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Multi-Functional RIS-Aided Wireless Communications

IEEE INTERNET OF THINGS JOURNAL 2023年第23期10卷 21133-21134页

作者： Wang, Wen Ni, Wanli Tian, Hui Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

In this article, we propose a multi-functional reconfigurable intelligent surface (MF-RIS) to address the half-space coverage and double-fading attenuation issues faced by existing RISs. By simultaneously reflecting, refracting, and amplifying the incident signal, the proposed MF-RIS is capable of realizing full-space coverage with the mitigated signal degradation. The operation principle of the MF-RIS is first provided, and then an efficient beamforming scheme is proposed for MF-RIS-aided wireless communications. Simulation results show that, through combining multiple functions on one surface, the MF-RIS achieves significant throughput improvement over existing RISs.

关键词： Beamforming design multi-functional reconfigurable intelligent surface (MF-RIS) operation principle rate maximization

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Swin-Loc: Transformer-Based CSI Fingerprinting Indoor Localization With MIMO ISAC System

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IEEE TRANSACTIONS ON VEHICULAR technology 2024年第8期73卷 11664-11679页

作者： Xu, Xiaodong Zhu, Fangzhou Han, Shujun Yu, Zhongyao Zhao, Hangyu Wang, Bizhu Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

With multiple-input multiple-output (MIMO) technologies widely employed in mobile communication systems, wireless signals will have higher resolution in both the time and angle domains. It makes high-precision localization gain increasing attention in MIMO integrated sensing and communication (ISAC) systems. However, the decimeter-level precise indoor localization is full of challenges due to the multi-path fading and additional noise in indoor propagations. Excessive resource overhead and channel state information (CSI) fingerprint distortion in complex channel environments are the main factors that hinder indoor high-precision localization. To solve the CSI fingerprint distortion while reducing resource consumption, we propose a Swin Transformer-based CSI data-driven indoor localization framework called Swin-Loc. In the proposed Swin-Loc, a channel fingerprint extraction scheme is formulated to enhance the CSI features. Moreover, an improved Swin Transformer-based CSI network (SwinCSINet) model is proposed to improve localization precision. Experiments are conducted on the real-world CSI dataset given by the KU Leuven lab and the simulation CSI dataset generated by the DeepMIMO platform. Simulation results demonstrate that the localization precision on all datasets in the metric of root mean squared error (RMSE) is within 0.3 m, which outperforms current deep neural networks (DNNs) based schemeand attention-based scheme. Furthermore, the storage overhead of the improved SwinCSINet model is reduced to about 33$\%$ of the DNN regression model, and the real-time performance is optimized.

关键词： Location awareness Fingerprint recognition Feature extraction Transformers MIMO communication Data mining Wireless communication CSI fingerprint data-driven localization ISAC MIMO swin transformer

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MIMO Free-Space Optical Communications Using Photon-Counting Receivers Under Weak Links

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IEEE COMMUNICATIONS LETTERS 2023年第4期27卷 1185-1189页

作者： Wang, Siming Peng, Mugen Yuan, Renzhi Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Spatial diversity techniques are usually used to mitigate the influence of turbulence in the free-space optical communication (FSOC) with photon-counting receivers, especially when the communication links are weak. A maximum likelihood (ML) detection can be directly applied to the outputs of photon-counting receivers. However, it is challenging to obtain an analytical error probability for the ML detection. In this letter, we derive the analytical error probability of the ML detection under both turbulent and non-turbulent cases by equivalently considering L detected photons;and the approximating error is in the order of magnitude O(?(L)b/L!), where ?(b) is the back -b ground noise. Besides, we prove that the ML detection equals a threshold detection following an equal-gain combining when the signal strengths are close to each other in non-turbulent cases. Numerical results show that L < 10 is sufficient for typical scenarios with small background noises ?(b) < 1. Besides, we demonstrate that the derived analytical error probability can achieve a higher precision and computational efficiency compared with Monte-Carlo methods.

关键词： Photonics Error probability Receivers MIMO communication Background noise Monte Carlo methods Fading channels Free-space optical communication maximum likelihood detection multiple-input multiple-output photon-counting receiver

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Reduced Guard Interval Orthogonal Frequency Division Multiplexing: A Soft Trade-Off Solution Toward Spectral Efficiency

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IEEE COMMUNICATIONS LETTERS 2024年第12期28卷 2920-2924页

作者： Wang, Zhaoqi Xu, Ran Wu, Shihao Liu, Xiqing Peng, Mugen Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The guard interval, termed as cyclic prefix (CP) or zero-padding (ZP), is utilized to mitigate multipath interference in orthogonal frequency division multiplexing (OFDM) systems but at the expense of spectral efficiency. Symbol cyclic shift equalization (SCSE) is our previous proposal to address multipath propagation in CP-free OFDM systems. However, SCSE-OFDM exhibits poor bit error rate (BER) performance when the power of the shortest resolvable path is relatively low. In light of this, we carry out a reduced guard interval (RGI) OFDM scheme that achieves a soft trade-off between SCSE-OFDM and ZP-OFDM, where RGI length can be optimized using genetic algorithm. Simulation results demonstrate superior BER performance of RGI-OFDM over SCSE-OFDM and higher spectral efficiency compared to both SCSE-OFDM and ZP-OFDM. RGI-OFDM also exhibits evident advantages in harsh environments with large delay spread.

关键词： OFDM Tail Delays Symbols Spectral efficiency Receivers Interference cancellation Genetic algorithms Baseband Digital-analog conversion Orthogonal frequency division multiplexing reduced guard interval delay spread spectral efficiency

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Enhancing NOMA Networks via Reconfigurable Multi-Functional Surface

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IEEE COMMUNICATIONS LETTERS 2023年第4期27卷 1195-1199页

作者： Zheng, Ailing Ni, Wanli Wang, Wen Tian, Hui Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

By flexibly manipulating the radio propagation environment, reconfigurable intelligent surface (RIS) is a promising technique for future wireless communications. However, the single-side coverage and double-fading attenuation faced by conventional RISs largely restrict their applications. To address this issue, we propose a novel concept of multi-functional RIS (MF-RIS), which provides reflection, transmission, and amplification simultaneously for the incident signal. With the aim of enhancing the performance of a non-orthogonal multiple-access (NOMA) downlink multiuser network, we deploy an MF-RIS to maximize the sum rate by jointly optimizing the active beamforming and MF-RIS coefficients. Then, an alternating optimization algorithm is proposed to solve the formulated non-convex problem by exploiting successive convex approximation and penalty-based method. Numerical results show that the proposed MF-RIS outperforms conventional RISs under different settings.

关键词： NOMA Array signal processing Quality of service Decoding Radio propagation Power supplies Nickel Multi-functional reconfigurable intelligent surface non-orthogonal multiple access rate maximization

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PrivTDSI: A Local Differentially Private Approach for Truth Discovery via Sampling and Inference

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IEEE TRANSACTIONS ON BIG DATA 2023年第2期9卷 471-484页

作者： Zhang, Pengfei Cheng, Xiang Su, Sen Zhu, Binyuan Beijing Univ Posts ands Telecommun State Key Lab Networking & Switching Tech Beijing 100876 Peoples R China

Truth discovery is an effective way to identify the aggregated truth of each task among multiple observed data drawn from different workers of varying reliabilities. However, existing studies are insufficient to protect individuals' privacy, as they either just guarantee the weaker versions of local differential privacy (LDP) or potentially assume that the tasks are independent. In this paper, we, for the first time, investigate the problem of truth discovery while achieving the rigorous LDP for each worker with continuous inputs without the independence assumption. We present a locally differentially private truth discovery approach called PrivTDSI based on sampling and inference with solid privacy and utility guarantees. In PrivTDSI, the server first determines which values of each worker should be sampled according to a sample proportion and sends the indexes of these values to each worker. Then, each worker adds noise into the sampled values for privacy protection and uploads them to the server. After receiving the noisy sampled values from all the workers, the server first infers the unsampled values and then conducts truth discovery based on both the noisy sampled values and the inferred values. In particular, to determine the sample proportion, we formulate a constrained nonlinear programming problem and give a closed-form solution to this problem. Moreover, to determine which values of each worker should be sampled while avoiding the situation where the values of some workers or tasks might not be sampled at all, we develop a two-stage sampling method called TOSS. Furthermore, to infer the unsampled values accurately, we design a quality-aware inference method based on matrix factorization called QualityMF. Experimental results on two real-world datasets and a synthetic dataset demonstrate the effectiveness of PrivTDSI.

关键词： Task analysis Privacy Servers Reliability Crowdsourcing Protocols Noise measurement truth discovery local differential privacy privacy protection sampling and inference

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Knowledge Distillation for Image Restoration: Simultaneous Learning from Degraded and Clean Images

Knowledge Distillation for Image Restoration: Simultaneous L...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yongheng Yan, Danfeng State Key Laboratory of Networking and Switching Technology BUPT Beijing China

ISBN: (纸本)9798350368741

Model compression through knowledge distillation has seen extensive application in classification and segmentation tasks. However, its potential in image-to-image translation, particularly in image restoration, remains underexplored. To address this gap, we propose a Simultaneous Learning Knowledge Distillation (SLKD) framework tailored for model compression in image restoration tasks. SLKD employs a dual-teacher, single-student architecture with two distinct learning strategies: Degradation Removal Learning (DRL) and Image Reconstruction Learning (IRL), simultaneously. In DRL, the student encoder learns from Teacher A to focus on removing degradation factors, guided by a novel BRISQUE extractor. In IRL, the student decoder learns from Teacher B to reconstruct clean images, with the assistance of a proposed PIQE extractor. These strategies enable the student to learn from degraded and clean images simultaneously, ensuring high-quality compression of image restoration models. Experimental results across five datasets and three tasks demonstrate that SLKD achieves substantial reductions in FLOPs and parameters, exceeding 80%, while maintaining strong image restoration performance. © 2025 IEEE.

关键词： feature extractor image restoration Knowledge distillation simultaneous learning

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Two-Hop Delay-Aware Energy Efficiency Resource Allocation in Space-Air-Ground Integrated Smart Grid Network

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Computers, Materials & Continua 2025年第5期83卷 2429-2447页

作者： Qinghai Ou Min Yang Jingcai Kong Yang Yang Beijing FibrLink Communications Co.Ltd. Beijing100071China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

The lack of communication infrastructure in remote regions presents significant obstacles to gathering data from smart power sensors(SPSs)in smart grid *** such cases,a space-air-ground integrated network serves as an effective emergency *** study addresses the challenge of optimizing the energy efficiency of data transmission fromSPSs to low Earth orbit(LEO)satellites through unmanned aerial vehicles(UAVs),considering both effective capacity and fronthaul link capacity *** to the non-convex nature of the problem,the objective function is reformulated,and a delay-aware energy-efficient power allocation and UAV trajectory design(DEPATD)algorithm is proposed as a two-loop *** the inner loop remains non-convex,the block coordinate descent(BCD)method is employed to decompose it into three subproblems:power allocation for SPSs,power allocation for UAVs,and UAV trajectory *** first two subproblems are solved using the Lagrangian dual method,while the third is addressed with the successive convex approximation(SCA)*** iteratively solving these subproblems,an efficient algorithm is developed to resolve the inner loop *** results demonstrate that the energy efficiency of the proposed DEPATD algorithm improves by 4.02% compared to the benchmark algorithm when the maximum transmission power of the SPSs increases from 0.1 to 0.45W.

关键词： Energy efficiency effective capacity delay requirement power allocation smart grid space-air-ground integrated network

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Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression

Soft Knowledge Distillation with Multi-Dimensional Cross-Net...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yongheng Yan, Danfeng State Key Laboratory of Networking and Switching Technology BUPT Beijing China

ISBN: (纸本)9798350368741

Transformer-based encoder-decoder models have achieved remarkable success in image-to-image transfer tasks, particularly in image restoration. However, their high computational complexity-manifested in elevated FLOPs and parameter counts-limits their application in real-world scenarios. Existing knowledge distillation methods in image restoration typically employ lightweight student models that directly mimic the intermediate features and reconstruction results of the teacher, overlooking the implicit attention relationships between them. To address this, we propose a Soft Knowledge Distillation (SKD) strategy that incorporates a Multi-dimensional Cross-net Attention (MCA) mechanism for compressing image restoration models. This mechanism facilitates interaction between the student and teacher across both channel and spatial dimensions, enabling the student to implicitly learn the attention matrices. Additionally, we employ a Gaussian kernel function to measure the distance between student and teacher features in kernel space, ensuring stable and efficient feature learning. To further enhance the quality of reconstructed images, we replace the commonly used L1 or KL divergence loss with a contrastive learning loss at the image level. Experiments on three tasks-image deraining, deblurring, and denoising-demonstrate that our SKD strategy significantly reduces computational complexity while maintaining strong image restoration capabilities. © 2025 IEEE.

关键词： contrastive learning image restoration Knowledge distillation multi-dimensional cross-net attention

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Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

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China Communications 2023年第6期20卷 100-115页

作者： Zhen Zhang Yuxiang Zhang Jianhua Zhang Feifei Gao State Key Lab of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Institute for Artificial Intelligence Tsinghua UniversityBeijing 100084China

In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real *** capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

关键词： channel prediction time-varying channel conditional generative adversarial network multipath channel deep learning

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