  Recently, non-orthogonal codes have been advocated for IoT massive access.
Activity detection has been demonstrated to entail common support recovery in a
jointly sparse multiple measurement vector (MMV) problem and MMV algorithms
have been successfully applied offering various degrees of
complexity-performance trade-off. Targeting the small measurement per antenna
but large number of antennas setup, independent sensing matrices do offer
significant performance advantages. Unfortunately, the IoT random access
problem can not readily benefit from this concept as code matrix is fixed over
all receiving antennas. Our contributions towards addressing this challenge are
as follows. First, independent small-scale fading across antennas and users is
established as a possible source of sensing matrix decorrelation. Secondly, two
novel algorithms are proposed which exploit this partial de-correlation and
collect sensing matrix diversity. Enjoying a low-complexity, these methods do
offer great practical advantages as they target small measurement size, which
is indeed severely constrained due to limited coherence time/bandwidth, but
instead compensate for it by using a large array of antennas. 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.
