User activity detection in grant-free random access massive machine type communication (mMTC) using pilot-hopping sequences can be formulated as solving a non-negative least squares (NNLS) problem. In this work, two architectures using different algorithms to solve the NNLS problem is proposed. The algorithms are implemented using a fully parallel approach and fixed-point arithmetic, leading to high detection rates and low power consumption. The first algorithm, fast projected gradients, converges faster to the optimal value. The second algorithm, multiplicative updates, is partially implemented in the logarithmic domain, and provides a smaller chip area and lower power consumption. For a detection rate of about one million detections per second, the chip area for the fast algorithm is about 0.7 mm ² compared to about 0.5 mm ² for the multiplicative algorithm when implemented in a 28 nm FD-SOI standard cell process at 1 V power supply voltage. The energy consumption is about 300 nJ/detection for the fast projected gradient algorithm using 256 iterations, leading to a convergence close to the theoretical. With 128 iterations, about 250 nJ/detection is required, with a detection performance on par with 192 iterations of the multiplicative algorithm for which about 100 nJ/detection is required.

中文翻译：

---

基于非负最小二乘的无授权随机访问的大规模机器类型通信飞行员跳变序列检测架构

可以将使用导频跳变序列的无授予随机访问大规模机器类型通信（mMTC）中的用户活动检测公式化为解决非负最小二乘（NNLS）问题。在这项工作中，提出了两种使用不同算法来解决NNLS问题的体系结构。这些算法使用完全并行的方法和定点算法实现，从而实现了高检测率和低功耗。第一种算法是快速投影梯度，它可以更快地收敛到最佳值。第二种算法，乘法更新，在对数域中部分实现，并提供了较小的芯片面积和较低的功耗。对于每秒约一百万次检测的检测速率，快速算法的芯片面积约为0.7 mm ²与在28 nm FD-SOI标准单元工艺中以1 V电源电压实施时的乘法算法约0.5 mm ²相比 。对于使用256次迭代的快速投影梯度算法，能耗约为300 nJ /次，导致收敛接近理论值。在128次迭代中，大约需要250 nJ /次检测，而与192次迭代的乘法算法相比，其检测性能大约需要100 nJ /次。