Angular Information Based Robust Downlink Transmission for IRS-Enhanced Cognitive Satellite-Aerial Networks

This article proposes a downlink transmission for intelligent reflecting surface (IRS) enhanced cognitive-satellite-aerial-network, which can provide heterogeneous services for various users. The satellite adopts multicast transmission scheme to provide content-aware services for many satellite terminals, while the aerial platform offers connection-centric services for users having line-of-sight links through space division multiple access, and for users locating in blocked aera via IRS-enhanced non-orthogonal multiple access. Assuming that the satellite network and aerial network share the same spectrum, and only the imperfect channel state information is available, we formulate a total transmit power minimization problem subject to the outage probability constraints for users, the per-antenna transmit power budgets of satellite and aerial platform, and unit-modulus requirement for IRS. To tackle this mathematically intractable problem, we propose an alternation-based robust transmission algorithm, combining the central limit theorem, successive convex approximation and penalty function, to optimize the beamformers of satellite and aerial platform, phase shifts and power allocation. Furthermore, we propose a generalized zero-forcing based low-complexity robust transmission algorithm, integrating the second-order Taylor expansion and Bernstein-type inequality, to obtain a satisfactory performance while reducing the computational load. Finally, simulation results validate the effectiveness of the proposed two algorithms and show the superiority to benchmarks.