# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 2 Related Work  
Link scheduling plays a crucial role in meeting strict QoS requirement in wireless networks. Maximum link scheduling is an important sub-problem of link scheduling. The authors of [10] proved for the first time that link scheduling under a SINR model is NP-hard and proposed an algorithm for the one-shot link scheduling problem. The algorithm first separates the problem instance into disjoint link classes and then schedules each link class by using a greedy method. The work in [22] firstly explored a one-slot algorithm for maximizing the number of links with a constant approximation guarantee, and then an extended algorithm with \(O(logn)\) (\(n\) is total number of links) computational complexity was introduced for a minimized-length schedule. However, none of the above papers considered the ambient noise. A method based on partitioning was presentedin [23] to find the independent link set with the maximum weight. In [24], a distributed greedy algorithm for maximizing the number of links, subject to interference constraints was proposed. In these approaches, the improvements achieved in terms of optimization quality could result in a hugely higher computational complexity and, hence, an increment in overall runtime performance. A low-complexity scheduling scheme named DistGreedy based on a link-conflict graph has been proposed in [25]. The algorithm repeatedly removed the active links and blocked links in contention slots until the graph was left empty. The authors of [26] attempted to find a maximum-weighted subset of communications without spectral splitting at the individual time unit. In this model, \(A\) denotes a set of communication tasks, and each request, \(a\in A\), has a demand \(d(a\ )\in(0,1]\). If \(d(a\ )\in(1/2,1]\), it is a heavy request, otherwise it is a light request. The algorithm considered both heavy requests and light requests of \(A\) for determining the link schedule. In [27], a maximum tolerance and minimum (MTMA) model based on a greedy schedule was studied for maximum link scheduling in one-slot. The experiments showed that the algorithm improved 28-64% of the current algorithm. The authors in [28] developed a distributed greedy heuristic for a \(k\)-hop link schedule. Because of interference from two layers, the algorithm for the D2D links schedule was much more complicated than above schemes.  
With the development of the IoT, due the increased complexity of the network structure, link scheduling experiences new difficulties in meeting QoS requirements when using a traditional optimization method. In recent years, deep learning, has been promisingly applied to wireless networks optimization and big data processing [21; 29; 30]. In [31], the authors considered solving a resource allocation problem for D2D communication using a Q-learning-based method. In [32], the authors applied deep learning to reduce the complexity of solving a wireless networks optimization problem. The authors in [33] proposed a resource allocation strategy using cooperative reinforcement learning. The target of their algorithm was to maximize the throughput of the system by selecting the proper level of power for the resource blocks of the cellular user and D2D pairs. In [34], the authors adopted deep learning to solve the objective function of maximizing the weighed sum rate over \(N\) D2D users and demonstrated that link scheduling does not necessarily require the exact channel estimates. In [35], the author adopted a method based on reinforcement learning to solve the resource scheduling for vehicle-to-vehicle (V2V) communications based on D2D. In this article, each vehicle, regarded as an agent, made decisions by itself. Since they do not require global information, decentralized scheduling methods are characterized by a low overhead. Experiments have shown that the proposed algorithm can effectively schedule limited links with minimized interference under delay constraints. Solving the maximum active D2D links problem under interference constraints by deep learning is promising.