arXiv subjects

Qixing Wang

Publications and source records attributed to Qixing Wang.

At least 19 recordsLinked to original sources

arXiv2026

DeepRT Engine: A Unified GPU-Parallel Ray-Tracing Framework with Hybrid SBR-IM Path Search for 6G Digital Twin Channel

Digital twin channel (DTC) aims to establish a real-time digital counterpart of physical wireless channels for reproducing and predicting site-specific propagation characteristics. As a high-precision channel computation method for realistic propagation scenarios, ray tracing (RT) serves as a key enabler for DTC construction. However, conventional RT suffers from high complexity under serial path-searching workflows. This letter proposes DeepRT Engine (DeepRT-E), a parallel RT acceleration architecture with a three-stage physically-inspired pipeline for real-time DTC construction. Firstly, DeepRT-E constructs a bounding volume hierarchy (BVH) to partition the scene and reduce redundant ray-surface intersections. Secondly, the shooting and bouncing rays (SBR) algorithm is executed through a ray-level parallel tracing framework to identify candidate surface sequences and prune the search space of the image method (IM). Finally, a parallel batched IM solver refines the retained candidates for accurate propagation-path recovery. Simulation results show that DeepRT-E reduces runtime by 96.3% and achieves a converged error of only 0.001 dB, outperforming Wireless InSite and Sionna in efficiency and accuracy.

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arXiv2026

Capacity Bounds and High-SNR Characterization for MIMO-OWC Channels Under Average-Power Constraint

This paper investigates the capacity of multipleinput multiple-output (MIMO) optical wireless communication (OWC) channels under a total average-power constraint. Since different nonnegative input vectors can be mapped to the same image vector and thus induce the same output distribution, we formulate a nonnegative basis pursuit (NN-BP) problem to identify the minimum-l1-norm input vector for each image vector. Based on the NN-BP characterization, we derive an equivalent expression for the channel capacity in terms of the image-vector distribution. We then establish computable lower and upper capacity bounds for both nT >= nR and nT < nR cases, and prove that the proposed bounds are asymptotically tight in the high signal-to-noise ratio (SNR) regime. Numerical results for indoor and outdoor OWC scenarios demonstrate that the proposed bounds improve upon existing ones and close the constant gap in the high-SNR regime.

cs.IT↗

arXiv2026

Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning

Ventilator decision support requires sequential decisions that track evolving physiology and disease trajectories while respecting safety boundaries and clinician specific tuning styles. Rule based approaches rarely generalize personalization, and end to end reinforcement learning or single large language model systems remain difficult to control and audit. We propose the Ventilator Decision Support System (VDSS), a human in the loop multi agent framework that coordinates modular decision components through contract driven structured interfaces and produces traceable evidence for review. VDSS performs online preference adaptation with a contextual bandit, updating clinician specific preferences from the final accepted decision at each adjustment cycle and using them to guide subsequent recommendations. Structured rejection feedback triggers targeted replanning to reduce unproductive iterations and improve interaction stability. Retrospective ICU trajectory replay with expert review indicates higher recommendation acceptability and fewer interaction rounds to reach an acceptable plan, supporting clinically deployable human AI collaboration.

cs.AI↗

arXiv2026

Secrecy Capacity Analysis and Beamforming Optimization for MIMO-VLC Wiretap Channels

This paper investigates a multiple-input multipleoutput (MIMO) visible light communication (VLC) wiretap channel consisting of a transmitter, a legitimate receiver, and an eavesdropper. The optical input is subject to both peakand average-intensity constraints. By applying the generalized entropy-power inequality to truncated exponential inputs, we derive a novel closed-form expression for the achievable secrecy rate for general MIMO VLC configurations. To enhance transmission confidentiality, a fully-connected beamforming scheme is proposed, along with a low-complexity sub-connected alternative. Although the resulting beamforming design problems are nonconvex, they are efficiently addressed by transforming them into a sequence of convex subproblems solvable via the successive convex approximation framework. Numerical results demonstrate that the proposed schemes achieve significant secrecy performance improvements compared with the benchmark scheme.

cs.IT↗

arXiv2026

Experimental Performance of Bidirectional Phase Coherent Transmission and Sensing for mmWave Cell-free Massive MIMO Systems with Reciprocity Calibration

Phase synchronization among distributed transmission reception points (TRPs) is a prerequisite for enabling coherent joint transmission and high-precision sensing in millimeter wave (mmWave) cell-free massive multiple-input and multiple-output (MIMO) systems. This paper proposes a bidirectional calibration scheme and a calibration coefficient estimation method for phase synchronization, and presents a calibration coefficient phase tracking method using unilateral uplink/downlink channel state information (CSI). Furthermore, this paper introduces the use of reciprocity calibration to eliminate non-ideal factors in sensing and leverages sensing results to achieve calibration coefficient phase tracking in dynamic scenarios, thus enabling bidirectional empowerment of both communication and sensing. Simulation results demonstrate that the proposed method can effectively implement reciprocal calibration with lower overhead, enabling coherent collaborative transmission, and resolving non-ideal factors to acquire lower sensing error in sensing applications. Experimental results show that, in the mmWave band, over-the-air (OTA) bidirectional calibration enables coherent collaborative transmission for both collaborative TRPs and collaborative user equipments (UEs), achieving beamforming gain and long-time coherent sensing capabilities.

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arXiv2025

Cooperative ISAC for LAE: Joint Trajectory Planning, Power allocation, and Dynamic Time Division

To enhance the performance of aerial-ground networks, this paper proposes an integrated sensing and communication (ISAC) framework for multi-UAV systems. In our model, ground base stations (BSs) cooperatively serve multiple unmanned aerial vehicles (UAVs), employing a dynamic time-division strategy where beam scanning for sensing precedes data communication in each time slot. To maximize the sum communication rate while satisfying a mission-level cumulative radar mutual information (MI) requirement, we jointly optimize the UAV trajectories, communication and sensing power allocation, and the time-division ratio. The resulting highly coupled non-convex optimization problem is efficiently solved using an alternating optimization (AO) and successive convex approximation (SCA) framework, which yields a non-decreasing objective sequence and convergence to a finite objective value under the adopted surrogate-based iterative procedure. Extensive simulation results demonstrate that our proposed joint design significantly outperforms benchmark schemes with static trajectories, partially optimized resources, or non-cooperative single-BS transmission. Furthermore, a comprehensive sensitivity analysis reveals the distinct mechanisms by which sensing thresholds and the number of UAVs influence resource allocation and spatial organization, highlighting the critical importance of dynamic, multi-dimensional resource management for effectively navigating the sensing-communication trade-off in low-altitude economies.

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arXiv2025

C-RAN Advanced: From a Network Cooperation Perspective

Future mobile networks in the sixth generation (6G) are poised for a paradigm shift from conventional communication services toward comprehensive information services, driving the evolution of radio access network (RAN) architectures toward enhanced cooperation, intelligence, and service orientation. Building upon the concept of centralized, collaborative, cloud, and clean RAN (C-RAN), this article proposes a novel cooperative, intelligent, and service-based RAN (CIS-RAN) architecture. Focusing on cooperation, CIS-RAN extends the traditional cooperative communication paradigm by further integrating cooperative sensing and cooperative artificial intelligence (AI). To improve both performance and effectiveness across diverse application scenarios, CIS-RAN enhances network cooperation throughout the entire process of acquisition, transmission, and processing, thereby enabling efficient information acquisition, diverse cooperative interactions, and intelligent fusion decision-making. Key technologies are discussed, with network cooperative multiple-input multiple-output (MIMO) examined as a case study, demonstrating superior performance over traditional architectures, as demonstrated by numerical results. Future research directions are outlined, emphasizing the continued exploration and advancement of the CIS-RAN architecture, particularly in enhancing network cooperation.

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arXiv2025

Cooperative Sensing and Communication Beamforming Design for Low-Altitude Economy

To empower the low-altitude economy with high-accuracy sensing and high-rate communication, this paper proposes a cooperative integrated sensing and communication (ISAC) framework for aerial-ground networks. In the proposed system, the ground base stations (BSs) cooperatively serve the unmanned aerial vehicles (UAVs), which are equipped for either joint communication and sensing or sensing-only operations. The BSs employ coordinated beamforming to simultaneously transmit communication and sensing signals, while the UAVs execute their missions. To maximize the weighted sum rate under the sensing signal-to-interference-plus-noise ratio (SINR) constraints, we jointly optimize the transmit beamforming, receive filtering, and UAV trajectory. The resulting non-convex problem is solved using an alternating optimization framework incorporating semidefinite relaxation (SDR) and successive convex approximation (SCA). Simulation results demonstrate that the proposed joint design achieves higher communication throughput while ensuring required sensing robustness. Additionally, the sensing SINR threshold and the UAV altitude have a significant impact on the trajectory design, highlighting the necessity of adaptive deployment strategies in practical applications.

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arXiv2025

Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G

Next generation cellular deployments are expected to exploit the 6-24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies render the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models invalid, giving rise to new features such as near-field propagation and spatial non-stationarity (SNS). Despite extensive prior research, incorporating these new features within the standardized channel modeling framework remains an open issue. To address this, this paper presents a channel modeling framework for XL-MIMO systems that incorporates both near-field and SNS features, adopted by 3GPP. For the near-field propagation feature, the framework models the distances from the base station (BS) and user equipment to the spherical-wave sources associated with clusters. These distances are used to characterize element-wise variations of path parameters, such as nonlinear changes in phase and angle. To capture the effect of SNS at the BS side, a stochastic-based approach is proposed to model SNS caused by incomplete scattering, by establishing power attenuation factors from visibility probability and visibility region to characterize antenna element-wise path power variation. In addition, a physical blocker-based approach is introduced to model SNS effects caused by partial blockage. Finally, a simulation framework for near-field and SNS is developed within the structure of the existing 3GPP channel model. Performance evaluations demonstrate that the near-field model captures higher channel capacity potential compared to the far-field model. Coupling loss results indicate that SNS leads to more pronounced propagation fading relative to the spatial stationary model.

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arXiv2025

Empirical Study on Near-Field and Spatial Non-Stationarity Modeling for THz XL-MIMO Channel in Indoor Scenario

Terahertz (THz) extremely large-scale MIMO (XL-MIMO) is considered a key enabling technology for 6G and beyond due to its advantages such as wide bandwidth and high beam gain. As the frequency and array size increase, users are more likely to fall within the near-field (NF) region, where the far-field plane-wave assumption no longer holds. This also introduces spatial non-stationarity (SnS), as different antenna elements observe distinct multipath characteristics. Therefore, this paper proposes a THz XL-MIMO channel model that accounts for both NF propagation and SnS, validated using channel measurement data. In this work, we first conduct THz XL-MIMO channel measurements at 100 GHz and 132 GHz using 301- and 531-element ULAs in indoor environments, revealing pronounced NF effects characterized by nonlinear inter-element phase variations, as well as element-dependent delay and angle shifts. Moreover, the SnS phenomenon is observed, arising not only from blockage but also from inconsistent reflection or scattering. Based on these observations, a hybrid NF channel modeling approach combining the scatterer-excited point-source model and the specular reflection model is proposed to capture nonlinear phase variation. For SnS modeling, amplitude attenuation factors (AAFs) are introduced to characterize the continuous variation of path power across the array. By analyzing the statistical distribution and spatial autocorrelation properties of AAFs, a statistical rank-matching-based method is proposed for their generation. Finally, the model is validated using measured data. Evaluation across metrics such as entropy capacity, condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread confirms that the proposed model closely aligns with measurements and effectively characterizes the essential features of THz XL-MIMO channels.

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arXiv2025

Performance Analysis of Cooperative Integrated Sensing and Communications for 6G Networks

In this work, we aim to effectively characterize the performance of cooperative integrated sensing and communication (ISAC) networks and to reveal how performance metrics relate to network parameters. To this end, we introduce a generalized stochastic geometry framework to model the cooperative ISAC networks, which approximates the spatial randomness of the network deployment. Based on this framework, we derive analytical expressions for key performance metrics in both communication and sensing domains, with a particular focus on communication coverage probability and radar information rate. The analytical expressions derived explicitly highlight how performance metrics depend on network parameters, thereby offering valuable insights into the deployment and design of cooperative ISAC networks. In the end, we validate the theoretical performance analysis through Monte Carlo simulation results. Our results demonstrate that increasing the number of cooperative base stations (BSs) significantly improves both metrics, while increasing the BS deployment density has a limited impact on communication coverage probability but substantially enhances the radar information rate. Additionally, increasing the number of transmit antennas is effective when the total number of transmit antennas is relatively small. The incremental performance gain reduces with the increase of the number of transmit antennas, suggesting that indiscriminately increasing antennas is not an efficient strategy to improve the performance of the system in cooperative ISAC networks.

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arXiv2025

High-Resolution Multipath Angle Estimation Based on Power-Angle-Delay Profile for Directional Scanning Sounding

Directional scanning sounding (DSS) has become widely adopted for high-frequency channel measurements because it effectively compensates for severe path loss. However, the resolution of existing multipath component (MPC) angle estimation methods is constrained by the DSS angle sampling interval. Therefore, this communication proposes a high-resolution MPC angle estimation method based on power-angle-delay profile (PADP) for DSS. By exploiting the mapping relationship between the power difference of adjacent angles in the PADP and MPC offset angle, the resolution of MPC angle estimation is refined, significantly enhancing the accuracy of MPC angle and amplitude estimation without increasing measurement complexity. Numerical simulation results demonstrate that the proposed method reduces the mean squared estimation errors of angle and amplitude by one order of magnitude compared to traditional omnidirectional synthesis methods. Furthermore, the estimation errors approach the Cram\'er-Rao Lower Bounds (CRLBs) derived for wideband DSS, thereby validating its superior performance in MPC angle and amplitude estimation. Finally, experiments conducted in an indoor scenario at 37.5 GHz validate the excellent performance of the proposed method in practical applications.

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arXiv2025

AI-driven 6G Air Interface: Technical Usage Scenarios and Balanced Design Methodology

This paper systematically analyzes the typical application scenarios and key technical challenges of AI in 6G air interface transmission, covering important areas such as performance enhancement of single functional modules, joint optimization of multiple functional modules, and low-complexity solutions to complex mathematical problems. Innovatively, a three-dimensional joint optimization design criterion is proposed, which comprehensively considers AI capability, quality, and cost. By maximizing the ratio of multi-scenario communication capability to comprehensive cost, a triangular equilibrium is achieved, effectively addressing the lack of consideration for quality and cost dimensions in existing design criteria. The effectiveness of the proposed method is validated through multiple design examples, and the technical pathways and challenges for air interface AI standardization are thoroughly discussed. This provides significant references for the theoretical research and engineering practice of 6G air interface AI technology.

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arXiv2025

Movable Antenna-Aided Cooperative ISAC Network with Time Synchronization error and Imperfect CSI

Cooperative-integrated sensing and communication (C-ISAC) networks have emerged as promising solutions for communication and target sensing. However, imperfect channel state information (CSI) estimation and time synchronization (TS) errors degrade performance, affecting communication and sensing accuracy. This paper addresses these challenges {by employing} {movable antennas} (MAs) to enhance C-ISAC robustness. We analyze the impact of CSI errors on achievable rates and introduce a hybrid Cramer-Rao lower bound (HCRLB) to evaluate the effect of TS errors on target localization accuracy. Based on these models, we derive the worst-case achievable rate and sensing precision under such errors. We optimize cooperative beamforming, {base station (BS)} selection factor and MA position to minimize power consumption while ensuring accuracy. {We then propose a} constrained deep reinforcement learning (C-DRL) approach to solve this non-convex optimization problem, using a modified deep deterministic policy gradient (DDPG) algorithm with a Wolpertinger architecture for efficient training under complex constraints. {Simulation results show that the proposed method significantly improves system robustness against CSI and TS errors, where robustness mean reliable data transmission under poor channel conditions.} These findings demonstrate the potential of MA technology to reduce power consumption in imperfect CSI and TS environments.

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arXiv2024

BUPTCMCC-6G-DataAI+: A generative channel dataset for 6G AI air interface research

In September 2024, Beijing University of Posts and Telecommunications and China Mobile Communications Group jointly releases a channel dataset for the sixth generation (6G) mobile communications, named BUPTCMCC-6G-DataAI+. BUPTCMCC-6G-DataAI+ is the update version of BUPTCMCC-6G-DataAI, which is already published in June 2023, aiming at extending 6G new technologies, frequency bands, and applications. BUPTCMCC-6G-DataAI+ provides deterministic data covering new mid-bands, millimeter wave (mmWave), and terahertz (THz), supports the features of XL-MIMO near-field, high mobility and provides multiple 6G scenarios such as reconfigurable intelligent surface (RIS) and industrial Internet. Configured with customized features according to different user needs, BUPTCMCC-6G-DataAI+ can adaptively generate scalable large-scale or small-scale parameters, providing data support for 6G research and development, and standardization.

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arXiv2024

Augmenting Channel Simulator and Semi- Supervised Learning for Efficient Indoor Positioning

This work aims to tackle the labor-intensive and resource-consuming task of indoor positioning by proposing an efficient approach. The proposed approach involves the introduction of a semi-supervised learning (SSL) with a biased teacher (SSLB) algorithm, which effectively utilizes both labeled and unlabeled channel data. To reduce measurement expenses, unlabeled data is generated using an updated channel simulator (UCHS), and then weighted by adaptive confidence values to simplify the tuning of hyperparameters. Simulation results demonstrate that the proposed strategy achieves superior performance while minimizing measurement overhead and training expense compared to existing benchmarks, offering a valuable and practical solution for indoor positioning.

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arXiv2024

An Approximate Wave-Number Domain Expression for Near-Field XL-array Channel

As Extremely large-scale array (XL-array) technology advances and carrier frequency rises, the near-field effects in communication are intensifying. In near-field conditions, channels exhibit a diffusion phenomenon in the angular domain, existing research indicates that this phenomenon can be leveraged for efficient parameter estimation and beam training. However, the channel model in angular domain lacks closed-form analysis, making the time complexity of the corresponding algorithm high. To address this issue, this paper analyzes the near-field diffusion effect in the wave-number domain, where the wave-number domain can be viewed as the continuous form of the angular domain. A closed-form approximate wave-number domain expression is proposed, based on the Principle of Stationary Phase. Subsequently, we derive a simplified expression for the case where the user distance is much larger than the array aperture, which is more concise. Subsequently, we verify the accuracy of the proposed approximate expression through simulations and demonstrate its effectiveness using a beam training example. Results indicate that the beam training scheme, improved by the wave-number domain approximation model, can effectively estimate near-field user parameters and perform beam training using far-field DFT codebooks. Moreover, its performance surpasses that of existing DFT codebook-based beam training methods.

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arXiv2024

A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation

Multi-modality magnetic resonance imaging(MRI) data facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC). The lack of publicly available, comprehensive datasets limits advancements in diagnosis, treatment planning, and the development of machine learning algorithms for NPC. Addressing this critical need, we introduce the first comprehensive NPC MRI dataset, encompassing MR axial imaging of 277 primary NPC patients. This dataset includes T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences, totaling 831 scans. In addition to the corresponding clinical data, manually annotated and labeled segmentations by experienced radiologists offer high-quality data resources from untreated primary NPC.

eess.IV↗