AlexNet Classifier and Support Vector Regressor for Scheduling and Power Control in Multimedia Heterogeneous Networks

In this paper, the downlink transmission of a two-tier heterogeneous network (HetNet) is considered in which a macro base station (MBS) serves the macro users using orthogonal frequency division multiple access (OFDMA) and small base stations (SBSs) serve the small-cell users through multi-carrier non-orthogonal multiple access (MC-NOMA) and joint transmission (JT). In particular, assuming the subcarriers are already allocated to macro users, the problem of scheduling (i.e., joint user association and subcarrier allocation) and power control is studied with the goal of maximizing the total users’ perceived quality-of -experience (QoE) for small-cell users, while a minimum data rate for macro users is guaranteed. To solve the joint optimization problem, a near-optimal and computationally efficient two-phase solution approach is proposed based on the tools from optimization and machine learning (ML). In the first phase, the optimization problem is solved to obtain the scheduling decisions and transmit power variables. In the second phase, the optimized scheduling decisions and transmit power variables serve as training samples for an AlexNet classifier and support vector regressor (SVR), respectively. Simulation results reveal that the integration of JT into MC-NOMA, outperforms the conventional MC-NOMA scheme by up to 24%, 19%, and 21% for the web, video and audio multimedia services, respectively. Compared to a conventional convolutional neural network, our results demonstrate that for the web, video, and audio-services, AlexNet increases the scheduling prediction accuracy up to 14%, 11%, and 17%, while SVR increases the power prediction accuracy up to 8%, 7%, and 12%, respectively.