Abstract
Since traffic in networks is growing rapidly, it is difficult for the existing network architecture to support the huge traffic requirement. This article proposes a novel intelligent architecture as a promising paradigm for B5G heterogeneous networks to optimize network resource usage and network performance. The main idea is to build a suitable network model through AI, and integrate edge computing and cloud computing to improve computing performance. In addition, this article gives appropriate recommendations of the deep learning method for different network issues. Since the deep learning method requires a large amount of computing resources, the network resource allocation needs to be paid attention to in this architecture. For complex environments of B5G heterogeneous networks, integrated packet forwarding is one potential technology to improve quality of service. Moreover, we discuss the challenges and open issues for B5G.
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