Multi-Class Unsourced Random Access via Coded Demixing

Abstract

Unsourced random access (URA) is a recently proposed communication paradigm attuned to machine-driven data transfers. In the original URA formulation, all the active devices share the same number of bits per packet. The scenario where several classes of devices transmit concurrently has so far received little attention. An initial solution to this problem takes the form of group successive interference cancellation, where codewords from a class of devices with more resources are recovered first, followed by the decoding of the remaining messages. This article introduces a joint iterative decoding approach rooted in approximate message passing. This framework has a concatenated coding structure borrowed from the singleclass coded compressed sensing and admits a solution that offers performance improvement at little added computational complexity. Our findings point to new connections between multiclass URA and compressive demixing. The performance of the envisioned algorithm is validated through numerical simulations.

Stochastic Binning and Coded Demixing for Unsourced Random Access

“…Recently, the CCS scheme was adapted to serve multiple classes of devices [9]: under this alternate formulation, devices with different transmit powers or message lengths can be processed simultaneously over a same channel. To achieve concurrent transmission, each class of users employs its own dictionary for CS encoding, and compressed demixing techniques are subsequently used to recover the messages from each class [10]- [12].…”

“…To achieve concurrent transmission, each class of users employs its own dictionary for CS encoding, and compressed demixing techniques are subsequently used to recover the messages from each class [10]- [12]. This article improves the state-of-the-art CCS formulation in [6] by incorporating ideas from multi-class CCS [9], where sampling matrices with low cross-coherence are utilized to separate classes. Specifically, we demonstrate that significant performance improvements can be obtained by having active devices stochastically partition themselves into groups before encoding their messages.…”