arXiv subjects

Qixun Zhang

Publications and source records attributed to Qixun Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Beyond Single-Band: Analysis and Resource Allocation for Multi-band ISAC Systems

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for sixth-generation wireless networks to empower high-precision sensing. The demand for superior sensing resolution and the reality of spectrum fragmentation have driven the research of multi-band ISAC. Multi-band ISAC provides frequency diversity through independent observations across disparate bands, mitigating sensing performance fluctuations caused by frequency-selective radar cross-section compared to single-band counterparts. In this paper, we propose a framework for analytical performance characterization and resource optimization in multi-band ISAC systems. Specifically, analytical closed-form detection and false alarm probabilities for multi-band OFDM signals are derived, providing a theoretical foundation for subsequent resource allocation. Then, a joint power and time-frequency resource allocation scheme is developed and solved via a proposed ADMM-based algorithm to maximize detection performance. Numerical results validate the accuracy of the closed-form derivations and demonstrate the superior robustness of multi-band signals. Notably, the proposed optimization scheme achieves an 18 dB detection gain over traditional single-band baselines at a 90\% detection probability.

eess.SP↗

arXiv2026

TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles

Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints, ranging from verifiable ones (e.g., output length) to unverifiable ones (e.g., tone). Reinforcement learning with verifiable rewards has emerged as a paradigm for IF tasks, leveraging LLM-as-a-judge to assess unverifiable constraints. However, we empirically find that this approach remains a significant bottleneck, suffering from severe reward hacking and higher computational overhead. In this work, we first analyze the generalization capabilities of unverifiable constraints and discover that specific constraints exhibit distinct, high-generalization patterns. Motivated by this, we propose TinyJudge, a framework that employs an ensemble of specialized tiny language models ($\sim0.6B$) to provide rewards for soft constraints. By distilling expertise from frontier models into these tiny models, it achieves high-precision, lightweight evaluation. Extensive evaluations across five benchmarks demonstrate that TinyJudge outperforms the baselines by $\sim10\%$ in average performance and $12\%$ in reward precision. Crucially, it also achieves a $3\times$ speedup in total training time. Our work provides a scalable and robust path for aligning LLMs with unverifiable human instructions.

cs.CL↗

arXiv2026

Upward Spatial Coverage Recovery via Movable Antenna in Low-Altitude Communications

The rapid proliferation of unmanned aerial vehicle (UAV) applications imposes stringent requirements on continuous and reliable communication coverage in low-altitude airspace. Conventional cellular systems built upon fixed-position antennas (FPAs) are inherently constrained by static array geometries and limited mechanical degrees of freedom, which severely restrict their ability to adapt to highly dynamic three-dimensional (3D) propagation environments. Movable antenna (MA) technology has recently emerged as a promising paradigm to overcome these limitations by actively reconfiguring electromagnetic radiation characteristics through controllable antenna positioning and array orientation, thereby enabling flexible spatial coverage adaptation. To systematically quantify the airspace coverage capability of MA-enabled systems, this paper formulates a spatial coverage maximization problem over a discretized 3D voxel space. For each voxel, the received signal-to-noise ratio (SNR) is maximized via joint optimization of the MA's 3D positions and beamforming matrices. To efficiently solve the resulting non-convex problem, a hybrid particle swarm optimization and simulated annealing framework is developed to search for high-quality antenna configurations. Simulation results demonstrate that the proposed MA design framework substantially outperforms conventional FPA-based schemes in terms of spatial coverage, achieving coverage rates of 26.8% and 29.65% for airspace below 300m and 600m, respectively. Moreover, further coverage enhancement can be attained by incorporating mechanical tilt adjustment, highlighting the strong potential of MA technology for reliable low-altitude communication coverage.

cs.IT↗

arXiv2026

Precision over Diversity: High-Precision Reward Generalizes to Robust Instruction Following

A central belief in scaling reinforcement learning with verifiable rewards for instruction following (IF) tasks is that, a diverse mixture of verifiable hard and unverifiable soft constraints is essential for generalizing to unseen instructions. In this work, we challenge this prevailing consensus through a systematic empirical investigation. Counter-intuitively, we find that models trained on hard-only constraints consistently outperform those trained on mixed datasets. Extensive experiments reveal that reward precision, rather than constraint diversity, is the primary driver of effective alignment. The LLM judge suffers from a low recall rate in detecting false response, which leads to severe reward hacking, thereby undermining the benefits of diversity. Furthermore, analysis of the attention mechanism reveals that high-precision rewards develop a transferable meta-skill for IF. Motivated by these insights, we propose a simple yet effective data-centric refinement strategy that prioritizes reward precision. Evaluated on five benchmarks, our approach outperforms competitive baselines by 13.4\% in performance while achieving a 58\% reduction in training time, maintaining strong generalization beyond instruction following. Our findings advocate for a paradigm shift: moving away from the indiscriminate pursuit of data diversity toward high-precision rewards.

cs.LG↗

arXiv2025

A Location-Aware Hybrid Deep Learning Framework for Dynamic Near-Far Field Channel Estimation in Low-Altitude UAV Communications

In low altitude UAV communications, accurate channel estimation remains challenging due to the dynamic nature of air to ground links, exacerbated by high node mobility and the use of large scale antenna arrays, which introduce hybrid near and far field propagation conditions. While conventional estimation methods rely on far field assumptions, they fail to capture the intricate channel variations in near-field scenarios and overlook valuable geometric priors such as real-time transceiver positions. To overcome these limitations, this paper introduces a unified channel estimation framework based on a location aware hybrid deep learning architecture. The proposed model synergistically combines convolutional neural networks (CNNs) for spatial feature extraction, bidirectional long short term memory (BiLSTM) networks for modeling temporal evolution, and a multihead self attention mechanism to enhance focus on discriminative channel components. Furthermore, real-time transmitter and receiver locations are embedded as geometric priors, improving sensitivity to distance under near field spherical wavefronts and boosting model generalization. Extensive simulations validate the effectiveness of the proposed approach, showing that it outperforms existing benchmarks by a significant margin, achieving at least a 30.25% reduction in normalized mean square error (NMSE) on average.

cs.IT↗

arXiv2025

Moving or Predicting? RoleAware-MAPP: A Role-Aware Transformer Framework for Movable Antenna Position Prediction to Secure Wireless Communications

Movable antenna (MA) technology provides a promising avenue for actively shaping wireless channels through dynamic antenna positioning, thereby enabling electromagnetic radiation reconstruction to enhance physical layer security (PLS). However, its practical deployment is hindered by two major challenges: the high computational complexity of real time optimization and a critical temporal mismatch between slow mechanical movement and rapid channel variations. Although data driven methods have been introduced to alleviate online optimization burdens, they are still constrained by suboptimal training labels derived from conventional solvers or high sample complexity in reinforcement learning. More importantly, existing learning based approaches often overlook communication-specific domain knowledge, particularly the asymmetric roles and adversarial interactions between legitimate users and eavesdroppers, which are fundamental to PLS. To address these issues, this paper reformulates the MA positioning problem as a predictive task and introduces RoleAware-MAPP, a novel Transformer based framework that incorporates domain knowledge through three key components: role-aware embeddings that model user specific intentions, physics-informed semantic features that encapsulate channel propagation characteristics, and a composite loss function that strategically prioritizes secrecy performance over mere geometric accuracy. Extensive simulations under 3GPP-compliant scenarios show that RoleAware-MAPP achieves an average secrecy rate of 0.3569 bps/Hz and a strictly positive secrecy capacity of 81.52%, outperforming the strongest baseline by 48.4% and 5.39 percentage points, respectively, while maintaining robust performance across diverse user velocities and noise conditions.

cs.IT↗

arXiv2025

Predictive Position Control for Movable Antenna Arrays in UAV Communications: A Spatio-Temporal Transformer-LSTM Framework

In complex urban environments, dynamic obstacles and multipath effects lead to significant link attenuation and pervasive coverage blind spots. Conventional approaches based on large-scale fixed antenna arrays and UAV trajectory optimization struggle to balance energy efficiency, real-time adaptation, and spatial flexibility. The movable antenna (MA) technology has emerged as a promising solution, offering enhanced spatial flexibility and reduced energy consumption to overcome the bottlenecks of urban low-altitude communications. However, MA deployment faces a critical velocity mismatch between UAV mobility and mechanical repositioning latency, undermining real-time link optimization and security assurance. To overcome this, we propose a predictive MA-UAV collaborative control framework. First, optimal antenna positions are derived via secrecy rate maximization. Second, a Transformer-enhanced long short-term memory (LSTM) network predicts future MA positions by capturing spatio-temporal correlations in antenna trajectories. Extensive simulations demonstrate superior prediction accuracy (NMSE reduction exceeds 49\%) and communication reliability versus current popular benchmarks.

cs.IT↗

arXiv2025

Does Movable Antenna Present A Dual-edged Nature? From the Perspective of Physical Layer Security: A Joint Design of Fixed-position Antenna and Movable Antenna

In conventional artificial noise (AN)-aided physical-layer security systems, fixed-position antenna (FPA) arrays exhibit inherent vulnerability to coverage gaps due to their static spatial configuration. Adversarial eavesdroppers can strategically exploit their mobility to infiltrate these spatial nulls of AN radiation patterns, thereby evading interference suppression and successfully intercepting the confidential communication. To overcome this limitation, in this paper, we investigate a hybrid antenna deployment framework integrating FPA arrays and movable antenna (MA) arrays (denoted by FMA co-design) to address the security performance in dynamic wireless environments, based on the fact that MA arrays enable channel reconfiguration through localized antenna repositioning, achieving more higher spatial degree of freedom (DoF). Enabled by FMA co-design framework, FPA arrays ensure baseline connectivity for legitimate links while MA arrays function as dynamic security enhancers, replacing conventional static AN generation. Furthermore, we formulate a non-convex optimization problem of the secrecy rate maximization through jointly optimizing MA positioning, FPA beamforming, and MA beamforming under practical constraints. the solution employs a dual-algorithm approach: Nesterov momentum-based projected gradient ascent (NMPGA) accelerates convergence in continuous position optimization, while alternating optimization (AO) handles coupled beamforming design. Experimental evaluations demonstrate that the proposed FMA co-design framework achieves significant secrecy performance gains over individual optimization benchmarks, yielding 42.34% and 9.12% improvements in secrecy rate compared to isolated FPA for AN generation and MA for confidential information baselines, respectively.

cs.IT↗

arXiv2025

An Effective Equivalence Model of Analyzing PLS of Multiple Eavesdroppers Facing Low-altitude Communication Systems

In low-altitude wireless communications, the increased complexity of wireless channels and the uncertainty of eavesdroppers (Eves)--caused by diverse altitudes, speeds, and obstacles--pose significant challenges to physical layer security (PLS) technologies based on fixed-position antennas (FPAs), particularly in terms of beamforming capabilities and spatial efficiency. In contrast, movable antennas (MAs) offer a flexible solution by enabling channel reconstruction through antenna movement, effectively compensating for the limitations of FPAs. In this paper, we aim to derive a closed-form expression for the secrecy rate, a key metric in PLS, which is often unattainable in current studies due to the uncertainty of Eves. We construct an equivalent model that leverages the reconfigurable nature of MAs, equating the secrecy rates obtained by multiple Eves with single FPAs to those achieved by a single virtual Eve equipped with an MA array. To minimize the gap between these two types of secrecy rates, we formulate and solve an optimization problem by jointly designing the equivalent distance between the transmitter and the virtual Eve} and the antenna positions of MAs at the virtual Eve. Numerical simulations validate the effectiveness of the proposed equivalent model, offering a new perspective for PLS strategies. This work provides significant insights for network designers on how system parameters affect PLS performance.

cs.IT↗

arXiv2025

Can Movable Antenna-enabled Micro-Mobility Replace UAV-enabled Macro-Mobility? A Physical Layer Security Perspective

This paper investigates the potential of movable antenna (MA)-enabled micro-mobility to replace UAV-enabled macro-mobility for enhancing physical layer security (PLS) in air-to-ground communications. While UAV trajectory optimization offers high flexibility and Line-of-Sight (LoS) advantages, it suffers from significant energy consumption, latency, and complex trajectory optimization. Conversely, MA technology provides fine-grained spatial reconfiguration (antenna positioning within a confined area) with ultra-low energy overhead and millisecond-scale response, enabling real-time channel manipulation and covert beam steering. To systematically compare these paradigms, we establish a dual-scale mobility framework where a UAV-mounted uniform linear array (ULA) serves as a base station transmitting confidential information to a legitimate user (Bob) in the presence of an eavesdropper (Eve). We formulate non-convex average secrecy rate (ASR) maximization problems for both schemes: 1) MA-based micro-mobility: Jointly optimizing antenna positions and beamforming (BF) vectors under positioning constraints; 2) UAV-based macro-mobility: Jointly optimizing the UAV's trajectory and BF vectors under kinematic constraints. Extensive simulations reveal distinct operational regimes: MA micro-mobility demonstrates significant ASR advantages in low-transmit-power scenarios or under antenna constraints due to its energy-efficient spatial control. Conversely, UAV macro-mobility excels under resource-sufficient conditions (higher power, larger antenna arrays) by leveraging global mobility for optimal positioning. The findings highlight the complementary strengths of both approaches, suggesting hybrid micro-macro mobility as a promising direction for balancing security, energy efficiency, and deployment complexity in future wireless networks.

cs.IT↗

arXiv2025

Toward Low-Altitude Airspace Management and UAV Operations: Requirements, Architecture and Enabling Technologies

The low-altitude economy (LAE) is rapidly advancing toward intelligence, connectivity, and coordination, bringing new challenges in dynamic airspace management, unmanned aerial vehicle (UAV) operation, and security management. Existing systems remain fragmented and lack effective coordination. To bridge these gaps, we propose UTICN (Ubiquitous and Trusted Intelligent Cellular-native Network) for LAE, a unified cellular-native architecture that integrates multi-domain sensing, high-precision positioning, intelligent aircraft-to-everything communication, dynamic airspace management, and UAV operational services. UTICN introduces key technologies such as integrated sensing and communication (ISAC), passive and active positioning, intelligent machine communication, swarm coordination, and control-data decoupled management frameworks. We demonstrate UTICN's feasibility through two use cases, i.e., a city-level LAE management platform and a multi-frequency collaborative ISAC system. This work provides a fundamental reference for building a unified operational foundation and airspace management architecture for the LAE.

eess.SY↗

arXiv2025

First Glimpse on Physical Layer Security in Internet of Vehicles: Transformed from Communication Interference to Sensing Interference

Integrated sensing and communication (ISAC) plays a crucial role in the Internet of Vehicles (IoV), serving as a key factor in enhancing driving safety and traffic efficiency. To address the security challenges of the confidential information transmission caused by the inherent openness nature of wireless medium, different from current physical layer security (PLS) methods, which depends on the \emph{additional communication interference} costing extra power resources, in this paper, we investigate a novel PLS solution, under which the \emph{inherent radar sensing interference} of the vehicles is utilized to secure wireless communications. To measure the performance of PLS methods in ISAC-based IoV systems, we first define an improved security performance metric called by transmission reliability and sensing accuracy based secrecy rate (TRSA\_SR), and derive closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), success ranging probability (SRP) for evaluating transmission reliability, security and sensing accuracy, respectively. Furthermore, we formulate an optimization problem to maximize the TRSA\_SR by utilizing radar sensing interference and joint design of the communication duration, transmission power and straight trajectory of the legitimate transmitter. Finally, the non-convex feature of formulated problem is solved through the problem decomposition and alternating optimization. Simulations indicate that compared with traditional PLS methods obtaining a non-positive STC, the proposed method achieves a secrecy rate of 3.92bps/Hz for different settings of noise power.

cs.IT↗

arXiv2024

Deep Cooperation in ISAC System: Resource, Node and Infrastructure Perspectives

With the emerging Integrated Sensing and Communication (ISAC) technique, exploiting the mobile communication system with multi-domain resources, multiple network elements, and large-scale infrastructures to realize cooperative sensing is a crucial approach satisfying the requirements of high-accuracy and large-scale sensing in IoE. In this article, the deep cooperation in ISAC system including three perspectives is investigated. In the microscopic perspective, namely, within a single node, the sensing information carried by time-frequency-space-code domain resources is processed, such as phase compensation, coherent accumulation and other operations, thereby improving the sensing accuracy. In the mesoscopic perspective, the sensing accuracy could be improved through the cooperation of multiple nodes. We explore various multi-node cooperative sensing scenarios and present the corresponding challenges and future research trends. In the macroscopic perspective, the massive number of infrastructures from the same operator or different operators could perform cooperative sensing to extend the sensing coverage and improve the sensing continuity. We investigate network architecture, target tracking methods, and the large-scale sensing assisted digital twin construction. Simulation results demonstrate the superiority of multi-nodes and multi-resources cooperative sensing over single resource or node sensing. This article may provide a deep and comprehensive view on the cooperative sensing in ISAC system to enhance the performance of sensing, supporting the applications of IoE.

eess.SP↗

arXiv2024

UAV's Rotor Micro-Doppler Feature Extraction Using Integrated Sensing and Communication Signal: Algorithm Design and Testbed Evaluation

With the rapid application of unmanned aerial vehicles (UAVs) in urban areas, the identification and tracking of hovering UAVs have become critical challenges, significantly impacting the safety of aircraft take-off and landing operations. As a promising technology for 6G mobile systems, integrated sensing and communication (ISAC) can be used to detect high-mobility UAVs with a low deployment cost. The micro-Doppler signals from UAV rotors can be leveraged to address the detection of low-mobility and hovering UAVs using ISAC signals. However, determining whether the frame structure of the ISAC system can be used to identify UAVs, and how to accurately capture the weak rotor micro-Doppler signals of UAVs in complex environments, remain two challenging problems. This paper first proposes a novel frame structure for UAV micro-Doppler extraction and the representation of UAV micro-Doppler signals within the channel state information (CSI). Furthermore, to address complex environments and the interference caused by UAV body vibrations, the rotor micro-Doppler null space pursuit (rmD-NSP) algorithm and the feature extraction algorithm synchroextracting transform (SET) are designed to effectively separate UAV's rotor micro-Doppler signals and enhance their features in the spectrogram. Finally, both simulation and hardware testbed demonstrate that the proposed rmD-NSP algorithm enables the ISAC base station (BS) to accurately and completely extract UAV's rotor micro-Doppler signals. Within a 0.1s observation period, ISAC BS successfully captures eight rotations of the DJI M300 RTK UAV's rotor in urban environments. Compared to the existing AM-FM NSP and NSP signal decomposition algorithms, the integrity of the rotor micro-Doppler features is improved by 60%.

eess.SP↗

arXiv2024

Communication-Assisted Sensing in 6G Networks

Exploring the mutual benefit and reciprocity of sensing and communication (S\&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems. This paper investigates a novel communication-assisted sensing (CAS) system within 6G perceptive networks, where the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the estimated information to end-users. In such a CAS system, we first establish an optimal waveform design framework based on the rate-distortion (RD) and source-channel separation (SCT) theorems. After analyzing the relationships between the sensing distortion, coding rate, and communication channel capacity, we propose two distinct waveform design strategies in the scenario of target impulse response estimation. In the separated S\&C waveforms scheme, we equivalently transform the original problem into a power allocation problem and develop a low-complexity one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S\&C waveform. In the dual-functional waveform scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff in this scheme. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations.

eess.SP↗

arXiv2024

An Multi-resources Integration Empowered Task Offloading in Internet of Vehicles: From the Perspective of Wireless Interference

The task offloading technology plays a vital role in the Internet of Vehicles (IoV), by satisfying the diversified demands of the vehicles, such as the energy consumption and processing latency of the computing task. Different from the previous works, on the one hand, they ignored the wireless interference of communications among vehicle-to-vehicle (V2V), as well as between vehicles and roadside units (RSU); on the other hand, the available resources of parked vehicles on the roadside and other moving vehicles on the road are also ignored. In this paper, first of all, we adopt a truncated Gaussian distribution for modeling the vehicle moving speed, instead of the simplistic average speed models in prior studies. Then, with the consideration of wireless interference and effective communication duration existing in V2V and RSUs, we establish an analytical framework of the task offloading, characterized by the energy consumption and processing delay, by integrating computing resources of parked/moving vehicles and RSUs. Furthermore, inspired by the method of multi-agent deterministic policy gradient (MADDPG), we address a joint optimization of the energy consumption and processing delay of the computing task, while ensuring the load balancing of the resources. Finally, the simulations demonstrate the effectiveness and correctness of the proposed MADDPG. In particular, compared with the current popular methods of the task offloading, the MADDPG shows the best performance, in terms of convergence speed, energy consumption and processing delay.

cs.IT↗

arXiv2024

Integrated Sensing and Communication Driven Digital Twin for Intelligent Machine Network

Intelligent machines (IMs), including industrial machines, unmanned aerial vehicles (UAVs), and unmanned vehicles, etc., could perform effective cooperation in complex environment when they form IM network. The efficient environment sensing and communication are crucial for IM network, enabling the real-time and stable control of IMs. With the emergence of integrated sensing and communication (ISAC) technology, IM network is empowered with ubiquitous sensing capabilities, which is helpful in improving the efficiency of communication and sensing with the mutual benefit of them. However, the massive amount of sensing information brings challenges for the processing, storage and application of sensing information. In this article, ISAC driven digital twin (DT) is proposed for IM network, and the architecture and enabling technologies are revealed. ISAC driven DT structurally stores the sensing information, which is further applied to optimize communication, networking and control schemes of IMs, promoting the widespread applications of IMs.

cs.NI↗

arXiv2023

Joint Localization and Communication Enhancement in Uplink Integrated Sensing and Communications System with Clock Asynchronism

In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO) due to the clock asynchronism between UE and base station (BS). Our proposed scheme integrates the CSI enhancement into the multiple signal classification (MUSIC)-based AoA estimation and thus imposes no extra complexity on the ISAC system. We further exploit a MUSIC-based range estimation method and prove that it can suppress the time-varying TO-related phase terms. Exploiting the AoA and range estimation of UE, we can estimate the location of UE. Finally, we propose a joint CSI and data signals-based localization scheme that can coherently exploit the data and the CSI signals to improve the AoA and range estimation, which further enhances the single-base localization of UE. The extensive simulation results show that the enhanced CSI can achieve equivalent bit error rate performance to the minimum mean square error (MMSE) CSI estimator. The proposed joint CSI and data signals-based localization scheme can achieve decimeter-level localization accuracy despite the existing clock asynchronism and improve the localization mean square error (MSE) by about 8 dB compared with the maximum likelihood (ML)-based benchmark method.

cs.IT↗