Access Delay Constrained Activity Detection in Massive Random Access

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 ${\Omega }\left( {\log \left( {\frac{n}{k}} \right)} \right)$, the frequency-multiplexed group testing strategy requires $O\left( {k\log \left( {\frac{n}{k}} \right)} \right)$ 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.