Joint Channel Allocation and Power Control for Uplink NOMA-Assisted Multi-UAV Networks

The explosive growth of data leads to that the traditional wireless networks cannot enable various quality of service (QoS) communication for cellular-connected multi-UAV (unmanned aerial vehicle) networks. To overcome this obstacle, we solve the joint optimization problem of channel allocation and power control for uplink NOMA-assisted multi-UAV networks. Firstly, we design a mixed integer nonlinear programming framework, where the channel gains are characterized with integral form in time interval and sorted in nondescending order as the priority index of the decoded signal. In order to propose a feasible algorithm, the initial power levels of UAVs are obtained and integrated into the original problem which is reduced to integer programming problem. Then, the UAVs whose channel gain differences satisfy the constraints will be divided into a group to share the same channel, while the initial power levels of UAVs are adjusted to get a more satisfactory initial solution for power control. Combining the solution of channel allocation and the initial power levels, we solve power control problem with asynchronous update mechanism until the power levels of UAVs remain unchanged. Finally, we propose a channel allocation algorithm and a power control algorithm with the asynchronous optimization mechanism, respectively. Simulation results show that the proposed algorithms can effectively improve the network performance in terms of the aggregated rate.

Machine Learning Based User Retention and Channel Allocation: 6G Aspect

Future 6G wireless network will be focused on Artificial Intelligence (AI) based network selection, resource allocation and user satisfaction. A user has multiple options to switch one service provider to another service provider in case of network quality degradation. The new schemes/policies are required to retain their valuable users. This paper proposed supervised machine learning methods such as Decision Tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), etc., to classify and identify the loyal user. The decision tree algorithm has been identified as the best classification technique in order to identify the type of user (loyal, normal, and recent). A threshold-based algorithm is proposed to allocate the resource, particularly to loyal users. The performance of the algorithm is measured in terms of average waiting time (AWT), and the number of particular types of user’s services dropped. Priority is given to the loyal user when only 10% network resource is available. The simulation environment is created by SimPy implemented in Python. The result of the simulation run represents that no loyal user’ services have been interrupted during communication. Loyal users achieved less AWT as 32.51s compare to the normal user and recent user.