arXiv subjects

Xianhua Yu

Publications and source records attributed to Xianhua Yu.

9 recordsLinked to original sources

arXiv2026

Movable-Antenna Empowered Backscatter ISAC: Toward Geometry-Adaptive, Low-Power Networks

Backscatter-based integrated sensing and communication (B-ISAC) elevates passive tags into information-bearing scatterers, offering an ultra-low-power path toward dual-function wireless systems. However, this promise is fundamentally undermined by a cascaded backscattering link that suffers from severe double fading and is exquisitely sensitive to geometric misalignment. This article tackles this geometric bottleneck by integrating movable antenna systems (MAS) at the transceiver side. MAS provides real-time, controllable spatial degrees of freedom through sub-wavelength antenna repositioning, enabling active reconfiguration of the cascaded channel without modifying passive tags or consuming additional spectrum. We position this solution within a unified ISAC-backscatter communication-B-ISAC evolution, describe the resulting MAS-assisted B-ISAC architecture and operating principles, and demonstrate its system-level gains through comparative analysis and numerical results. Finally, we showcase the potential of this geometry-adaptive paradigm across key IoT application scenarios, pointing toward future motion-aware wireless networks.

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arXiv2026

Dual-Mapping Sparse Vector Transmission for Short Packet URLLC

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation communication systems. In this paper, a dual-mapping SVC (DM-SVC) based short packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme lies in mapping the transmitted information bits onto sparse vectors via block and single-element sparse mappings. The block sparse mapping pattern is able to concentrate the transmit power in a small number of non-zero blocks thus improving the decoding accuracy, while the single-element sparse mapping pattern ensures that the code length does not increase dramatically with the number of transmitted information bits. At the receiver, a two-stage decoding algorithm is proposed to sequentially identify non-zero block indexes and single-element non-zero indexes. Extensive simulation results verify that proposed DM-SVC scheme outperforms the existing SVC schemes in terms of block error rate and spectral efficiency.

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arXiv2025

Adaptive Phase Shift Information Compression for IRS Systems: A Prompt Conditioned Variable Rate Framework

Intelligent reflecting surfaces (IRSs) have become a vital technology for improving the spectrum and energy efficiency of forthcoming wireless networks. Nevertheless, practical implementation is obstructed by the excessive overhead associated with the frequent transmission of phase shift information (PSI) over bandwidth-constrained control lines. Current deep learning-based compression methods mitigate this problem but are constrained by elevated decoder complexity, inadequate flexibility to dynamic channels, and static compression ratios. This research presents a prompt-conditioned PSI compression system that integrates prompt learning inspired by large models into the PSI compression process to address these difficulties. A hybrid prompt technique that integrates soft prompt concatenation with feature-wise linear modulation (FiLM) facilitates adaptive encoding across diverse signal-to-noise ratios (SNRs), fading kinds, and compression ratios. Furthermore, a variable rate technique incorporates the compression ratio into the prompt embeddings through latent masking, enabling a singular model to adeptly balance reconstruction accuracy. Additionally, a lightweight depthwise convolutional gating (DWCG) decoder facilitates precise feature reconstruction with minimal complexity. Comprehensive simulations indicate that the proposed framework significantly reduces NMSE compared to traditional autoencoder baselines, while ensuring robustness across various channel circumstances and accommodating variable compression ratios within a single model. These findings underscore the framework's promise as a scalable and efficient solution for real-time IRS control in next-generation wireless networks.

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arXiv2025

Revisiting Wireless-Powered MEC: A Cooperative Energy Recycling Framework for Task-Energy Co-Design

Cooperative energy recycling (CER) offers a new way to boost energy utilization in wireless-powered multi-access edge computing (MEC) networks, yet its integration with computation-communication co-design remains underexplored. This paper proposes a CER-enabled MEC framework that maximizes the minimum computable data among users under energy causality, latency, and power constraints. The intractable problem is reformulated into a convex form through relaxation, maximum ratio combining, and variable substitution, and closed-form solutions are derived via Lagrangian duality and alternating optimization, offering analytical insights. Simulation results verify that the proposed CER mechanism markedly increases total computable data while maintaining equitable performance across heterogeneous users.

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arXiv2025

Fairness-Aware Computation Offloading in Wireless-Powered MEC Systems with Cooperative Energy Recycling

In this paper, cooperative energy recycling (CER) is investigated in wireless-powered mobile edge computing systems. Unlike conventional architectures that rely solely on a dedicated power source, wireless sensors are additionally enabled to recycle energy from peer transmissions. To evaluate system performance, a joint computation optimization problem is formulated that integrates local computing and computation offloading, under an alpha-fairness objective that balances total computable data and user fairness while satisfying energy, latency, and task size constraints. Due to the inherent non-convexity introduced by coupled resource variables and fairness regularization, a variable-substitution technique is employed to transform the problem into a convex structure, which is then efficiently solved using Lagrangian duality and alternating optimization. To characterize the fairness-efficiency tradeoff, closed-form solutions are derived for three representative regimes: zero fairness, common fairness, and max-min fairness, each offering distinct system-level insights. Numerical results validate the effectiveness of the proposed CER-enabled framework, demonstrating significant gains in throughput and adaptability over benchmark schemes. The tunable alpha fairness mechanism provides flexible control over performance-fairness trade-offs across diverse scenarios.

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arXiv2025

Phase Shift Information Compression in IRS-aided Wireless Systems: Challenges and Opportunities

Intelligent reflecting surfaces (IRS) have emerged as a promising technology for future 6G wireless networks, offering programmable control of the wireless environment by adjusting the phase shifts of reflecting elements. However, IRS performance relies on accurately configuring the phase shifts of reflecting elements, which introduces substantial phase shift information (PSI) delivery overhead, especially in large-scale or rapidly changing environments. This paper first introduces the architecture of IRS-assisted systems and highlights real-world use cases where PSI delivery becomes a critical bottleneck. It then reviews current PSI compression approaches, outlining their limitations in adaptability and scalability. To address these gaps, we propose a prompt-guided PSI compression framework that leverages task-aware prompts and meta-learning to achieve efficient and real-time PSI delivery under diverse conditions. Simulation results show improved reconstruction accuracy and robustness compared to the baseline method. Finally, we discuss open challenges and outline promising directions for future research.

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arXiv2025

Meta-Learning Driven Lightweight Phase Shift Compression for IRS-Assisted Wireless Systems

The phase shift information (PSI) overhead poses a critical challenge to enabling real-time intelligent reflecting surface (IRS)-assisted wireless systems, particularly under dynamic and resource-constrained conditions. In this paper, we propose a lightweight PSI compression framework, termed meta-learning-driven compression and reconstruction network (MCRNet). By leveraging a few-shot adaptation strategy via model-agnostic meta-learning (MAML), MCRNet enables rapid generalization across diverse IRS configurations with minimal retraining overhead. Furthermore, a novel depthwise convolutional gating (DWCG) module is incorporated into the decoder to achieve adaptive local feature modulation with low computational cost, significantly improving decoding efficiency. Extensive simulations demonstrate that MCRNet achieves competitive normalized mean square error performance compared to state-of-the-art baselines across various compression ratios, while substantially reducing model size and inference latency. These results validate the effectiveness of the proposed asymmetric architecture and highlight the practical scalability and real-time applicability of MCRNet for dynamic IRS-assisted wireless deployments.

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arXiv2022

Phase Shift Compression for Control Signaling Reduction in IRS-Aided Wireless Systems: Global Attention and Lightweight Design

A potential 6G technology known as intelligent reflecting surface (IRS) has recently gained much attention from academia and industry. However, acquiring the optimized quantized phase shift (QPS) presents challenges for the IRS due to the phenomenon of signaling storms. In this paper, we attempt to solve the above problem by proposing two deep learning models, the global attention phase shift compression network (GAPSCN) and the simplified GAPSCN (S-GAPSCN). In GAPSCN, we propose a novel attention mechanism that emphasizes a greater number of meaningful features than previous attention-related works. Additionally, S-GAPSCN is built with an asymmetric architecture to meet the practical constraints on computation resources of the IRS controller. Moreover, in S-GAPSCN, to compensate for the performance degradation caused by simplifying the model, we design a low-computation complexity joint attention-assisted multi-scale network (JAAMSN) module in the decoder of S-GAPSCN. Simulation results demonstrate that the proposed global attention mechanism achieves prominent performance compared with the existing attention mechanisms and the proposed GAPSCN can achieve reliable reconstruction performance compared with existing state-of-the-art models. Furthermore, the proposed S-GAPSCN can approach the performance of the GAPSCN at a much lower computational cost.

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arXiv2021

Convolutional Autoencoder-Based Phase Shift Feedback Compression for Intelligent Reflecting Surface-Assisted Wireless Systems

In recent years, intelligent reflecting surface (IRS) has emerged as a promising technology for 6G due to its potential/ability to significantly enhance energy- and spectrum-efficiency. To this end, it is crucial to adjust the phases of reflecting elements of the IRS, and most of the research works focus on how to optimize/quantize the phase for different optimization objectives. In particular, the quantized phase shift (QPS) is assumed to be available at the IRS, which, however, does not always hold and should be fed back to the IRS in practice. Unfortunately, the feedback channel is generally bandwidth-limited, which cannot support a huge amount of feedback overhead of the QPS particularly for a large number of reflecting elements and/or the quantization level of each reflecting element. In order to break this bottleneck, in this letter, we propose a convolutional autoencoder-based scheme, in which the QPS is compressed on the receiver side and reconstructed on the IRS side. In order to solve the problems of mismatched distribution and vanishing gradient, we remove the batch normalization (BN) layers and introduce a denosing module. By doing so, it is possible to achieve a high compression ratio with a reliable reconstruction accuracy in the bandwidth-limited feedback channel, and it is also possible to accommodate existing works assuming available QPS at the IRS. Simulation results confirm the high reconstruction accuracy of the feedback/compressed QPS through a feedback channel, and show that the proposed scheme can significantly outperform the existing compression algorithms.

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