# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 1 Introduction  
In the last decade, the diffusion of smartphones, tablets, and other smart devices has led to an astonishing demand for ubiquitous mobile network access, and the rise of online services, such as video streaming, social network applications, and mobile gaming, has significantly increased the traffic load characterizing wireless communications. Typically, the same popular data and content may be requested multiple times at the same location by different users/devices, which results in an unnecessary waste of backhaul capacity and spectrum resources. In fact, the main cause of these problems is due to video on demand contents accessed in an asynchronous way (unlike live streaming and digital TV), so that the demands concentrate on a small set of popular contents that are requested by a significant number of devices, often located in the same cellular coverage area [1]. To solve these problems, it is possible to reconsider the current network architecture and explore more advancedcommunication models and paradigms. D2D direct communication is considered one of the most promising technological issues in next generation cellular networks [2]. It consists of establishing a direct communication channel between two nearby mobile users, sharing a common short-range radio coverage space, without traversing a base station (BS) or core network equipment [3]. It results in a very flexible communication model with unique advantages. First, due to its underlying short-range direct communication technology, D2D user equipment (UE) allows for higher transfer rates together with very limited end-to-end delays. Second, any direct proximity-based transmission that does not traverse centralized channel collection points (and potential bottlenecks) in the cellular infrastructure, such as the evolved Node B (eNB) in LTE networks, may be more resource-efficient than conventional cellular communication by saving energy and channel capacity, as well as improving spectrum utilization. Third, data are transferred locally, which is helpful for offloading cellular traffic and alleviating backhaul crowding [4]. To enhance the efficiency of content sharing and reduce the load on the eNB, it is important to maximize the number of simultaneous D2D transmissions, each associated to an active communication channel or link, within a specific coverage area. In particular, maximizing the potential number of D2D links within a cellular coverage area not only improves the spectrum and energy efficiency but also reduces transmission delay. However, such a maximization process poses three major challenges. First, interference between cellular and D2D users could critically affect their performance. Second, the minimum quality of service (QoS) requirements must be simultaneously guaranteed for both cellular and D2D communications. Therefore, the selection of suitable active links in each time interval (or slot, in presence of a slotted time evolution model) is fundamental to achieve the maximum number of simultaneous D2D links. Third, a highly flexible and scalable network infrastructure needs to support a large number of heterogeneous users as well as the deployment and interworking of a multiplicity of combined technologies, such as the ones characterizing the Internet of Things (IoT) and vehicular ad-hoc communications [5; 6; 7], so that the degree of complexity characterizing such infrastructure, both in terms of variables and degrees of freedom in the associated model, makes the traditional approaches provided by optimization theory more challenging than ever [3]. The research of scheduling for network resources also causes great concern about computational efficiency [8; 9]. The D2D links scheduling problem, aiming at maximizing the number of active D2D links, consists of determining which D2D pairs can communicate simultaneously (by using direct channels) while guaranteeing that the remaining communications between cellular users are not affected.