Artificial Neural Network based Power Control in D2D Communication

As a viable technique for next-generation wireless networks, Device-to-Device (D2D) communication has attracted interest because it encourages the usage of point-to-point communications between User Equipment (UE) without passing over base stations (BS). Device-to-device (D2D) communication has been proposed in cellular networks as a supplementary paradigm to primarily increase network connection. This research takes into account a cellular network where users are trying device-to-device (D2D) connection. A D2D pair is composed of two D2D users (DUEs), a transmitter, and a receiver. To improve spectral efficiency, we use the premise that the D2D pairs only employ one communication channel. In order to minimize interference between D2D pairs and increase capacity, a power control is required. In the scenario where only typical cellular channel gains between base stations and DUEs are known and channel gains among DUEs are completely inaccessible, we address the issue of D2D power control. For each individual D2D pair, we use an artificial neural network (ANN) to calculate the transmission power. We show that the maximum aggregate capacity for the D2D pairs may be reached while anticipating the transmission power setting for D2D pairs using cellular channel gains.