Massive Random Access in Massive MIMO via Opportunistic Thresholding

Non-orthogonal codes have been recently applied for massive IoT random access to a massive MIMO base station. Activity detection for this extension of on-off random access channel yields a jointly sparse multiple measurement vector (MMV) problem. However, no one investigated the regime when measurements per antenna are very limited but number of antennas is extremely large. Our contributions towards addressing this problem are as follows. Firstly, motivated by trivial pursuit which performs well with independent sensing matrices, we designate independent small-scale fading across antennas and users as a possible source of sensing matrix de-correlation. Secondly, two novel algorithms are proposed which exploit this partial de-correlation and collect sensing matrix diversity. Thirdly, probability of failure (PoF) for these methods are rigorously derived and corresponding measurement inequalities are presented. Fourthly, extensive simulations are conducted to confirm the superior performance of these methods versus state of the art in the aforementioned regime.