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Collision-Aware Pattern Design for Uplink Complementary Coded Code-Hopping Multiple Access Systems

IEEE Transactions on Communications 2025年

作者： Liu, Xiqing Yang, Yiran Lu, Xuanji Peng, Mugen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Information and Communication Engineering Beijing100876 China

Code-hopping multiple access (CHMA) technology is an effective approach for improving the user capacity of direct sequence spread spectrum systems. However, the use of randomly generated code-hopping (CH) patterns in a typical CHMA system often causes code collisions that severely degrade the bit error rate (BER) performance. Our previous work has developed a CH pattern design method to improve the BER performance of complementary coded CHMA (CC-CHMA) by pre-generating well-designed CH patterns. However, this method is mainly applicable to single-path channels, while detecting collisions and taking appropriate actions in complex multipath channels remains challenging. In this work, we present a collision-aware algorithm and propose a CH pattern update algorithm based on multi-agent reinforcement learning for CC-CHMA in uplink multipath channels. The simulation results show that the proposed CH pattern design scheme can automatically adapt to the uplink multipath channels and effectively reduce code collisions, thereby achieving further improved BER performance compared with existing CC-CHMA systems. © 1972-2012 IEEE.

关键词： Direct sequence systems

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QoKV: Comprehending and Surpassing the Hurdles of KV Cache Quantization

QoKV: Comprehending and Surpassing the Hurdles of KV Cache Q...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinguang Wang Yuexi Yin Haifeng Sun Tingting Yang Qi Qi Zirui Zhuang Jingyu Wang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China PengCheng Laboratory Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Large language models (LLMs) have demonstrated outstanding performance in various tasks. However, the memory footprint of the key-value (KV) cache generated during model inference poses significant challenges for efficient model deployment. This paper presents a detailed analysis of the KV cache and identifies potential sources of quantization errors. We find that the attention scores exhibit a strong power-law distribution while showing an additional attention to the initial and recent tokens, and that only a small subset of outlier channels in KV cache span a wide numerical range. Based on these findings, we propose QoKV, an efficient post-training quantization (PTQ) method designed for the KV cache, which facilitates accurate low-bit quantization through a novel group evaluation strategy and numerical scaling. Extensive experimental results across various tasks demonstrate that our proposed method outperforms existing methods, achieving near-floating-point performance while saving 73.75% of the cache footprint.

关键词： Quantization (signal) Accuracy Large language models Memory management Acoustics Speech processing

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MergeQuant: Accurate 4-bit Static Quantization of Large Language Models by Channel-wise Calibration

arXiv

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arXiv 2025年

作者： Wang, Jinguang Wang, Jingyu Sun, Haifeng Yang, Tingting Zhuang, Zirui Ning, Wanyi Yin, Yuexi Qi, Qi Liao, Jianxin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China PengCheng Laboratory Shenzhen China

Quantization has been widely used to compress and accelerate inference of large language models (LLMs). Existing methods focus on exploring the per-token dynamic calibration to ensure both inference acceleration and model accuracy under 4-bit quantization. However, in autoregressive generation inference of long sequences, the overhead of repeated dynamic quantization and dequantization steps becomes considerably expensive. In this work, we propose MergeQuant, an accurate and efficient per-channel static quantization framework. MergeQuant integrates the per-channel quantization steps with the corresponding scalings and linear mappings through a Quantization Step Migration (QSM) method, thereby eliminating the quantization overheads before and after matrix multiplication. Furthermore, in view of the significant differences between the different channel ranges, we propose dimensional reconstruction and adaptive clipping to address the non-uniformity of quantization scale factors and redistribute the channel variations to the subsequent modules to balance the parameter distribution under QSM. Within the static quantization setting of W4A4, MergeQuant reduces the accuracy gap on zero-shot tasks compared to FP16 baseline to 1.3 points on Llama-2-70B model. On Llama-2-7B model, MergeQuant achieves up to 1.77× speedup in decoding, and up to 2.06× speedup in end-to-end compared to FP16 baseline. Copyright © 2025, The Authors. All rights reserved.

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Barycenter Calibration for MUSIC Spectrum of OFDM-ISAC Signals – A Super-Resolution Waveform Perspective 14th

Barycenter Calibration for MUSIC Spectrum of OFDM-ISAC Sig...

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14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Yin, Meiyu Li, Jingwen Gong, Jialong Liu, Xiqing Peng, Mugen Xia, Nian State Key Laboratory of Networking and Switching Technology School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University Nanjing210023 China

ISBN: (纸本)9783031862021

The sixth-generation mobile communication system is expected to provide sensing services, and thus the integrated sensing and communication waveform has emerged as a hot research topic. In typical sensing systems, such as radar, sensing resolution is often constrained by available wireless resources. Achieving super-resolution sensing within the resource constraint poses a significant challenge. To this end, this work proposed a calibration algorithm for orthogonal frequency division multiplexing waveforms to conduct barycenter calibration (BC) of the multiple signal classification (MUSIC) spectrum that performs two-dimensional sensing. Simulation results demonstrated that the BC algorithm could significantly reduce sensing errors. Even at the edge of the MUSIC spectrum, it still exhibits robust performance. In addition, to comprehensively evaluate the performance gains, we introduce the capacity-to-error ratio and provide simulation results and discussions. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Orthogonal frequency division multiplexing

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FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection

FGU3R: Fine-Grained Fusion via Unified 3D Representation for...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guoxin Zhang Ziying Song Lin Liu Zhonghong Ou State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Computer Science and Technology Beijing Jiaotong University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal 3D object detection has garnered considerable interest in autonomous driving. However, multimodal detectors suffer from dimension mismatches that derive from fusing 3D points with 2D pixels coarsely, which leads to suboptimal fusion performance. In this paper, we propose a multimodal framework FGU3R to tackle the issue mentioned above via unified 3D representation and fine-grained fusion, which consists of two important components. First, we propose an efficient feature extractor for raw and pseudo points, termed Pseudo-Raw Convolution (PRConv), which modulates multimodal features synchronously and aggregates the features from different types of points on key points based on multimodal interaction. Second, a Cross-Attention Adaptive Fusion (CAAF) is designed to fuse homogeneous 3D RoI (Region of Interest) features adaptively via a cross-attention variant in a fine-grained manner. Together they make fine-grained fusion on unified 3D representation. The experiments conducted on the KITTI and nuScenes show the effectiveness of our proposed method.

关键词： Point cloud compression Three-dimensional displays Fuses Convolution Aggregates Object detection Detectors Feature extraction Speech processing Autonomous vehicles

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Generative- AiEnabled Lightweight Traffic Detection Architecture for Programmable Gateways in Wireless Networks

Generative- AiEnabled Lightweight Traffic Detection Architec...

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IEEE Conference on Wireless Communications and networking

作者： Yuanling Liu Haipeng Yao Wenji He Tianle Mai State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The rapid growth of 5G and 6G networks has introduced complex traffic patterns and stringent real-time demands. Traditional SDN architectures struggle to meet the low-latency and dynamic requirements of wireless environments due to high communication overhead and rigid hardwares. Programmable switches, with their ability to dynamically cus-tomize data plane behavior, offer a more flexible solution for real-time traffic management at the network edge. However, most existing solutions rely on offline models with limited real-time detection capabilities, resulting in increased overhead and suboptimal performance. In this paper, we present Gendetect, a generative-AI enabled lightweight traffic detection architec-ture for programmable wireless gateways. Gendetect employs generative knowledge distillation to train decision tree-based models, enabling efficient online training and adaptive updates. By generating synthetic training data in real-time, it reduces the need for frequent control plane interactions, mitigating north-south overhead. Additionally, a feature selection mechanism optimizes resource utilization, balancing table entry consumption and detection accuracy. Extensive simulations demonstrate that Gendetect significantly improves traffic detection performance while reducing match-action table entries, making it well-suited for dynamic and resource-constrained wireless networks.

关键词： Training Adaptation models Accuracy Wireless networks Training data Logic gates Traffic control Feature extraction Dynamic scheduling Real-time systems

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EPVerifier: accelerating update storms verification with edge-predicate 24

EPVerifier: accelerating update storms verification with edg...

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Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation

作者： Chenyang Zhao Yuebin Guo Jingyu Wang Qi Qi Zirui Zhuang Haifeng Sun Lingqi Guo Yuming Xie Jianxin Liao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Huawei Technologies Co. Ltd

ISBN: (纸本)9781939133397

Data plane verification is designed to automatically verify network correctness by directly analyzing the data plane. Recent data plane verifiers have achieved sub-millisecond verification for per rule update by partitioning packets into equivalence classes (ECs). A large number of data plane updates can be generated in a short interval, known as update storms, due to network events such as end-to-end establishments, disruption or recovery. When it comes to update storms, however, the verification speed of current EC-based methods is often slowed down by the maintenance of their EC-based network model (EC-model).This paper presents EPVerifier, a fast, partitioned data plane verification for update storms to further accelerate update storms verification. EPVerifier uses a novel edge-predicate-based (EP-based) local modeling approach to avoid drastic oscillations of the EC-model caused by changes in the set of equivalence classes. In addition, with local EPs, EPVerifier can achieve a partition of verification tasks by switches that EC-based methods cannot to get better parallel performance. We implement EPVerifier as an easy-to-use tool, allowing users to quickly get the appropriate verification results at any moment by providing necessary input. Both dataset tracedriven simulations and deployments in the wild show that EPVerifier achieves robustly fast update storm verification and superior parallel performance and these advantages expand with the data plane's complexity and storm size growth. The verification time of EPVerifier for an update storm of size 1M is around 10s on average, a 2-10× improvement over the state-of-the-art.

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Trusted access control mechanism for data with blockchain-assisted attribute encryption

High-Confidence Computing

引用

High-Confidence Computing 2025年第2期5卷

作者： Liu, Chang Wang, Dong Li, Da Guo, Shaoyong Li, Wenjing Qiu, Xuesong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China State Grid Digital Technology Holdings Co. Ltd Beijing100053 China

In the growing demand for data sharing, how to realize fine-grained trusted access control of shared data and protect data security has become a difficult problem. Ciphertext policy attribute-based encryption (CP-ABE) model is widely used in cloud data sharing scenarios, but there are problems such as privacy leakage of access policy, irrevocability of user or attribute, key escrow, and trust bottleneck. Therefore, we propose a blockchain-assisted CP-ABE (B-CP-ABE) mechanism for trusted data access control. Firstly, we construct a data trusted access control architecture based on the B-CP-ABE, which realizes the automated execution of access policies through smart contracts and guarantees the trusted access process through blockchain. Then, we define the B-CP-ABE scheme, which has the functions of policy partial hidden, attribute revocation, and anti-key escrow. The B-CP-ABE scheme utilizes Bloom filter to hide the mapping relationship of sensitive attributes in the access structure, realizes flexible revocation and recovery of users and attributes by re-encryption algorithm, and solves the key escrow problem by joint authorization of data owners and attribute authority. Finally, we demonstrate the usability of the B-CP-ABE scheme by performing security analysis and performance analysis. © 2024 The Author(s)

关键词： Ciphertext

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Redactable Blockchain Supporting Rewriting Authorization Without Trapdoor Exposure

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Wei Duan, Junke Wang, Licheng Peng, Haipeng Zhu, Liehuang Li, Lixiang Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China

Blockchain technology, known for its decentralization and immutability, has been widely applied across various domains. However, this immutability reveals limitations in adapting to rapidly changing legal environments and preventing malicious misuse. To introduce a degree of flexibility, various transaction-level redactable blockchain solutions have been proposed. Yet, current schemes grant modifiers redaction privileges by providing access to the trapdoor, potentially posing risks of malicious dissemination and abuse of the trapdoor. In this paper, we first propose an RSA-based threshold chameleon hash (TCH) construction, allowing the distribution of the trapdoor among a group of authorities. Building on TCH, we develop a threshold policy-based chameleon hash (TPCH). Compared to the Policy-Based Chameleon Hash (PCH) proposed by Derler at NDSS'19, our TPCH supports authorization without exposing the trapdoor. Furthermore, leveraging TPCH, we introduce a novel transaction-level redactable blockchain (TPRB). TPRB supports decentralized authorization without trapdoor exposure and fine-grained rewriting control. Finally, through implementation and evaluation, we demonstrate the practicality and efficiency of our TCH and TPCH schemes. © 2004-2012 IEEE.

关键词： Decentralized control

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Learnable Residual-Based Latent Denoising in Semantic Communication

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IEEE Wireless Communications Letters 2025年第5期14卷 1376-1380页

作者： Xu, Mingkai Wu, Yongpeng Shi, Yuxuan Xia, Xiang-Gen Zhang, Wenjun Zhang, Ping Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Shanghai Jiao Tong University School of Cyber and Engineering Shanghai200240 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancing the quality of the decoded images. Specifically, a latent denoising mapping is established by an iterative residual learning approach to improve the denoising efficiency while ensuring stable performance. Moreover, channel signal-to-noise ratio (SNR) is utilized to estimate and predict the latent similarity score (SS) for conditional denoising, where the number of denoising steps is adapted based on the predicted SS sequence, further reducing the communication latency. Finally, simulations demonstrate that the proposed framework can effectively and efficiently remove the channel noise at various levels and reconstruct visual-appealing images. © 2012 IEEE.

关键词： Noise reduction Semantics Transformers Signal to noise ratio Iterative methods Iterative decoding Decoding Training Noise measurement Merging

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