  In 5G and future generation wireless systems, massive IoT networks with
bursty traffic are expected to co-exist with cellular systems to serve several
latency-critical applications. Thus, it is important for the access points to
identify the active devices promptly with minimal resource consumption to
enable massive machine-type communication without disrupting the conventional
traffic. In this paper, a frequency-multiplexed strategy based on group testing
is proposed for activity detection which can take into account the constraints
on network latency while minimizing the overall resource utilization. The core
idea is that during each time-slot of active device discovery, multiple
subcarriers in frequency domain can be used to launch group tests in parallel
to reduce delay. Our proposed scheme is functional in the asymptotic and
non-asymptotic regime of the total number of devices $(n)$ and the number of
concurrently active devices $(k)$. We prove that, asymptotically, when the
number of available time-slots scale as $\Omega\big(\log (\frac{n}{k})\big)$,
the frequency-multiplexed group testing strategy requires $O\big(k\log
(\frac{n}{k})\big)$ time-frequency resources which is order-optimal and results
in an $O(k)$ reduction in the number of time-slots with respect to the optimal
strategy of fully-adaptive generalized binary splitting. Furthermore, we
establish that the frequency-multiplexed GT strategy shows significant
tolerance to estimation errors in $k$. Comparison with 3GPP standardized random
access protocol for NB-IoT indicates the superiority of our proposed strategy
in terms of access delay and overall resource utilization.
