesdropping scenarios, which incorporates non-orthogonal multiple access with imperfect successive interference cancellation. Asymptotic approximations of AST are derived as transmit power approaches infinity. Furthermore, we formulate a blocklength optimization problem to maximize the AST, effectively resolving the trade-offs among delay, reliability, and secrecy. Extensive simulations validate the analytical frameworks, which reveal that large-scale RIS deployment significantly boosts AST, and the power allocation coefficient exhibits dual effects in the internal eavesdropping scenario. These observations provide useful insights for designing reliable and secure lowlatency AAV communications systems.

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arXiv2025

Large-scale Aerial Reconfigurable Intelligent Surface-aided Robust Anti-jamming Transmission

Aerial reconfigurable intelligent surfaces (ARIS), deployed on unmanned aerial vehicles (UAVs), could enhance anti-jamming communication performance by dynamically configuring channel conditions and establishing reliable air-ground links. However, large-scale ARIS faces critical deployment challenges due to the prohibitive computational complexity of conventional discrete optimization methods and sophisticated jamming threats. In this paper, we introduce a mean field modeling approach to design the spatial configuration of ARIS by a continuous density function, thus bypassing high-dimensional combinatorial optimization. We consider an adaptive jammer which adjusts its position and beamforming to minimize the sum-rate. A key finding reveals that the jammer's optimal strategy is governed by a proximity-directivity trade-off between reducing path loss and enhancing spatial focusing. To combat the jamming, we propose a robust anti-jamming transmission framework that jointly optimizes the BS beamforming, the ARIS reflection, and the ARIS spatial distribution to maximize the worst-case sum-rate. By leveraging variational optimization and Riemannian manifold methods, we efficiently solve the functional optimization problems. Our analysis further unveils that the optimal ARIS deployment follows a spatial water-filling principle, concentrating resources in high-gain regions while avoiding interference-prone areas. Simulation results demonstrate that the proposed framework remarkably improves the sum-rate. Furthermore, the computational complexity of the proposed algorithm is independent of the number of UAVs, validating its effectiveness for scalable ARIS-assisted anti-jamming communications.

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arXiv2025

Fluid-Antenna-aided AAV Secure Communications in Eavesdropper Uncertain Location

For autonomous aerial vehicle (AAV) secure communications, traditional designs based on fixed position antenna (FPA) lack sufficient spatial degrees of freedom (DoF), which leaves the line-of-sight-dominated AAV links vulnerable to eavesdropping. To overcome this problem, this paper proposes a framework that effectively incorporates the fluid antenna (FA) and the artificial noise (AN) techniques. Specifically, the minimum secrecy rate (MSR) among multiple eavesdroppers is maximized by jointly optimizing AAV deployment, signal and AN precoders, and FA positions. In particular, the worst-case MSR is considered by taking the channel uncertainties due to the uncertainty about eavesdropping locations into account. To tackle the highly coupled optimization variables and the channel uncertainties in the formulated problem, an efficient and robust algorithm is proposed. Particularly, the uncertain regions of eavesdroppers, whose shapes can be arbitrary, are disposed by constructing convex hull. In addition, two movement modes of FAs are considered, namely, free movement mode and zonal movement mode, for which different optimization techniques are applied, respectively. Numerical results show that, the proposed FA schemes boost security by exploiting additional spatial DoF rather than transmit power, while AN provides remarkable gains under high transmit power. Furthermore, the synergy between FA and AN results in a secure advantage that exceeds the sum of their individual contributions, achieving a balance between security and reliability under limited resources.

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arXiv2025

Dual-end Fluid Antennas For Robust Anti-jamming in Low-altitude Air-ground Communications

This paper addresses the challenge of co-channel interference and intentional jamming in low-altitude air-ground communications. Since conventional fixed-position antenna (FPA) systems lack spatial adaptability to dynamically balance signal enhancement against interference suppression, we propose a transformative fluid antenna system (FAS)-assisted heterogeneous dual-layer transmission architecture. Specifically, a terrestrial base station with FPA serves ground users, while a low altitude-serving base station equipped with FAS communicates with the aerial user, also equipped with FAS, under the attack of a malicious jammer. We formulate a worst-case achievable rate maximization problem for aerial user subject to constraints including quality-of-service for terrestrial users, imperfect jamming directions, minimum antenna separation, etc. To address the non-convex problem, we propose a fractional programming-block coordinate descent algorithm that alternately optimizes the transmit precoders, receive combiner, and antenna positions at both transceiver sides. Convex hull-based approach and geometric boundary method are used to handle the jamming uncertainty and antenna placement constraints in confined spatial regions, respectively. Extensive simulations validate significant performance gains. The FAS achieves up to 56\% higher data rates than FPA under equivalent power constraints. Strategic antenna repositioning demonstrably enhances signal quality while suppressing interference, maintaining robustness across diverse jammer channel uncertainties.

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arXiv2025

Towards Secure Semantic Transmission In the Era of GenAI: A Diffusion-based Framework

Semantic communication, due to its focus on the transmitting meaning rather than the raw bit data, poses unique security challenges compared to the traditional communication systems. In particular, semantic communication systems are vulnerable to the malicious attacks that focus on the semantic layer, with the intention of understanding or distorting the intended meaning of the transmitted privacy data. Diffusion models, a class of generative artificial intelligence (GenAI), are well-suited for ensuring data security to attack. Through iteratively adding and then removing noise, diffusion models can generate meaningful information despite the presence of the unknown noise. This article proposes a diffusion-based framework to enhance the security of semantic transmission for the attacks including eavesdropping and jamming. Specifically, the proposed framework incorporates both the artificial noise and natural channel noise into the forward process of the diffusion models during the semantic transmission, with the reverse process used to remove noise at the legitimate receiver. In the eavesdropping scenarios, the artificial noise is the friendly noise designed to prevent semantic eavesdropping. In the jamming scenarios, the artificial noise is the malicious jamming generated by the jammer, which disrupts the semantic transmission. The case studies show that the proposed diffusion-based framework is promising in securing the semantic transmission. We also consolidate several broad research directions associated with the proposed framework.

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arXiv2022

A Learning Aided Flexible Gradient Descent Approach to MISO Beamforming

This paper proposes a learning aided gradient descent (LAGD) algorithm to solve the weighted sum rate (WSR) maximization problem for multiple-input single-output (MISO) beamforming. The proposed LAGD algorithm directly optimizes the transmit precoder through implicit gradient descent based iterations, at each of which the optimization strategy is determined by a neural network, and thus, is dynamic and adaptive. At each instance of the problem, this network is initialized randomly, and updated throughout the iterative solution process. Therefore, the LAGD algorithm can be implemented at any signal-to-noise ratio (SNR) and for arbitrary antenna/user numbers, does not require labelled data or training prior to deployment. Numerical results show that the LAGD algorithm can outperform of the well-known WMMSE algorithm as well as other learning-based solutions with a modest computational complexity. Our code is available at https://github.com/XiaGroup/LAGD.

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arXiv2019

Spatial Coded Modulation

In this paper, we propose a spatial coded modulation (SCM) scheme, which improves the accuracy of the active antenna detection by coding over the transmit antennas. Specifically, the antenna activation pattern in the SCM corresponds to a codeword in a properly designed codebook with a larger minimum Hamming distance than its counterpart conventional spatial modulation. As the minimum Hamming distance increases, the reliability of the active antenna detection is directly enhanced, which in turn improves the demodulation of the modulated symbols and yields a better system reliability. In addition to the reliability, the proposed SCM scheme also achieves a higher capacity with the identical antenna configuration compared to the conventional spatial modulation technique. Moreover, the proposed SCM scheme strikes a balance between spectral efficiency and reliability by trading off the minimum Hamming distance with the number of available codewords. The optimal maximum likelihood detector is first formulated. Then, a low-complexity suboptimal detector is proposed to reduce the computational complexity, which has a two-step detection. Theoretical derivations of the channel capacity and the bit error rate are presented in various channel scenarios, i.e., Rayleigh, Rician, Nakagami-m, imperfect channel state information, and spatial correlation. Further derivation on performance bounding is also provided to reveal the insight of the benefit of increasing the minimum Hamming distance. Numerical results validate the analysis and demonstrate that the proposed SCM outperforms the conventional spatial modulation techniques in both channel capacity and system reliability.

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