基于LSTM-ACB的物联网混合流量随机接入方案
LSTM-ACB-Based RA for IoT Mixed Traffic
AI总结：
针对URLLC-mMTC混合流量，提出基于LSTM预测与ACB流量感知资源切片的随机接入方案，结合免授权接入与拥塞缓解步骤，相比GF基准显著提升系统性能。
摘要：
针对URLLC-mMTC混合流量场景，提出了一种利用真实统计模型的新型随机接入（RA）方案，其使用模式呈现长期流量规律性。通过长短期记忆神经网络预测流量，实现基于竞争接入类别禁止（ACB）过程的流量感知资源切片。该方法将免授权（GF）随机接入方案与缓解拥塞的中间步骤相结合。协议权衡是引入较小开销，同时凭借中间步骤能够解码更多接收数据包。数值结果评估了各过程及组合方案的系统性能。与GF基准相比，系统性能显著提升。
英文摘要：
A novel random access (RA) scheme for mixed URLLC-mMTC traffic scenario is proposed using realistic statistical models, with the use mode presenting long-term traffic regularity. The traffic is predicted by a long short-term memory neural network, which enables a traffic-aware resource slicing aided by contention access class barring-based procedure. The method combines a grant-free (GF) RA scheme with an intermediate step to congestion alleviation. The protocol trade-off is a small overhead while enabling a higher number of decoded received packets thanks to the intermediate step. Numerical results evaluate the system performance for each procedure and combined solution. A comparison with GF benchmark reveals substantial improvement in system performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
