We consider the multi-user detection (MUD) problem in uplink grant-free non-orthogonal multiple access (NOMA), where the access point has to identify the total number and correct identity of the active Internet of Things (IoT) devices and decode their transmitted data. We assume that IoT devices use complex spreading sequences and transmit information in a random-access manner following the burst-sparsity model, where some IoT devices transmit their data in multiple adjacent time slots with a high probability, while others transmit only once during a frame. Exploiting the temporal correlation, we propose an attention-based bidirectional long short-term memory (BiLSTM) network to solve the MUD problem. The BiLSTM network creates a pattern of the device activation history using forward and reverse pass LSTMs, whereas the attention mechanism provides essential context to the device activation points. By doing so, a hierarchical pathway is followed for detecting active devices in a grant-free scenario. Then, by utilising the complex spreading sequences, blind data detection for the estimated active devices is performed. The proposed framework does not require prior knowledge of device sparsity levels and channels for performing MUD. The results show that the proposed network achieves better performance compared to existing benchmark schemes.

翻译：本文研究上行免授权非正交多址接入中的多用户检测问题，其中接入点需要识别活跃物联网设备的数量和正确身份，并解码其传输数据。我们假设物联网设备采用复扩频序列，按照突发稀疏模型以随机接入方式传输信息：部分设备以高概率在多个相邻时隙传输数据，而其他设备在一帧内仅传输一次。利用时间相关性，我们提出了一种基于注意力的双向长短期记忆网络来解决多用户检测问题。该双向LSTM网络通过前向和后向LSTM构建设备激活历史模式，而注意力机制为设备激活点提供必要上下文。通过这种方式，在免授权场景下形成检测活跃设备的分层路径。随后利用复扩频序列对估计的活跃设备进行盲数据检测。所提框架无需先验设备稀疏度或信道状态信息即可执行多用户检测。结果表明，与现有基准方案相比，所提网络实现了更优的性能。