5G Demand Oriented Technology Research On Integrated Access And Converge Network

With the development of mobile communication technology,business flow in radio access network increase substantially,traditional mobile access architecture gradually reached its bottleneck.In the next generation of mobile communication,the fifth-generation mobile communication(5 G),people put forward a higher demand for mobile access.In order to break through the bottleneck,achieve 5G demands including much higher transmission speed,high density access,high spectrum efficiency requirements and so on,a new radio access architecture is imperative.In recent years,Cloud Radio Access Network(C-RAN)which is a new architecture based on the open platform and real time cloud infrastructure is widely recognized,and it is becoming one of the effective solution to 5G mobile access.However,C-RAN is still a new architecture and there are a lot of related technologies need to be studied.In this paper,we consider 5G heterogeneous access scenario and constitute a heterogeneous access and converge network architecture based on C-RAN architecture,and integrated the traditional Passive Optical Network(PON)architecture.The heterogeneous access network was controlled by Software-Defined Networking(SDN)and virtualization technology.Then we put forward resource scheduling algorithm,effectiveness evaluation method and fault control scheme based on this architecture.The main innovation points in this paper are summarized as follows:First,we put forward a heterogeneous access and converge network architecture to adapt 5G mobile communication needs,and describe the feature and related technologies for each part in the architecture in detail.Second,we propose a resource scheduling algorithm to save energy consumption based on the characteristics of the proposed architecture in this paper.In the scheduling algorithm,businesses are centralized at few processing units,small particle businesses are dealt directly,and larger businesses will be transferred to internal resources pool,enable internal equipment according to the requirements.Then,we simulated and analyzed the performance of the core of the algorithm.Third,we put forward an effectiveness evaluation system for proposed architecture system,then analysis and calculate the weights of each evaluation index in the evaluation system.Finally,we propose a fault prediction method to prevent equipment fault based on machine learning algorithm.We use real network equipment data proposed by network operators to verify the effectiveness of our proposed method.