# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 1 Introduction
Since D2D users share spectrum resources with cellular users, interference is one of the major challenges in D2D link scheduling. Interference may be not only experienced between D2D users operating within the same area but, D2D transmission may cause cross-layer interference phenomena also involving cellular users. The interference occurring in D2D communication can be modeled by using both a graph-based model and a physics-based one. The maximum D2D links problem has been proven to be NP-hard in both models [10]. Most research is based on the graph model (e.g., [11; 12; 13; 14; 15], but such model is too simplistic to simulate how the signal strength attenuation is affected by distance for D2D users which share the same cellular spectrum resources. On the contrary, the physics-based model adopted in this paper reflects the physical reality in a more precise and realistic way. In real production networks, scheduling decisions must be taken online and in real-time so that the available optimization strategies aiming at solving offline the NP-hard linear programming-based problem formulations are not acceptable at all. In order to satisfy the strict delay demands of online operations, the complexity of a link scheduling algorithm must be significantly reduced, eventually applying some heuristic strategy leading to sub-optimal but yet acceptable results. However, this may affect network performance, depending on the achieved distance from the optimum solution. Therefore, developing efficient and effective heuristic-based strategies to solve the link scheduling problem becomes a really challenging task when implementing D2D communication in cellular network scenarios. In recent years, machine learning has emerged as a successful approach for coping with complex problems and has been widely applied to many fields, such as hyperspectralimage classification, ship detection, and wireless communications [16; 17; 18; 19]. With the success of massive parallel computing environments, also empowered by the use of graphics processing unit (GPU)-based acceleration frameworks, machine learning has emerged as a successful approach for coping with complex problems in the wireless communications area. In particular, deep learning, a promising subset of machine learning, has been applied to wireless networks optimization and big data processing. The purpose of deep learning is to simulate complex functions through a predefined model consisting of neuron units. In the deep learning training process, appropriate weight values are determined through calculation and tuning between neurons, a process which has the goal of extracting knowledge/information, in form of specific features from input data. Then the trained-model is able to make accurate classification or prediction decisions based on such features [20]. Unlike traditional machine learning methods that heavily rely on relevant domain experts to extract features from data, the deep learning model automatically acquires a sample feature through multiple hidden layers consisting of neuron units to achieve classification or prediction [21]. In this paper, we leverage the physical interference model and develop a novel machine learning-based approach for achieving the maximum number of D2D links within a cellular coverage area. We use deep neural network (DNN)-derived features from D2D link information to make time-efficient and near-optimal decisions in the operation phase through a real-time optimization strategy driven by the neural network itself. The DNN model is trained by pre-computed samples to find out the complex relationship between link information and D2D link activation. In doing this, we consider a more realistic and accurate signal-to-interference-plus-noise ratio (SINR) interference model that considers both cellular users and D2D users. We also accomplish extensive simulations to assess the performance of the DNN-based method and highlight its effectiveness.