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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. INDEX TERMS 5G mobile communication, base stations, Internet of Things, machine-to-machine communications, MIMO.

In this work, an approach for transposing solutions to the multiple constant multiplication (MCM) problem to obtain a sum of product (SOP) computation with minimum depth is proposed. The reason for doing this is that solving the SOP problem directly is highly computationally intensive when adder graph algorithms are used. Compared to using sub-expression sharing algorithms, which has a lower computational complexity, directly for the SOP problem, it is shown that the proposed approach, as expected, results in lower complexity for the SOP. It is also shown that there is no obvious way to construct the MCM solution in such a way that the SOP solution has the minimum theoretical depth. However, the proposed approach guarantees minimum depth subject to the MCM solution given as input.Index Terms-multiple constant multiplication (MCM), shiftand-add, Sum of Product (SOP), minimum depth expansion algorithm, and Vertex reduced SOP adder Graph (VSG).

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