AI 中文总结
针对短包URLLC的资源浪费与不可控错误问题，提出CoDE脉冲神经网络接收机，通过候选消息集实现可认证判决，在约一半延迟与计算量下保证可靠性。
AI 中文摘要
超可靠低延迟通信（URLLC）必须在严格时限内以低误码率传输短包。传统接收机需等待完整数据包后再判决，即便许多数据包可在时限前很久就被解析，仍会消耗全部延迟与能量。然而，无可靠性保障的过早判决可能导致静默错误传输，因此接收机需在资源浪费与不可控错误间权衡。本文提出Conformal Decode-or-Erase（CoDE），一种脉冲神经网络（SNN）接收机，以解决该矛盾。SNN在每个信道使用中读取一个符号，并在预定检查点形成一组候选消息，该集合以规定概率保证包含真实消息。当集合缩小为单元素时，CoDE执行判决；否则声明擦除，触发混合自动重传请求（HARQ）重传。错误判决意味着真实消息不在该单元素集合内，因此该预测集以无分布方式为任何预训练SNN及任何校准规模提供未检测错误率的上界。仿真证实，CoDE可在约为固定长度解码器一半的延迟与计算量下实现可靠性。
英文摘要
Ultra-reliable low-latency communication (URLLC) must deliver short packets within a hard deadline at low error probability. A conventional receiver waits for the full packet before deciding, spending the full latency and energy even though many packets are resolvable well before the deadline. Committing early without a reliability guarantee, however, risks a silent wrong delivery, so the receiver is left choosing between wasted resources and uncontrolled errors. We propose Conformal Decode-or-Erase (CoDE), a spiking neural network (SNN) receiver that resolves this tension. The SNN reads one symbol per channel use and forms, at predetermined checkpoints, a set of candidate messages that provably contains the true one with a prescribed probability. CoDE commits once the set narrows to a singleton and otherwise declares an erasure that triggers hybrid automatic repeat request (HARQ) retransmission. A wrong commit means the true message fell outside that singleton. Hence, the prediction set provides an upper bound on the undetected error rate in a distribution-free manner and for any pretrained SNN and any calibration size. Simulations confirm reliability at roughly half a fixed-length decoder's latency and compute.
