用于突发干扰信道的带GRAND接收机的闭环贝叶斯智能体编码器
Closed-Loop Bayesian Bandit Encoder with GRAND Receiver for a Bursty Interference Channel
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中文总结 AI 辅助
研究在有未知干扰源的信道上，随机线性码与跨码字交织码的分组级选择。采用GRAND接收机及贝叶斯估计器，通过折扣汤普森采样器选传输模式，经实验得出不同阶段模式偏好及模型优势，还给出误块率降低等成果。
中文摘要 AI 辅助
交织可减轻突发错误，但会引入解码延迟并消除信道感知解码器可利用的时间错误结构。我们考虑在具有未知数量开/关干扰源的信道上，在随机线性码和使用跨码字交织的相同码之间进行分组级选择。接收机使用具有可替换噪声模型的猜测随机加性噪声解码（GRAND），并将聚合信道统计信息反馈给发射机处的贝叶斯估计器。一旦估计出干扰幅度和定时参数，就会替换接收机的噪声模型：它计算隐马尔可夫模型后验比特翻转概率，并使用它们对GRAND查询进行排序。折扣汤普森采样器使用吞吐量减去延迟奖励在两种传输模式之间进行选择，其分布是内生非平稳的：接收机自适应而非信道变化会改变每种模式的值。在五个模拟种子中，在信道估计收敛之前，交织模式更受青睐。在激活学习到的解码器之后，非交织模式变得更可取，因为它在没有交织延迟的情况下实现了更低的误块率。在参考配置中，学习到的噪声模型相对于ORBGRAND将误块率降低了大约一个数量级。在完全收敛之前使用部分信道估计可将收敛前的误块率降低高达4.5倍。添加模型预测效用作为置信加权伪观测可将劣化臂的转换后选择减少约65%。在类似100 MHz 5G NR符号率的理想化空中时间转换下，学习瞬态对应于几毫秒的占用符号时间。
英文摘要
Interleaving mitigates burst errors but introduces decoding delay and removes temporal error structure that a channel-aware decoder could exploit. We consider packet-level selection between a random linear code and the same code used with cross-codeword interleaving, over a channel with an unknown number of on/off interferers. The receiver uses Guessing Random Additive Noise Decoding (GRAND) with a replaceable noise model and feeds aggregate channel statistics back to a Bayesian estimator at the transmitter. Once the interference amplitudes and timing parameters are estimated, the receiver's noise model is replaced: it computes hidden-Markov-model posterior bit-flip probabilities and uses them to order GRAND queries. A discounted Thompson sampler selects between the two transmission modes using a goodput-minus-latency reward whose distribution is endogenously nonstationary: receiver adaptation, rather than channel change, alters the value of each mode. Across five simulation seeds, the interleaved mode is preferred before channel estimation converges. After the learned decoder is activated, the non-interleaved mode becomes preferable because it achieves lower block error rate without interleaving delay. In the reference configuration, the learned noise model reduces block error rate by approximately one order of magnitude relative to ORBGRAND. Using partial channel estimates before full convergence reduces pre-convergence block error rate by up to $4.5\times$. Adding model-predicted utilities as confidence-weighted pseudo-observations reduces post-transition selection of the inferior arm by approximately $65\%$. Under an idealized airtime conversion at a 100~MHz 5G~NR-like symbol rate, the learning transient corresponds to a few milliseconds of occupied symbol time.
发表机构
- Ming Hsieh Department of Electrical and Computer Engineering（明希学校电气与计算机工程系）
- Viterbi School of Engineering, University of Southern California（维特比工程学院，南加州大学）
机构由 AI 辅助整理，请以论文原文为准。
