  Compressive Sensing has well boosted massive random access protocols over the
last decade. In this paper we apply an orthogonal FFT basis as it is used in
OFDM, but subdivide its image into so-called sub-channels and let each
sub-channel take only a fraction of the load. In a random fashion the
subdivision is consecutively applied over a suitable number of time-slots.
Within the time-slots the users will not change their sub-channel assignment
and send in parallel the data. Activity detection is carried out jointly across
time-slots in each of the sub-channels. For such system design we derive three
rather fundamental results: i) First, we prove that the subdivision can be
driven to the extent that the activity in each sub-channel is sparse by design.
An effect that we call sparsity capture effect. ii) Second, we prove that
effectively the system can sustain any overload situation relative to the FFT
dimension, i.e. detection failure of active and non-active users can be kept
below any desired threshold regardless of the number of users. The only price
to pay is delay, i.e. the number of time-slots over which cross-detection is
performed. We achieve this by jointly exploring the effect of measure
concentration in time and frequency and careful system parameter scaling. iii)
Third, we prove that parallel to activity detection active users can carry one
symbol per pilot resource and time-slot so it supports so-called one-shot
messaging.
  The key to proving these results are new concentration results for sequences
of randomly sub-sampled FFTs detecting the sparse vectors "en bloc".
Eventually, we show by simulations that the system is scalable resulting in a
coarsely 20-fold capacity increase compared to standard OFDM.
