Novel distributed beamforming algorithms for heterogeneous space terrestrial integrated network

An integrated space-terrestrial network based on the ultra-dense low-earth-orbit (LEO) satellite constellations has been envisioned in both 5G and beyond 5G (B5G) networks. This approach is a powerful solution to some key challenges from Internet-of-Thing (IoT) services, such as the lack of link capacity to deal with large data transfer or coverage in the remote areas. This paper focuses on the beamforming design for the transmissions from multiple LEO satellites, equipped with massive phased array antenna, to a large number of heterogeneous terrestrial terminals. Superposition coding based beamforming is efficient in dealing with the receiver heterogeneity, but at the cost of higher computational complexity. Based on the dual decomposition theory as well as deep-neural-networks (DNNs), this paper proposes to combine the non-linear approximation ability of DNNs with distributed algorithms. This combination not only supports advanced non-orthogonal beamforming algorithms for achieving superior throughput performance, but also keeps the overall computational complexity low and enables the beamforming process to be speed up dramatically through parallel computing.