发表机构
Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering, University of Southern California（南加州大学维特比工程学院明李电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出共享学习机制扩展贝叶斯老虎机编码器，通过离线共享性能模式与在线权重更新，在1008种配置中降低33.5%效用损失，并提升信道变化后的适应速度，支持共享与剪枝结合策略。
AI 中文摘要
通信系统必须根据信道条件的变化选择纠错保护和译码努力。贝叶斯老虎机编码器（BBE）利用接收端反馈来学习选择哪种传输配置。我们研究一种通过猜测错误模式进行译码的接收端，采用猜测随机加性噪声译码（GRAND）。我们将BBE的选择组件扩展到1008种编码和译码器配置，通过离线学习共享性能模式并在线更新其权重。译码器噪声模型保持不变。在由训练选择的六配置候选列表中，共享学习相对于独立学习将累积效用损失降低了33.5%。一个固定的训练选择配置与搜索整个目录的共享学习器性能相当。在信道变化后，经过剪枝的共享学习器在88.5%的事件中于2000个数据包内首次达到接近最优的选择标准，而采用相同折扣的剪枝独立学习仅为54.2%。结果支持将共享与剪枝相结合用于配置选择，尽管在严重噪声下，所测试的编码的数据包丢失率仍然较高。
英文摘要
A communication system must choose error protection and decoding effort as channel conditions change. A Bayesian bandit encoder (BBE) uses receiver feedback to learn which transmission configuration to select. We study a receiver that decodes by guessing error patterns, using Guessing Random Additive Noise Decoding (GRAND). We extend BBE's selection component to 1,008 code and decoder configurations by learning shared performance patterns offline and updating their weights online. Decoder noise models remain fixed. On a six-configuration training-selected shortlist, sharing reduces accumulated utility loss by 33.5% relative to independent learning. A fixed training-selected configuration matches the shared learner that searches the full catalog. After channel changes, the pruned shared learner first meets a near-optimal selection criterion in 88.5% of events by 2,000 packets, compared with 54.2% for pruned independent learning with the same discounting. The results support combining sharing and pruning for configuration selection, although packet losses remain high for the tested codes under severe noise.
Comments15 pages, 2 figures, 5 tables; includes appendices
