Recently, unmanned aerial vehicles (UAVs) or drones have found a wide range of applications in package delivery, video surveillance, remote sensing, aerial communication platform, and many others, thanks to their flexible deployment and high mobility. Driven by the increasingly prosperous market of UAV, cellular-enabled UAV communication, which leverages the ground base stations (GBSs) in cellular networks, has drawn significant interests currently for realizing high-performance and beyond visual line-of-sight (BVLoS) UAV-ground communications. It is thus envisioned that both the aerial and terrestrial users will exist in the fifth generation (5G) and beyond 5G cellular networks.

As compared to conventional terrestrial communications, one distinct feature of UAV-ground communications is the dominant line-of-sight (LoS) component in the channels between the UAV and the GBSs due to the high altitude of UAVs, which brings both opportunities and challenges in the design of future cellular networks supporting both terrestrial and aerial users. On one hand, the enhanced macro-diversity in association with a large number of GBSs with strong LoS channels with the UAV can be exploited to improve the UAV communication performance, especially when the UAV is equipped with multiple antennas. However, on the other hand, severe co-channel interference also occurs between the UAV and the GBSs serving the terrestrial users over the same time-frequency channel, which may drastically degrade the communication performance of terrestrial users in the uplink and the UAV in the downlink.

To exploit the macro-diversity gain for supporting high-speed UAV-ground communications, we consider the multi-beam transmission strategy where the multi-antenna UAV sends multiple data streams to the GBSs. On the other hand, the interference management issue for the occupied GBSs shares similarity with many classic interference control problems. One example is the cognitive radio network, where each secondary user transmits in the same resource block as the primary users, and thus needs to carefully mitigate its interference to the primary users. The key difference lies in the relationship between the number of transmit antennas and the number of users that need to be protected. In the previous interference control setups such as cognitive radio network, in general, the number of antennas at the secondary user is larger than that of the primary users. As a result, zero-forcing (ZF) beamforming can be designed for secondary communications without generating any interference to the primary communications. However, in our interested UAV communication systems, the number of antennas at the UAV is usally small due to its space and hardware limitations, while the number of occupied GBSs can be very large due to the strong LoS-dominant UAV-ground channels. As a result, in many cases the number of occupied GBSs is much larger than that of the antennas at the UAV, making it infeasible to design the ZF beamforming to totally mitigate the interference at all the occupied GBSs.

Since treating UAV interference as noise does not work, we have proposed two novel non-linear decoding (or interference cancellation) based interference control strategies, driven by the recent advances in cloud radio access network (C-RAN) and non-orthogonal multiple access (NOMA), to copy with the interference control issue in the challenging setup when the number of occupied GBSs is larger than that of the antennas at the UAV. One strategy is the cooperative interference cancellation that leverages the backhaul connections between adjacent GBSs, e.g., X-haul, for message forwarding. For example, in the above figure, if GBS 5 is connected to GBSs 1, 2, and 3, it can forward its decoded messages from the UAV to GBSs 1, 2, and 3 for interference cancellation. Another strategy is the local interference cancellation, i.e., NOMA. Under this strategy, the occupied GBSs can first decode the UAV messages by themselves, and then cancel the corresponding interference before decoding the messages of their served terrestrial users. Via either cooperative or local interference cancellation, the interference from the UAV to terrestrial communications can be significantly reduced.

1. L. Liu, S. Zhang, and R. Zhang, "Multi-beam UAV communication in cellular uplink: cooperative interference cancellation and sum-rate maximization,'' submitted to IEEE Trans. Wireless Commun., 2018.

2. L. Liu, S. Zhang, and R. Zhang, "Exploiting NOMA for multi-beam UAV communication in cellular uplink," to appear in Proc. IEEE International Conference on Communications (ICC), 2019.