Blind Goal-Oriented Massive Access for Future Wireless Networks

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