arXiv subjects

Shijian Gao

Publications and source records attributed to Shijian Gao.

At least 37 records · Page 2Linked to original sources

arXiv2026

Learn to Access and Backhaul the Sky: Multi-Scale Radio Map Guided Multi-UAV Cooperation

Driven by the emerging low-altitude economy, uncrewed aerial vehicle (UAV) swarms offer flexible integrated air-ground access and backhaul. However, providing seamless connectivity is difficult due to the interdependent dynamics of user mobility and building blockages in these 3D scenarios. These factors create rapidly shifting bottlenecks in end-to-end paths. Furthermore, the multi-dimensional nature of joint control limits the effectiveness of traditional heuristics. To address these challenges, a \textbf{\underline{M}}ulti-Scale \textbf{\underline{R}}adio \textbf{\underline{M}}ap-\textbf{\underline{G}}uided (MRMG) framework is proposed. The MRMG framework handles heterogeneous dynamics by integrating three distinct levels of radio information: global-level maps provide regional coverage insights, local-level maps capture neighborhood-scale service conditions, and link-level maps characterize high-resolution channel features. This design effectively decouples macro-movement from micro-link adaptation. To yield long-term performance improvements, A multi-agent reinforcement learning (MARL) controller learns cooperative policies for UAV movement, next-hop selection, and transmit-power control. Simulation results show that the MRMG framework not only improves network throughput but also significantly bolsters cell-edge service, nearly doubling the 5th-percentile user rate.

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arXiv2026

Sequential Task Assignment and Resource Allocation in V2X-Enabled Mobile Edge Computing

Nowadays, the convergence of mobile edge computing (MEC) and vehicular networks has emerged as a vital enabler for the ever-increasing intelligent onboard applications. This paper proposes a multi-tier task offloading mechanism for MEC-enabled vehicular networks leveraging vehicle-to-everything (V2X) communications. The study focuses on applications with sequential subtasks and explores the collaboration of two tiers. In the Vehicle Tier, the requesting vehicle (RV)-service vehicle (SV) matching scheme and the inter-vehicle collaborative computation are studied, with joint optimization of task offloading decision, communication, and computing resource allocation to minimize energy consumption while satisfying delay requirements. In the Roadside Unit (RSU) Tier, collaboration among RSUs is investigated to further address multi-access issues of uplink subchannels and computing resources for serving unmatched RVs. To tackle this intricate problem, a layered optimization framework is first proposed to obtain task offloading decisions and optimal continuous resource allocation, after which a subchannel allocation scheme is designed to recover the discrete solution with low complexity. Extensive experiments are conducted to demonstrate that the proposed method reduces average energy consumption by at least 15% compared with recent utility maximization and energy cost minimization benchmarks under varying task delay requirements and vehicle scales.

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arXiv2026

Dynamic Task and Resource Scheduling Towards Green Space-Air-Ground-Sea Integrated Network

In the context of 6G ubiquitous connectivity, the space-air-ground-sea integrated network (SAGSIN) emerges as a new paradigm to provide critical services for resource-limited ocean environments. To realize this paradigm efficiently, we propose an innovative dynamic task and resource scheduling approach for green SAGSIN that delivers computing support for vessels while minimizing overall task execution delay. To address the challenge of multi-layer task scheduling, a layer-wise task offloading algorithm is developed specifically for SAGSIN. It adapts to real-time, multi-dimensional system dynamics and integrates an anticipatory handover strategy that adaptively controls the amount of data offloaded to the satellite, thereby preventing post-handover congestion while improving satellite resource utilization. Furthermore, the bandwidth allocation of uncrewed aerial vehicles and base station, UAV trajectories, and computing resource allocation are jointly optimized to enhance connectivity among low-altitude devices and facilitate demand-driven resource allocation for green network development. Simulation results verify that the proposed method better adapts to dynamic system resources and achieves at least a 23% reduction in average task delay compared with benchmarks.

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arXiv2026

Scalable Multimodal Beam Alignment in V2X: An Anti-Imbalance Graph Learning Approach

Efficient beam alignment is fundamental to high-throughput and reliable connectivity in Vehicle-to-Everything (V2X) systems. However, conventional beam management in dynamic vehicular topologies incurs prohibitive alignment overhead and struggles to maintain robust links under rapid mobility. To overcome these challenges, this paper proposes a distributed multimodal graph beam alignment (GBA) framework. The core innovation lies in leveraging onboard multimodal sensing data to predict implicit feedback while employing graph neural networks to coordinate multi-user alignment, thereby jointly enhancing scalability and drastically reducing overhead. The architecture adopts a dual-network design with GBA-RSU and GBA-Vehicle units, optimized through a hybrid strategy of centralized learning and federated learning (FL) to balance global performance with local privacy. Furthermore, a dedicated data augmentation (DA) scheme is introduced to address multimodal data imbalance issues in vehicular networks. Negative augmentation applies dominant modality dropout to bolster robustness, while positive augmentation generates underrepresented samples to mitigate label imbalance. Numerical results demonstrate that GBA maintains a competitive sum rate on par with high-resolution codebook-based feedback yet reduces beam alignment overhead by over 90\% and scales efficiently in mobile scenarios. Notably, integrating DA enables GBA to consistently outperform state-of-the-art FL-based alignment benchmarks, with particularly pronounced gains under severe label and modality imbalance, establishing a practical solution for V2X beam management.

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arXiv2026

Score-Based Conditional Flow Models for MIMO Receiver Design with Superimposed Pilots

Accurate channel state information (CSI) is vital for multiple-input multiple-output (MIMO) systems. However, superimposed pilots (SIP), which reduce overhead, introduce severe pilot contamination and data interference, complicating joint channel estimation and data detection. This paper proposes a conditional flow matching receiver (CFM-Rx), an unsupervised generative framework that learns directly from received signals, eliminating the need for labeled data and improving adaptability across diverse system settings. By leveraging flow-based generative modeling, CFM-Rx enables deterministic, low-latency inference and exploits model invertibility to capture the bidirectional nature of signal propagation. This framework unifies flow matching with score-based diffusion modeling via a moment-consistent ordinary differential equation (ODE), replacing stochastic differential equation (SDE) sampling with a deterministic and efficient process. Furthermore, it integrates receiver-side priors to ensure stable, data-consistent inference. Extensive simulation results across various MIMO configurations demonstrate that CFM-Rx consistently outperforms conventional estimators and state-of-the-art data-driven receivers, achieving notable gains in channel estimation accuracy and symbol detection robustness, particularly under severe pilot contamination.

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arXiv2026

Transfer to Sky: Unveil Low-Altitude Route-Level Radio Maps via Ground Crowdsourced Data

The expansion of the low-altitude economy is contingent on reliable cellular connectivity for unmanned aerial vehicles (UAVs). A key challenge in pre-flight planning is predicting communication link quality along proposed and pre-defined routes, a task hampered by sparse measurements that render existing radio map methods ineffective. This paper introduces a transfer learning framework for high-fidelity route-level radio map prediction. Our key insight is to leverage abundant crowdsourced ground signals as auxiliary supervision. To bridge the significant domain gap between ground and aerial data and address spatial sparsity, our framework learns general propagation priors from simulation, performs adversarial alignment of the feature spaces, and is fine-tuned on limited real UAV measurements. Extensive experiments on a real-world dataset from Meituan show that our method achieves over 50% higher accuracy in predicting Route RSRP compared to state-of-the-art baselines.

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arXiv2026

Multi-Tier UAV Edge Computing Towards Long-Term Energy Stability for Low Altitude Networks

The agile mobility of Unmanned Aerial Vehicles (UAVs) makes them ideal for low-altitude edge computing. This paper proposes a novel multi-tier UAV edge computing system where lightweight Low-Tier UAVs (L-UAVs) function as edge servers for vehicle users, supported by a powerful High-Tier UAV (H-UAV) acting as a backup server. The objective is to minimize task execution delays while ensuring the long-term energy stability of the L-UAVs, despite unknown future system states. To this end, the problem is decoupled using Lyapunov optimization, which adaptively balances the priorities of task delays and L-UAV energy cost based on their real-time energy states. An efficient vehicle to L-UAV matching scheme is designed, and the joint optimization problem for task assignment, computing resource allocation, and trajectory control of L-UAVs and H-UAV is then solved via a Block Coordinate Descent (BCD) algorithm. Simulation results demonstrate a reduction in L-UAV transmission energy of over 26% and superior L-UAV energy stability compared to existing benchmarks.

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arXiv2026

Synesthesia of Machines (SoM)-Aided Online FDD Precoding via Heterogeneous Multi-Modal Sensing: A Vertical Federated Learning Approach

This paper investigates a heterogeneous multi-vehicle, multi-modal sensing (H-MVMM) aided online precoding problem. The proposed H-MVMM scheme utilizes a vertical federated learning (VFL) framework to minimize pilot sequence length and optimize the sum rate. This offers a promising solution for reducing latency in frequency division duplexing systems. To achieve this, three preprocessing modules are designed to transform raw sensory data into informative representations relevant to precoding. The approach effectively addresses local data heterogeneity arising from diverse on-board sensor configurations through a well-structured VFL training procedure. Additionally, a label-free online model updating strategy is introduced, enabling the H-MVMM scheme to adapt its weights flexibly. This strategy features a pseudo downlink channel state information label simulator (PCSI-Simulator), which is trained using a semi-supervised learning (SSL) approach alongside an online loss function. Numerical results show that the proposed method can closely approximate the performance of traditional optimization techniques with perfect channel state information, achieving a significant 90.6\% reduction in pilot sequence length.

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arXiv2026

Tiny-WiFo: A Lightweight Wireless Foundation Model for Channel Prediction via Multi-Component Adaptive Knowledge Distillation

The massive scale of Wireless Foundation Models (FMs) hinders their real-time deployment on edge devices. This letter moves beyond standard knowledge distillation by introducing a novel Multi-Component Adaptive Knowledge Distillation (MCAKD) framework. Key innovations include a Cross-Attention-Based Knowledge Selection (CA-KS) module that selectively identifies critical features from the teacher model, and an Autonomous Learning-Passive Learning (AL-PL) strategy that balances knowledge transfer with independent learning to achieve high training efficiency at a manageable computational cost. When applied to the WiFo FM, the distilled Tiny-WiFo model, with only 5.5M parameters, achieves a 1.6 ms inference time while retaining over 98% of WiFo's performance and its crucial zero-shot generalization capability, making real-time FM deployment viable.

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arXiv2026

WiFo-E: A Scalable Wireless Foundation Model for End-to-End FDD Precoding in Communication Networks

Accurate precoding in massive multiple-input multiple-output (MIMO) frequency-division duplexing (FDD) systems relies on efficient channel state information (CSI) acquisition. End-to-end learning frameworks improve performance by jointly optimizing this process, but they lack scalability and fail to generalize across different system configurations, such as varying numbers of antennas and users. To overcome this limitation, we introduce WiFo-E, a wireless foundation model designed for scalable end-to-end precoding. WiFo-E employs multi-task pretraining on a diverse set of configurations to learn transferable representations of underlying wireless principles. Central to the model is a sparse Mixture-of-Experts (MoE) Transformer architecture, which mitigates task interference and enhances training efficiency by activating specialized parameter subsets adaptively. Extensive simulations demonstrate that WiFo-E outperforms conventional per-configuration training and shows strong generalization to unseen system configurations, providing a flexible and efficient foundation for adaptive massive MIMO precoding.

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arXiv2026

WiFo-MUD: Wireless Foundation Model for Heterogeneous Multi-User Demodulator

Multi-user signal demodulation is critical to wireless communications, directly impacting transmission reliability and efficiency. However, existing demodulators underperform in generic multi-user environments: classical demodulators struggle to balance accuracy and complexity, while deep learning-based methods lack adaptability under heterogeneous configurations. Although diffusion models have been introduced for demodulation, their flexibility remains limited for practical use. To address these issues, this work proposes WiFo-MUD, a universal diffusion-based foundation model for multi-user demodulation. The model aligns inter-user signal-to-noise ratio imbalance and performs conditional denoising via a customized backbone. Furthermore, a communication-aware consistency distillation method and a dynamic user-grouping strategy are devised to enhance inference. WiFo-MUD achieves state-of-the-art results on large-scale heterogeneous datasets, demonstrating efficient inference and strong generalization across varying system configurations.

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arXiv2025

Relaying Signal When Monitoring Traffic: Double Use of Aerial Vehicles Towards Intelligent Low-Altitude Networking

In intelligent low-altitude networks, integrating monitoring tasks into communication unmanned aerial vehicles (UAVs) can consume resources and increase handoff latency for communication links. To address this challenge, we propose a strategy that enables a "double use" of UAVs, unifying the monitoring and relay handoff functions into a single, efficient process. Our scheme, guided by an integrated sensing and communication framework, coordinates these multi-role UAVs through a proactive handoff network that fuses multi-view sensory data from aerial and ground vehicles. A lightweight vehicle inspection module and a two-stage training procedure are developed to ensure monitoring accuracy and collaborative efficiency. Simulation results demonstrate the effectiveness of this integrated approach: it reduces communication outage probability by nearly 10% at a 200 Mbps requirement without compromising monitoring performance and maintains high resilience (86% achievable rate) even in the absence of multiple UAVs, outperforming traditional ground-based handoff schemes. Our code is available at the https://github.com/Jiahui-L/UAP.

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arXiv2025

Denoising and Augmentation: A Dual Use of Diffusion Model for Enhanced CSI Recovery

This letter introduces a dual application of denoising diffusion probabilistic model (DDPM)-based channel estimation algorithm integrating data denoising and augmentation. Denoising addresses the severe noise in raw signals at pilot locations, which can impair channel estimation accuracy. An unsupervised structure is proposed to clean field data without prior knowledge of pure channel information. Data augmentation is crucial due to the data-intensive nature of training deep learning (DL) networks for channel state information (CSI) estimation. The network generates new channel data by adjusting reverse steps, enriching the training dataset. To manage varying signal-to-noise ratios (SNRs) in communication data, a piecewise forward strategy is proposed to enhance the DDPM convergence precision. The link-level simulations indicate that the proposed scheme achieves a superior tradeoff between precision and computational cost compared to existing benchmarks.

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arXiv2025

Robust MIMO Semantic Communication with Imperfect CSI via Knowledge Distillation

Semantic communication (SemComm) has emerged as a new communication paradigm. To enhance efficiency, multiple-input-multiple-output (MIMO) technology has been further integrated into SemComm systems. However, existing MIMO SemComm systems assume perfect channel matrix estimation for channel-adaptive joint source-channel coding, which is impractical due to hardware and pilot overhead constraints. In this paper, we propose a semantic image transmission system with channel matrix and channel noise adaptation, named HANA-JSCC, to cope with channel estimation errors in MIMO systems. We propose a channel matrix adaptor that collaborates with the channel codec to adapt to misaligned channel state information, thereby mitigating the impact of estimation errors. Since the relationship between the estimated channel matrix and true channel matrix is ill-posed (one-to-many), we further introduce a two-stage training strategy with knowledge distillation to overcome the convergence difficulties caused by the ill-posed problem. Comparing with the state-of-the-art benchmarks, HANA-JSCC achieves $0.40\sim0.54$dB higher average performance across various noise and estimation error levels in various datasets.

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arXiv2025

Bayesian-Driven Graph Reasoning for Active Radio Map Construction

With the emergence of the low-altitude economy, radio maps have become essential for ensuring reliable wireless connectivity to aerial platforms. Autonomous aerial agents are commonly deployed for data collection using waypoint-based navigation; however, their limited battery capacity significantly constrains coverage and efficiency. To address this, we propose an uncertainty-aware radio map (URAM) reconstruction framework that explicitly leverages graph-based reasoning tailored for waypoint navigation. Our approach integrates two key deep learning components: (1) a Bayesian neural network that estimates spatial uncertainty in real time, and (2) an attention-based reinforcement learning policy that performs global reasoning over a probabilistic roadmap, using uncertainty estimates to plan informative and energy-efficient trajectories. This graph-based reasoning enables intelligent, non-myopic trajectory planning, guiding agents toward the most informative regions while satisfying safety constraints. Experimental results show that URAM improves reconstruction accuracy by up to 34% over existing baselines.

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arXiv2025

Multi-Tier UAV Edge Computing for Low Altitude Networks Towards Long-Term Energy Stability

This paper presents a novel multi-tier UAV-assisted edge computing system designed for low-altitude networks. The system comprises vehicle users, lightweight Low-Tier UAVs (L-UAVs), and High-Tier UAV (H-UAV). L-UAVs function as small-scale edge servers positioned closer to vehicle users, while the H-UAV, equipped with more powerful server and larger-capacity battery, serves as mobile backup server to address the limitations in endurance and computing resources of L-UAVs. The primary objective is to minimize task execution delays while ensuring long-term energy stability for L-UAVs. To address this challenge, the problem is first decoupled into a series of deterministic problems for each time slot using Lyapunov optimization. The priorities of task delay and energy consumption for L-UAVs are adaptively adjusted based on real-time energy status. The optimization tasks include assignment of tasks, allocation of computing resources, and trajectory planning for both L-UAVs and H-UAV. Simulation results demonstrate that the proposed approach achieves a reduction of at least 26% in transmission energy for L-UAVs and exhibits superior energy stability compared to existing benchmarks.

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arXiv2025

Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach

As vehicle intelligence advances, multi-modal sensing-aided communication emerges as a key enabler for reliable Vehicle-to-Everything (V2X) connectivity through precise environmental characterization. As centralized learning may suffer from data privacy, model heterogeneity and communication overhead issues, federated learning (FL) has been introduced to support V2X. However, the practical deployment of FL faces critical challenges: model performance degradation from label imbalance across vehicles and training instability induced by modality disparities in sensor-equipped agents. To overcome these limitations, we propose a generative FL approach for beam selection (GFL4BS). Our solution features two core innovations: 1) An adaptive zero-shot multi-modal generator coupled with spectral-regularized loss functions to enhance the expressiveness of synthetic data compensating for both label scarcity and missing modalities; 2) A hybrid training paradigm integrating feature fusion with decentralized optimization to ensure training resilience while minimizing communication costs. Experimental evaluations demonstrate significant improvements over baselines achieving 16.2% higher accuracy than the current state-of-the-art under severe label imbalance conditions while maintaining over 70% successful rate even when two agents lack both LiDAR and RGB camera inputs.

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arXiv2025

WiFo-CF: Wireless Foundation Model for CSI Feedback

Deep learning-based channel state information (CSI) feedback schemes demonstrate strong compression capabilities but are typically constrained to fixed system configurations, limiting their generalization and flexibility. To address this challenge, WiFo-CF, a novel wireless foundation model tailored for CSI feedback, is proposed, uniquely accommodating heterogeneous configurations such as varying channel dimensions, feedback rates, and data distributions within a unified framework through its key innovations: (1) a multi-user, multi-rate self-supervised pre-training strategy; and (2) a Mixture of Shared and Routed Expert (S-R MoE) architecture. Supporting the large-scale pre-training of WiFo-CF is the first heterogeneous channel feedback dataset, whose diverse patterns enable the model to achieve superior performance on both in-distribution and out-of-distribution data across simulated and real-world scenarios. Furthermore, the learned representations effectively facilitate adaptation to downstream tasks such as CSI-based indoor localization, validating WiFo-CF's scalability and deployment potential.

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