Autoencoder-based efficient resource allocation in device-to-device communication

Abstract

Device-to-device (D2D) communication offloads the base station (BS) traffic, enhances spectral efficiency, and provides efficient data transmission with low latency between devices in close proximity. However, there are many issues in D2D communication, such as mode selection, device discovery, security, privacy, resource allocation, and interference management. At the time of the resource allocation, there is an issue of interference and eavesdropping by the malicious user on D2D pairs. Here, an eavesdropper can decrypt the data exchanged between the D2D user devices. This paper focuses on the efficient resource allocation in D2D communication that improves sum rate, computational complexity, and information secrecy capacity by reusing cellular user’s (CU) resources. Here, we propose an efficient resource allocation scheme incorporating the artificial intelligence (AI) algorithm-based autoencoder that overcomes information ambiguity when there is the same weight in the data rate matrix of the Hungarian algorithm. First, we identify cellular and D2D users’ communication data rates and generate a data rate matrix using the channel gain, received signal, and signal-to-interference-plus-noise ratio (SINR) values. The generated data rate matrix is passed as an input into an autoencoder. An autoencoder performs the dimensionality reduction using latent space and reconstructs the optimal cost matrix. The AI model’s result shows that the autoencoder achieves 83% training and 90% validation accuracy using the mean squared error (MSE) loss function. Then, we determine the optimal cost using the Hungarian algorithm. As we aim to maximize the sum rate and reduce the computation cost, our simulation result shows that the resource allocation performs efficiently using the proposed scheme.