Channel Allocation and Power Control for Device-to-Device Communications Underlaying Cellular Network Incorporated with Deep Learning Assistance
My M.S. Thesis investigates the application of device-to-device (D2D) communication into the scenario of massive Machine Type Communication (mMTC). We formulate a joint channel allocation and power control problem with the objective to maximize the energy efficiency of the multi-cell cellular system under the constraints of the minimum rate requirements of the CUEs and D2D pairs. For solving the formulated problem efficiently, the convex approximation (CA) based algorithm is proposed. We first adopt the Dinklebach's method, and then successively approximate the problem into a convex optimization problem. To further reduce the computational complexity, a convolutional neural network (CNN) based algorithm is developed to devise a resource management framework, where the relation between the system states and the control policies is established by multiple neurons. The simulation results show that the CNN based algorithm can achieve order of magnitude speedup in computational time with only slight loss of performance.