The unsourced MAC model was originally introduced to study the communication scenario in which a number of devices with low-complexity and low-energy wish to upload their respective messages to a base station. In the original problem formulation, all devices communicate using the same information rate. This may be very inefficient in certain wireless situations with varied channel conditions, power budgets, and payload requirements at the devices. This paper extends the original problem setting so as to allow for such variability. More specifically, we consider the scenario in which devices are clustered into two classes, possibly with different SNR levels or distinct payload requirements. In the cluster with higher power,devices transmit using a two-layer superposition modulation. In the cluster with lower energy, users transmit with the same base constellation as in the high power cluster. Within each layer, devices employ the same codebook. At the receiver, signal groupings are recovered using Approximate Message Passing(AMP), and proceeding from the high to the low power levels using successive interference cancellation (SIC). This layered architecture is implemented using Coded Compressed Sensing(CCS) within every grouping. An outer tree code is employed to stitch fragments together across times and layers, as needed.This pragmatic approach to heterogeneous CCS is validated numerically and design guidelines are identified.

中文翻译：

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异构无源MAC的探索

最初引入无源MAC模型来研究通信场景，在该场景中，许多具有低复杂度和低能耗的设备希望将其各自的消息上载到基站。在最初的问题表述中，所有设备都使用相同的信息速率进行通信。在某些无线情况下，设备的信道状况，功率预算和有效负载要求各不相同，这可能会非常低效。本文扩展了最初的问题设置，以允许这种可变性。更具体地说，我们考虑这样一种场景：设备被分为两类，可能具有不同的SNR级别或不同的有效负载要求。在功率较高的群集中，设备使用两层叠加调制进行传输。在能量较低的集群中 用户以与高功率集群相同的基本星座进行传输。在每一层中，设备采用相同的密码本。在接收器处，使用近似消息传递（AMP）恢复信号分组，并使用连续干扰消除（SIC）从高功率级到低功率级进行恢复。此分层体系结构是在每个分组中使用编码压缩感知（CCS）来实现的。根据需要使用外部树代码将片段跨时间和层缝在一起。此实用的异构CCS方法在数值上得到了验证，并确定了设计准则。然后使用连续干扰消除（SIC）从高功率级到低功率级。此分层体系结构是在每个分组中使用编码压缩感知（CCS）来实现的。根据需要使用外部树代码将片段跨时间和层缝在一起。此实用的异构CCS方法在数值上得到了验证，并确定了设计准则。然后使用连续干扰消除（SIC）从高功率级到低功率级。此分层体系结构是在每个分组中使用编码压缩感知（CCS）来实现的。根据需要使用外部树代码将片段跨时间和层缝在一起。此实用的异构CCS方法在数值上得到了验证，并确定了设计准则。