Channel Allocation and Power Control for Device-to-Device Communications Underlaying Cellular Networks Incorporated With Non-Orthogonal Multiple Access

Abstract

This paper investigates the application of non-orthogonal multiple access (NOMA) and device-to-device (D2D) into the scenario of massive Machine Type Communications (mMTC). Specifically, we first propose a new NOMA-and-D2D integrated network, where NOMA is utilized to deal with the cross-tier and co-tier interference at the base station side. To fully exploit the advantages of the network, we formulate a joint channel allocation and power control problem with the objective to maximize the performance of the D2D communications under the constraints of the rate requirements of the cellular users. For solving the formulated problem efficiently, we first adopt the sequential convex approximation method to solve the channel allocation subproblem, and then transform the power control subproblem into a convex optimization problem. To further reduce the computational complexity, we employ the convolutional neural network (CNN) to devise a resource management framework, where the relation between the system states and the control policies is established by multiple neurons. Finally, simulation results indicate that the convex approximation based algorithm outperforms the other algorithms in terms of utility, sum-rate, and user satisfaction, and the CNN based algorithm achieves orders of magnitude speedup in computational time with only slight loss of performance.

Keywords

• Non-orthogonal multiple access
• device-to-device
• massive machine type communications
• convolutional neural network
• resource management