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Multi-Attempt List Decoding of Polar Codes 认领 引用
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作者 Yuan Peihong Chen Zhe +1 位作者 Wu Yongpeng Gao Yue 《China Communications》 SCIE EI CSCD 2026年第4期27-36,共10页
In this work,we propose a multi-attempt successive cancellation list(MA-SCL)decoder for polar codes that achieves identical error-correction performance to standard SCL decoding while reducing average complexity.Unlik... In this work,we propose a multi-attempt successive cancellation list(MA-SCL)decoder for polar codes that achieves identical error-correction performance to standard SCL decoding while reducing average complexity.Unlike CRC-aided SCL,the proposed MA-SCL progressively restarts decoding with increasing list sizes and reuses information from previous attempts.This design eliminates the need for outer CRC codes.The decoder features dynamic searchspace pruning and an early stopping criterion based on path metrics.Simulations show MA-SCL matches SCL performance with lower average complexity,particularly for short polar-like codes with reed-muller(RM)rate profiles and dynamic frozen constraints.Compared to existing adaptive decoders,MA-SCL offers implementation advantages by eliminating the need for stack-/heap management while providing relatively stable latency bounds(1×to|Λ|×SCL latency). 展开更多
关键词 complexity-adaptive decoding list decoding polar coding
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SatFed:A Resource-Efficient LEO-Satellite-Assisted Heterogeneous Federated Learning Framework 认领 引用
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作者 Yuxin Zhang Zheng Lin +5 位作者 Zhe Chen Zihan Fang Xianhao Chen Wenjun Zhu Jin Zhao Yue Gao 《Engineering》 SCIE EI CSCD 2025年第11期115-126,共12页
Traditional federated learning(FL)frameworks rely heavily on terrestrial networks,whose coverage limitations and increasing bandwidth congestion significantly hinder model convergence.Fortunately,the advancement of lo... Traditional federated learning(FL)frameworks rely heavily on terrestrial networks,whose coverage limitations and increasing bandwidth congestion significantly hinder model convergence.Fortunately,the advancement of low-Earth-orbit(LEO)satellite networks offers promising new communication avenues to augment traditional terrestrial FL.Despite this potential,the limited satellite-ground communication bandwidth and the heterogeneous operating environments of ground devices—including variations in data,bandwidth,and computing power—pose substantial challenges for effective and robust satellite-assisted FL.To address these challenges,we propose SatFed,a resource-efficient satellite-assisted heterogeneous FL framework.SatFed implements freshness-based model-prioritization queues to optimize the use of highly constrained satellite-ground bandwidth,ensuring the transmission of the most critical models.Additionally,a multigraph is constructed to capture the real-time heterogeneous relationships between devices,including data distribution,terrestrial bandwidth,and computing capability.This multigraph enables SatFed to aggregate satellite-transmitted models into peer guidance,improving local training in heterogeneous environments.Extensive experiments with real-world LEO satellite networks demonstrate that SatFed achieves superior performance and robustness compared with state-of-the-art benchmarks. 展开更多
关键词 Low-Earth-orbit satellite networks Distributed machine learning Federated learning System heterogeneity
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