In massive machine-type communications (mMTC) scenarios, grant-free non-orthogonal multiple access becomes crucial due to the small transmission latency, limited signaling overhead and the ability to support massive connectivity. In a multi-user detection (MUD) problem, the base station (BS) is unaware of the active users and needs to detect active devices. With sporadic devices transmitting signals at any moment, the MUD problem can be formulated as a multiple measurement vector (MMV) sparse recovery problem. Through the Khatri–Rao product, we prove that the MMV problem is transformed into a single measurement vector (SMV) problem. Based on the basis pursuit de-noising approximate message passing (BPDN-AMP) algorithm, a novel learning AMP network (LAMPnet) algorithm is proposed, which is designed to reduce the false alarm probability when the required detection probability is high. Simulation results show that when the required detection probablity is high, the AMP algorithm based on LAMPnet noticeably outperforms the traditional algorithms with acceptable computational complexity.

中文翻译：

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大规模机器类型通信中基于神经网络的多用户检测AMP方法

在大规模机器类型通信（mMTC）场景中，由于传输延迟小，信令开销有限以及支持大规模连接的能力，免授权非正交多路访问变得至关重要。在多用户检测（MUD）问题中，基站（BS）不了解活动的用户，需要检测活动的设备。通过零星的设备随时传输信号，MUD问题可以表述为多测量向量（MMV）稀疏恢复问题。通过Khatri–Rao乘积，我们证明MMV问题已转化为单个测量向量（SMV）问题。基于基本追踪消噪近似消息传递算法（BPDN-AMP），提出了一种新型的学习AMP网络（LAMPnet）算法，当需要的检测概率较高时，此功能可降低误报概率。仿真结果表明，当所需的检测概率较高时，基于LAMPnet的AMP算法在计算复杂度上明显优于传统算法。