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In 5G and future generation wireless systems, massive IoT networks withbursty traffic are expected to co-exist with cellular systems to serve severallatency-critical applications. Thus, it is important for the access points toidentify the active devices promptly with minimal resource consumption toenable massive machine-type communication without disrupting the conventionaltraffic. In this paper, a frequency-multiplexed strategy based on group testingis proposed for activity detection which can take into account the constraintson network latency while minimizing the overall resource utilization. The coreidea is that during each time-slot of active device discovery, multiplesubcarriers in frequency domain can be used to launch group tests in parallelto reduce delay. Our proposed scheme is functional in the asymptotic andnon-asymptotic regime of the total number of devices $(n)$ and the number ofconcurrently active devices $(k)$. We prove that, asymptotically, when thenumber 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 resultsin an $O(k)$ reduction in the number of time-slots with respect to the optimalstrategy of fully-adaptive generalized binary splitting. Furthermore, weestablish that the frequency-multiplexed GT strategy shows significanttolerance to estimation errors in $k$. Comparison with 3GPP standardized randomaccess protocol for NB-IoT indicates the superiority of our proposed strategyin terms of access delay and overall resource utilization.

Access Delay Constrained Activity Detection in Massive Random Access