Massive Machine-Type Communication Pilot-Hopping Sequence Detection Architectures Based on Non-Negative Least Squares for Grant-Free Random Access

Abstract

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.

Approximate Floating-Point Operations with Integer Units by Processing in the Logarithmic Domain

“…[15], [28], using low-precision Mitchell multipliers only provide a minor deviation from the binary32 results in the considered CNNs. Furthermore, in [6], unbiased Mitchell multiplication did not deteriorate the results compared to using a look-up table for the logarithm and exponentiation.…”

“…Compared to the reciprocal function, one less correct mantissa bit is obtained as the worst case for maximum norm. In [6], division was also used in the logarithmic domain and as mentioned above unbiased Mitchell approximations sufficed. Due to space constraints, no error plots are provided for division.…”

“…Logarithmic representations have been used in e.g. machine learning [5] and massive machine type communication activity detection [6].…”

Activity Detection in Distributed Massive MIMO With Pilot-Hopping and Activity Correlation

“…In this paper, we generalize the transmission model proposed in [4], [6] and implemented in [7] where the devices transmit pilot-hopping sequences in order to aid in activity detection and channel estimation, see Fig. 1.…”

“…Since, there is a huge number of devices in the system, they cannot be assigned orthogonal pilots in each coherence interval. There are two strategies to solve this problem: the first is to assign nonorthogonal pilots [5], and the second is to assign orthogonal pilots in each coherence interval, but have pilot-hopping sequences that span multiple coherence intervals [4], [6], [7].…”