Resource Allocation in SCMA-Empowered Multi-UAV Transmission System

This research work examines the utilization of sparse code multiple access (SCMA) in improving downlink communication for multiple unmanned aerial vehicles (UAVs) network, whereby SCMA is a disruptive multiple access technique for future massive machine-type communications (mMTC). The goal is to maximize the sum-rate of the ground users by optimizing the UAV three-dimensional deployment location, user-UAV association, SCMA subchannels, power and bandwidth allocation. Due to the complex and non-convex nature of the formulated optimization problem, obtaining the global optimal solution is infeasible. To address this problem, we propose to decompose the overall problem into four manageable subproblems and solve them sequentially. The user clustering and UAV deployment problem are tackled using a modified Kmeans algorithm, while the three proposed methods leveraging the channel state information and frequency reuse address the subchannel assignment subproblem. Further, iterative heuristic algorithms are then developed to optimize power and bandwidth allocation, thereby improving both sum-rate and the outage performance. The simulation results demonstrate significant performance improvements with the proposed methodologies, highlighting the potential of SCMA-assisted UAV networks over traditional orthogonal multiple access techniques.