Placement and power allocation for NOMA-UAV networks

Unmanned aerial vehicles (UAVs) can be used as flying base stations to provide ubiquitous connections for mobile devices in over-crowded areas. On the other hand, non-orthogonal multiple access (NOMA) is a promising technique to support massive connectivity. In this letter, the placement and power allocation (PA) are jointly optimized to improve the performance of the NOMA-UAV network. Since the formulated joint optimization problem is non-convex, the location of the UAV is first optimized, with the total path loss from the UAV to users minimized. Then, the PA for NOMA is optimized using the optimal location of the UAV to maximize the sum rate of the network. Simulation results are presented to show the effectiveness and efficiency of the proposed scheme for NOMA-UAV networks

Resource Allocation for Power Minimization in RIS-assisted Multi-UAV Networks with NOMA

Reconfigurable intelligent surface (RIS) is a promising technique that smartly reshapes wireless propagation environment in the future wireless networks. In this paper, we apply RIS to an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network, in which the transmit signals from multiple UAVs to ground users are strengthened through RIS. Our objective is to minimize the power consumption of the system while meeting the constraints of minimum data rate for users and minimum inter-UAV distance. The formulated optimization problem is non-convex by jointly optimizing the position of UAVs, RIS reflection coefficients, transmit power, active beamforming vectors and decoding order, and thus is quite hard to solve optimally. To tackle this problem, we divide the resultant optimization problem into four independent subproblems, and solve them in an iterative manner. In particular, we first consider the sub-solution of UAVs placement which can be obtained via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we yield the closed-form expression for the RIS reflection coefficients. Subsequently, the transmit power is optimized using standard convex optimization methods. Finally, a dynamic-order decoding scheme is presented for optimizing the NOMA decoding order in order to guarantee fairness among users. Simulation results verify that our designed joint UAV deployment and resource allocation scheme can effectively reduce the total power consumption compared to the benchmark methods, thus verifying the advantages of combining RIS into the multi-UAV assisted NOMA networks

A Deep Learning-Based Approach to Resource Allocation in UAV-aided Wireless Powered MEC Networks

Beamforming and non-orthogonal multiple access (NOMA) are two key techniques for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is mounted with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. We aim to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The considered optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in partial offloading pattern, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we propose an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. A resource allocation algorithm for partial offloading pattern is thereby proposed. Simulation results demonstrate that our designed algorithm yields a significant computation performance enhancement as compared to the benchmark schemes

Non-Orthogonal Multiple Access for mmWave Drones with Multi-Antenna Transmission

Unmanned aerial vehicles (UAVs) can be deployed as aerial base stations (BSs) for rapid establishment of communication networks during temporary events and after disasters. Since UAV-BSs are low power nodes, achieving high spectral and energy efficiency are of paramount importance. In this paper, we introduce non-orthogonal multiple access (NOMA) transmission for millimeter-wave (mmWave) drones serving as flying BSs at a large stadium potentially with several hundreds or thousands of mobile users. In particular, we make use of multi-antenna techniques specifically taking into consideration the physical constraints of the antenna array, to generate directional beams. Multiple users are then served within the same beam employing NOMA transmission. If the UAV beam can not cover entire region where users are distributed, we introduce beam scanning to maximize outage sum rates. The simulation results reveal that, with NOMA transmission the spectral efficiency of the UAV based communication can be greatly enhanced compared to orthogonal multiple access (OMA) transmission. Further, the analysis shows that there is an optimum transmit power value for NOMA beyond which outage sum rates do not improve further