Massive connectivity is a key requirement for the Internet of Things (IoT). In practice, the network should be capable of accommodating thousands of devices and meeting their traffic demands. In this article, we consider the access phase for IoT in a mixed-analog-to-digital converter distributed massive multiple-input–multiple-output system, in which users are classified into light-load users and heavy-load users depending on their traffic load requirements. To meet the low-latency and low-cost demands in IoT, the access scheme for both types of users are designed in a grant-free fashion. For users with light-load traffic demands, by formulating the user activity detection (UAD) and channel estimation (CE) into a compressed sensing problem, we provide a low-complexity algorithm solver which requires no prior information. The simulation results verify that the proposed algorithm can effectively detect user activity and estimate channel state information (CSI) between the users and access points (APs). To satisfy the throughput requirements of heavy-load users, after UAD and CE, a two-step dynamic clustering is proposed for coordinated multipoint transmission using the large-scale fading (LSF) information. The impact of quantization noise on LSF estimation is investigated, as well as, a corresponding compensation method and accuracy bound. By detecting the clustering behavior among users in the first step, the complexity of the joint user and AP clustering is substantially reduced. The numerical results reveal that the proposed algorithm can offer significant performance gains in various scenarios with fast convergence.

中文翻译：

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在混合ADC分布式大规模MIMO中实现物联网的大规模连接

大规模连接是物联网（IoT）的关键要求。实际上，网络应能够容纳数千个设备并满足其流量需求。在本文中，我们考虑了混合模数转换器分布式大规模多输入多输出系统中的物联网访问阶段，其中根据用户将其分为轻负载用户和重负载用户交通负荷要求。为了满足物联网中的低延迟和低成本需求，两种类型的用户的访问方案均以免授权方式设计。对于有轻载流量需求的用户，通过将用户活动检测（UAD）和信道估计（CE）公式化为压缩的感知问题，我们提供了一种无需先验信息的低复杂度算法求解器。仿真结果验证了所提算法能够有效地检测用户活动并估计用户与接入点（AP）之间的信道状态信息（CSI）。为了满足重载用户的吞吐量需求，在采用UAD和CE之后，提出了一种采用大规模衰落（LSF）信息的协调多点传输两步动态聚类。研究了量化噪声对LSF估计的影响，以及相应的补偿方法和精度范围。通过在第一步中检测用户之间的群集行为，可以大大降低联合用户和AP群集的复杂性。数值结果表明，所提出的算法可以在各种情况下以快速收敛提供显着的性能提升。