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A fast parallel SC-Fano decoding algorithm for PAC codes

Science China(information Sciences) 2023年第5期66卷 215-226页

作者： Yunzhi WU Li LI Pingzhi FAN Information Coding & Transmission Key Lab of Sichuan Province Communications & Sensor Networks for Modern Transportation International Cooperatation Research Centre of China Southwest Jiaotong University

In order to efectively reduce the “decoding latency” of successive cancellation decoding aided Fano(SC-Fano) decoder employed in polarization-adjusted convolutional(PAC) codes, a fast parallel SCFano decoding algorithm is designed, which leverages multiple individual decoding components. Critical challenges arise from how to appropriately initialize the thresholds of diferent SC-Fano decoding components and how will these decoding components cooperate with each other. After appropriately overcoming these challenges, our design is capable of halving the “decoding latency” without incurring any noticeable frame error rate(FER) performance degradation, meanwhile, the overall computational complexity is normally constrained below one and a half times of that required by conventional SC-Fano decoders in simulated typical configurations.

关键词： PAC codes parallel decoding Fano decoding complexity

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Specific Emitter Identification Based on CNN via Variational Mode Decomposition and Bimodal Feature Fusion 3

Specific Emitter Identification Based on CNN via Variational...

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3rd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Su, Jinling Liu, Heng Yang, Liu Southwest Jiaotong University Key Lab of Information Coding & Transmission Chengdu China

ISBN: (纸本)9781665472784

Specific emitter identification (SEI) refers to the technology of identifying specific emitter individuals by the RF fingerprint carried by the emitter transmitting signals. RF fingerprint is a hardware difference feature caused by the nonlinearity and tolerance of the components inside the emitter, and can be used as the unique identification of the emitter. In this paper, we propose an emitter identification scheme based on convolutional neural network (CNN) via variational mode decomposition (VMD) and bimodal feature fusion. Firstly, four stray components of LoRa signals are extracted by VMD, which contain unintentional modulation features of the original signals. Then the time-frequency features (VMD-S) and temporal features (VMD-T) are extracted from the four stray components. Consequently, a CNN-based two-channel feature extraction network is built with an attention module and a bimodal feature fusion strategy which realizes the fusion of VMD-S and VMD-T. The experimental results show that the proposed SEI scheme achieves higher identification accuracy in comparison with the scheme without feature fusion at the low signal-to-noise ratio (SNR) region. © 2023 IEEE.

关键词： Variational mode decomposition

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A layered grouping random access scheme based on dynamic preamble selection for massive machine type communications

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Science China(information Sciences) 2022年第7期65卷 269-270页

作者： Gaofeng CHENG Huan CHEN Pingzhi FAN Li LI Li HAO Information Coding & Transmission Key Lab of Sichuan Province CSNMT International Cooperation Research Centre of China Southwest Jiaotong University

Dear editor, The massive random access （RA） schemes in the existing literature are mainly grant-free approaches, which are supported by non-orthogonal multiple access [1–3], compressive sensing （CS） [4, 5], group paging [2, 6], and multichannel ALOHA [7]. However, they still face the challenges of high energy consumption and data overhead, especially in future ultra-dense Internet of Things （Io T） networks （> 10～6devices/km2） [8].

关键词： grant free layered grouping AMP algorithm minimum preamble length mMTC

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A UAV-Enabled Integrated Sensing and Covert Communication System

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhou, Yi Fan, Pingzhi Ma, Zheng Southwest Jiaotong University Key Lab of Information Coding and Transmission Chengdu610031 China

In this paper, an integrated sensing and covert communications (ISACC) framework is established for unmanned aerial vehicle (UAV) system, where one mobile UAV is deployed to provide sensing and communication services simultaneously, while there exists a malicious warden attempts to detect whether the UAV is communicating with multiple ground users during the whole flight. First, we consider that the communication signal from the UAV is hided under the cover of noise uncertainty in the presence of sensing signal. Then, to maximize the minimum achievable rate among all users with the required sensing performance and covertness, an efficient iterative solution is designed by jointly optimizing the power allocation, trajectory and communication scheduling of UAV. Numerical results are provided to verify the effectiveness of our proposed solution and demonstrate the fundamental trade-off between covertness, sensing requirement and communication achievable rate. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An Efficient Joint Decoding Scheme for Outer Codes in DNA-Based Data Storage

An Efficient Joint Decoding Scheme for Outer Codes in DNA-Ba...

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2023 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2023

作者： Ding, Yi He, Xuan Cai, Kui Song, Guanghui Dai, Bin Tang, Xiaohu Southwest Jiaotong University Information Coding and Transmission Key Lab of Sichuan Province China Cluster Singapore Xidian University State Key Lab of Integrated Services Networks China

ISBN: (纸本)9798350345407

In this paper, we consider a simplified system model for DNA-based data storage, where each DNA string is either correctly transmitted, or being erased, or being corrupted by random substitution errors, and all strings are randomly shuffled with each other. Any linear block error-correction code can be used as the outer code to encode bits among different strings. We derive soft/hard information of all bits, which allows to independently decode each bit within a string, leading to an independent decoding scheme. To improve the decoding performance, we measure the reliability of each string based on the independent decoding result, and perform a further step of decoding over the most reliable strings, leading to a joint decoding scheme. Simulations with low-density parity-check codes confirm that the joint decoding scheme can reduce the frame error rate by more than 3 orders of magnitude compared to the independent decoding scheme. © 2023 IEEE.

关键词： DNA

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Randomized Orthogonal Matching Pursuit Algorithm with Adaptive Partial Selection for Sparse Signal Recovery

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SIAM Journal on Imaging Sciences 2025年第2期18卷 1028-1057页

作者： Wen, Jinming Li, Changhao Shu, Qianyu Zhou, Zhengchun School of Mathematics Jilin University Changchun China Department of Information Science and Technology Jinan University Guangzhou China School of Mathematical Sciences Sichuan Normal University Chengdu China Key Lab of Information Coding and Transmission Southwest Jiaotong University Chengdu China

The orthogonal matching pursuit (OMP) algorithm, known for its exceptional ability to reconstruct sparse signals, is a widely employed algorithm in compressed sensing. Numerous studies have provided theoretical analyses supporting its capability for achieving exact recovery. However, when applied to large-scale sparse signal recovery, the OMP algorithm incurs substantial computational overhead, leading to prolonged running time. To address this challenge, we design a Randomized OMP with Adaptive Partial Selection (AROMP) algorithm to mitigate computational overhead and reduce runtime. The novelty of the AROMP algorithm lies in its utilization of a randomized index selection method rather than a greedy approach to select the index in each iteration. Subsequently, we theoretically characterize the gap between AROMP and OMP for exactly recovering an s-sparse signal and show that the gap decreases as the number of comparisons K increases, sparsity s decreases, or signal dimension n decreases. As K approaches n-1, the gap between AROMP and OMP tends to 0. The experimental results substantiate that our proposed method significantly reduces running time while maintaining satisfactory accuracy in sparse signal recovery, face recognition tasks, and image reconstruction tasks. © 2025 Society for Industrial and Applied Mathematics.

关键词： Image reconstruction

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Distributed Unknown Specific Emitter Identification Based on Federated Learning

Distributed Unknown Specific Emitter Identification Based on...

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IEEE Conference on Vehicular Technology (VTC)

作者： Hongyujie Xiao Heng Liu Yi Zhou Liu Yang Zheng Ma Key Lab of Information Coding and Transmission Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

The utilization of Specific Emitter Identification (SEI) in war defense has significantly improved the capability to identify and analyze enemy targets through the use of radio frequency fingerprint (RFF) extracted from received signals to identify specific emitters. The application of deep learning (DL) technology, specifically in wireless security authentication, has the potential to further enhance SEI. However, traditional machine learning approaches are centralized-based, which is not optimal for SEI due to the private nature of the emitter dataset, especially when the dataset are distributed among different organizations. Federated learning (FL) offers a solution by allowing multiple clients to cooperate in model training without dataset exchange. This paper introduces FL into SEI and proposes an open-set recognition framework. Unlike closed-set recognition, which only recognizes the emitters in the training set, open-set recognition is capable of identifying emitters that are not in the training set. Therefore the proposed framework is extremely suitable for the unknown emitter identification. The experimental results show that the proposed scheme achieves a high level of accuracy in identifying unknown emitters, even if the model is trained distributedly.

关键词： Training Deep learning Vehicular and wireless technologies Privacy Accuracy Federated learning Distributed databases

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Incentive Based Federated Learning Data Dissemination for Vehicular Edge Computing Networks

Incentive Based Federated Learning Data Dissemination for Ve...

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IEEE Conference on Vehicular Technology (VTC)

作者： Muhammad Saleh Bute Pingzhi Fan Quyuan Luo Key Lab. of Information Coding & Transmission School of Information Science and Technology Southwest Jiaotong University Chengdu China

The advancements in vehicular networks and applications brings about a huge demand for vehicles to process computation-intensive tasks such as machine learning (ML) algorithms. There is a need for an efficient solution to meet up these challenges. Federated learning (FL), which is distributed and does not require the transmission of dataset to the server, is more suitable to the resource constraint vehicular edge network. However, not all vehicles will participate in FL training without any reward. This paper proposes an incentive-based federated learning scheme for vehicular networks. Vehicles participating in federated learning are selected using an auction algorithm based on maximized social welfare. Simulations are conducted to evaluate the performance of the proposed scheme.

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Two Classes of Optimal Multi-Input Structures for Node Computations in Message Passing Algorithms

arXiv

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arXiv 2024年

作者： Lu, Teng He, Xuan Tang, Xiaohu Information Coding and Transmission Key Lab of Sichuan Province Southwest Jiaotong University Chengdu611756 China

In this paper, we delve into the computations performed at a node within a message-passing algorithm. We investigate low complexity/latency multi-input structures that can be adopted by the node for computing outgoing messages y = (y1, y2, ..., yn) from incoming messages x = (x1, x2, ..., xn), where each yj, j = 1, 2, ..., n is computed via a multi-way tree with leaves x excluding xj. Specifically, we propose two classes of structures for different scenarios. For the scenario where complexity has a higher priority than latency, the star-tree-based structures are proposed. The complexity-optimal ones (as well as their lowest latency) of such structures are obtained, which have the near-lowest (and sometimes the lowest) complexity among all structures. For the scenario where latency has a higher priority than complexity, the isomorphic-directed-rooted-tree-based structures are proposed. The latency-optimal ones (as well as their lowest complexity) of such structures are obtained, which are proved to have the lowest latency among all structures. Copyright © 2024, The Authors. All rights reserved.

关键词： Structural optimization

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Secondary Access Class Barring for Machine Type Communications with PUSCH Resource Constraints 100

Secondary Access Class Barring for Machine Type Communicatio...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Wen, Yuxuan Yang, Liu Bai, Chaoyuan Liu, Heng Chengdu611756 China Southwest Jiaotong University Key Lab of Information Coding & Transmission Institude of Mobile Communication Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China

ISBN: (纸本)9798331517786

With the continuous increase of IoT devices, multiple devices desire to use network resources simultaneously during peak hours and overload conditions, leading to collisions and a reduction in the network's effective throughput. The Access Class Barring (ACB) method has been proposed as the optimal potential solution in LTE communication networks to overcome this problem. However, existing ACB schemes do not fully utilize preamble resources, and most schemes do not consider the situation where PUSCH resources are limited. Therefore, this paper proposes a two-stage ACB algorithm that considers the situation of limited PUSCH resources. This algorithm recycles part of the preamble resources and sets the ACB factor based on the number of PUSCH resources. Simulation results show that our proposed algorithm significantly outperforms traditional schemes in terms of total service time, delay, throughput, and PUSCH resource efficiency. © 2024 IEEE.

关键词： Internet of things

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