Efficient offloading and load distribution based on D2D relaying and UAVs for emergent wireless networks

The device to device (D2D) and unmanned aerial vehicle (UAV) communications are considered as enabling technologies of the emergent 5th generation of wireless and cellular system (5G). Consequently, it is important to determine their corresponding performance with respect to the 5G requirements. In particular, we focus on enhancing the offloading and load balancing performance in three directions. In the first direction, we study the achievable data rate of user relay assisting other users in two-tier networks. We propose a novel heuristic communication scheme called device-for-device (D4D). The D4D enables moving users to share their resource by taking advantage of cooperative communication. We study the moving user rate sensitivity to the relay selection and blocking probability. In the second direction, we study the offloading from macrocell to small cell and load balancing among small cell. Also, we design a new utility weight function that enables a balanced relay assignment. We propose a novel low complexity algorithm for centralized scheme maximizing the load among small cells as well as users subject to SINR threshold constraints. The simulations show that our proposed schemes achieve performance in load balancing compared to those obtained with the previous or traditional method. In the third direction, we study the 3D deployment of multiple UAVs for emergent on-demand offloading. We propose a novel on-demand deployment scheme based on maximizing both the operator’s profit and the quality of service. The proposed scheme is based on solving a non-convex problem by combining k-means clustering with pattern search to find the suboptimal location of UAVs. The simulation results show that our proposed scheme maximizes the operator’s profit and improves offloading traffic efficiency. Our global contribution was the development of a scheme to improve the quality of service and the performance in emergent networks through the improvement of the load distribution and resource sharing using D2D and UAV

Fairness aware multiple drone base station deployment

The recent advances in drone technology significantly improved the effectiveness of applications such as border surveillance, disaster management, seismic surveying, and precision agriculture. The use of drones as base stations to improve communication in the next generation wireless networks is another attractive application. However, the deployment of drone base stations (DBSs) is not an easy task and requires a carefully designed strategy. Fairness is one of the most important metrics of tactical communications or a disaster-affected network and must be considered for the efficient deployment of DBSs. In this study, a fairness-aware multiple DBS deployment algorithm is proposed. As the proposed algorithm uses particle swarm optimisation (PSO) that requires significant processing power, simpler algorithms with faster execution times are also proposed and the results are compared. The simulations are performed to evaluate the performance of the algorithms in two different network scenarios. The simulation results show that the proposed PSO-based method finds the three-dimensional locations of DBSs, achieving the best fairness performance with a minimum number of DBSs for deployment. However, it is shown that the proposed suboptimal algorithm performs very close to the PSO-based solution and requires significantly less processing time