# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
## 1 Introduction  
The integration between computing and networking environments has been widely promoted for intelligent service provisioning to end users and effective resource usage for the next generation of manufacturing. This convergence paves the way toward the information and communication technologies (ICT) revolution, denoted by IoT. IoT is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, sensors, actuators, and connectivity which enables these things to connect, collect and exchange data [1]. The IoT paradigm is changing the way people interact with things around them and our everyday life for activities, tasks and rituals in an easy using information and intelligence hidden in the network linking the things [2]. This pervasive paradigm of IoT increases the value of information generated by many interconnections between people-things and things-things and the transformation of the processed information into knowledge for the benefit of mankind and industries. IoT is leading to the substantial deployment of ubiquitous computing with many applications built around various types of sensors and actuators. During the past decade,most issues at device levels have been solved and nowadays, there is a growing trend in the integration of sensors and novel communication technologies such as D2D for information-based smart systems [3]. IoT is taking the center stage as the devices are expected to form a significant portion of the 5G network that is on the horizon. IoT communication through D2D will complement intelligent data analysis expected to drastically change the landscape of various industries and our daily lives [4]. In other words, the proliferation of connected smart devices with receiving the boost in data analysis via artificial intelligence (AI) technologies leads to further innovation of manufacturing into the fourth industrial revolution and intelligent living areas such as smart homes and communities. This paper brings up a key point of technical issues to realize the integration vision of IoT and the 5G: Context-awareness [5]. To provide adequate intelligent services, systems should be aware of information on surrounding service components and their present status, and automatically adapt to dynamic situations. Data sensing and acquisition are the first main functionality and cognitive computing provides information and intelligence for smart applications. During/after service provisioning, service components also deliver feedbacks as improving context. Each element and process are adopted on different devices as a separated service component or embedded into one device according to the application depth from global adaptation to very local controls in a service domain. The context-aware service loop can be applied to D2D based social networking [6], smart home with connectable intelligent appliances [7], next generation manufacturing via machine-type D2D networking [8], self-driving cars with cameras and huge numbers of sensors, and so on. Those applications are composed of highly heterogeneous devices, especially for mobility and computation capacity [9]. It means that the context-aware smart application system should rely on dynamically-reforming D2D networks that easily harm the fundamental requirements of smart applications such as real-time collaboration and sustainability. In the 5G network systems, as a key component of Industry 4.0, robotics and autonomous systems require eMBB, mMTC, and URLLC. The first 5G standards, called Release 15 New Radio (NR), only support some level of flexibility and scalability of configurations as a general framework [10]. Although the 5G NR supports some requirements, URLLC cannot be guaranteed under the 5G NR defined by the 3 rd Generation Partnership Project (3 GPP) [11]. Thus, it should be applicable for limited use cases and optimized.