A reconfigurable intelligent surface (RIS) aided air-to-ground uplink non-orthogonal transmission framework is investigated for next generation multiple access. Occupying the same spectrum resource, unmanned aerial vehicle (UAV) users and ground users (GUs) are connected to terrestrial cellular networks via the uplink non-orthogonal multiple access (NOMA) protocol. As the flight safety is important for employing UAVs in civil airspace, the collision avoidance mechanism has to be considered during the flight. Therefore, a joint optimization problem of the UAV trajectory design, RIS configuration, and uploading power control is formulated for maximizing the network sum rate, while ensuring the UAV’s fight safety and satisfying the minimum data rate requirements of both the UAV and GU. The resultant problem is a sequential decision making one across multiple coherent time slots. Besides, the unknown locations of obstacles bring uncertainties into the decision making process. To tackle this challenging problem, a sample-efficient deep reinforcement learning (DRL) algorithm is proposed to optimize the UAV trajectory, RIS configuration, and power control simultaneously. Moreover, considering the ambiguous uncertainties in the environment, a distributionally robust DRL algorithm is further proposed to provide the worst-case performance guarantee. Numerical results demonstrate that the two proposed DRL algorithms outperform the conventional ones in terms of learning efficiency and robustness. It is also shown that the network sum rate is significantly improved by the proposed RIS-NOMA scheme compared to the conventional RIS-orthogonal multiple access (OMA) scheme and the case where no RIS is deployed.

The novel framework of an integrated aeronautical communication and radar system (IACRS) to realize spectrum sharing is investigated. A non-orthogonal multiple access (NOMA)-motivated multi-input–multi-output (MIMO) scheme is proposed for the dual-function system, which is able to detect multiple aircraft while simultaneously transmitting dedicated messages. Specifically, NOMA-inspired technology is utilized to enable dual-spectrum sharing. The superposition of communication and radar signals is facilitated in the power domain. Successive interference cancellation (SIC) is employed at the receiver to effectively mitigate inter-function interference. Subsequently, the regularity of the three-dimensional flight track and attitude is exploited to model the air-to-ground (A2G) MIMO channel. Based on this framework, a joint optimization problem is formulated to maximize the weighted achievable sum rate and the sensing signal–clutter–noise ratio (SCNR) while satisfying the rate requirements for message transmission and ensuring the radar detection threshold. An alternative optimization (AO) algorithm is proposed to solve the non-convex problem with highly coupled variables. The original problem is decoupled into two manageable subproblems: transmit beamforming of the ground base station combined with power allocation and receiver beamforming at the aircraft. The penalty-based approach and the successive rank-one constraint relaxation (SROCR) method are developed for iteratively handling the non-convex rank-one constraints in subproblems. Numerical simulations demonstrate that the proposed IACRS framework significantly outperforms benchmark schemes.

The novel framework of an integrated aeronautical communication and radar system (IACRS) to realize spectrum sharing is investigated. A non-orthogonal multiple access (NOMA)- motivated multi-input-multi-output (MIMO) scheme is proposed for the dual-function system, which is able to detect multiple aircraft while simultaneously transmitting dedicated messages. Specifically, NOMA-inspired technology is utilized to enable the dual-spectrum sharing. The superposition of communication and radar signals is facilitated in the power domain, with successive interference cancellation (SIC) employed at the receiver to effectively mitigate inter-function interference. Subsequently, the regularity of three-dimensional flight track and attitude is exploited to model the air-to-ground (A2G) MIMO channel. Based on this framework, a joint optimization problem is formulated to maximize the weighted achievable sum rate and the sensing signal-clutter-noise ratio (SCNR), while satisfying the rate requirements for message transmission and ensuring the radar detection threshold. An alternative optimization (AO) algorithm is proposed to solve the non-convex problem with highly coupled variables. The original problem is decoupled into two manageable subproblems: transmit beamforming of ground base station combined with power allocation and receiver beamforming at the aircraft. The penalty-based approach and the successive rank-one constraint relaxation (SROCR) method are developed for handling the non-convex rank-one constraints in subproblems iteratively. Numerical simulations demonstrate that the proposed IACRS framework significantly outperforms benchmark schemes.