  Emerging communication networks are envisioned to support massive wireless
connectivity of heterogeneous devices with sporadic traffic and diverse
requirements in terms of latency, reliability, and bandwidth. Providing
multiple access to an increasing number of uncoordinated users and sharing the
limited resources become essential in this context. In this work, we revisit
the random access (RA) problem and exploit the continuous angular group
sparsity feature of wireless channels to propose a novel RA strategy that
provides low latency, high reliability, and massive access with limited
bandwidth resources in an all-in-one package. To this end, we first design a
reconstruction-free goal-oriented optimization problem, which only preserves
the angular information required to identify the active devices. To solve this,
we propose an alternating direction method of multipliers (ADMM) and derive
closed-form expressions for each ADMM step. Then, we design a clustering
algorithm that assigns the users in specific groups from which we can identify
active stationary devices by their angles. For mobile devices, we propose an
alternating minimization algorithm to recover their data and their channel
gains simultaneously, which allows us to identify active mobile users.
Simulation results show significant performance gains in terms of active user
detection and false alarm probabilities as compared to state-of-the-art RA
schemes, even with limited number of preambles. Moreover, unlike prior work,
the performance of the proposed blind goal-oriented massive access does not
depend on the number of devices.
