Unsourced and Sourced Massive Connectivity for Airship-Borne Low-Altitude Wireless MIMO Network: Iterative Detection or Deep Unfolding?

The low-altitude wireless networks (LAWN) are envisioned to drive substantial economic benefits by offering integrated sensing, communication, computing and control services. However, the existing terrestrial cellular network cannot provide reliable LAWN coverage for unmanned aerial vehicles (UAVs). The near-space airships present unique advantages in addressing the low-altitude coverage problem and enabling low-latency transmission. Nevertheless, designing communication schemes that support massive connectivity for numerous UAVs and diverse services with heterogeneous requirements in airship-borne LAWNs remains a key challenge. Hence, in this paper, we design a grant-free non-orthogonal multiple access (GF-NOMA) scheme for airship-borne massive multiple-input-multiple-output (MIMO) LAWN. Specifically, we propose a codebook-based non-coherent GF-NOMA scheme with flexible time-frequency resource mapping strategy. The proposed scheme has two operation modes: grant-free unsourced connectivity (GFUC) and grant-free sourced connectivity (GFSC) respectively tailored for data-centric and identity-centric services in the LAWN. Next, we model data detection problems in GFUC and GFSC as a compressive sensing (CS) problem. Furthermore, we design an iterative detection algorithm named cross-domain orthogonal approximate message passing for generalized multiple measurement vector (CD-OAMP-GMMV), where the strong sparsity of wideband massive MIMO channel in the angular-delay domain is exploited. Additionally, we develop a deep unfolding algorithm named cross-domain orthogonal approximate message passing with fixed point network (CD-OAMP-FPN) for reduced computational complexity and enhanced channel estimation performance. Finally, our simulation results show the superiority of the proposed scheme over state-of-the-art GF-NOMA scheme in the airship-borne LAWN. Particularly, the proposed CD-OAMP-GMMV iterative algorithm has higher generalization ability in the GFUC case, while the proposed deep unfolding CD-OAMP-FPN has faster convergence speed and better channel estimation performance in the GFSC case.