As the idea of a new wireless communication standard (5G) started to circulate around the world, there was much speculation regarding its performance, making it necessary to carry out further research by keeping in view the challenges presented by it. 5G is considered a multi-system support network due to its ability to provide benefits to vertical industries. Due to the wide range of devices and applications, it is essential to provide support for massively interconnected devices. Network slicing has emerged as the key technology to meet the requirements of the communications network. In this paper, we present a review of the latest achievements of 5G network slicing by comparing the architecture of The Next Generation Mobile Network Alliance’s (NGMN’s) and 5G-PPP, using the enabling technologies software-defined networking (SDN) and network function virtualization (NFV). We then review and discuss machine learning (ML) techniques and their integration with network slicing for beyond 5G networks and elaborate on how ML techniques can be useful for mobility prediction and resource management. Lastly, we propose the use case of network slicing based on ML techniques in a smart seaport environment, which will help to manage the resources more efficiently.
Internet of vehicles (IoV) has been developed as a promising technology to improve road safety. However, resource management can be challenging in a congested traffic environment, which can affect the energy efficiency (EE) and spectrum efficiency (SE) in IoV networks. In this paper, we present a novel intelligent resource allocation approach based on deep reinforcement learning to maximize the weighted composite efficiency that incorporates the EE and SE metric subject to latency and reliability constraints of vehicle-to-vehicle (V2V) users. We employ Thompson sampling with double deep Q network to transform the objective function. Moreover, we present a probability-based learning approach to meet the quality of service requirements and to increase the learning ability of the proposed model. The simulation results indicate that the proposed approach maximizes the composite efficiency while satisfying the latency and reliability constraints of V2V users.
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