Signalling load reduction in 5G network based on cloud radio access network architecture

The rapid growth of both mobile users and application numbers has caused a huge load on the core network (CN). This is attributed to the large numbers of control messages circulating between CN entities for each communication or service request, however, making it imperative to develop innovative designs to handle this load. Consequently, a variety of proposed architectures, including a software defined network (SDN) paradigm focused on the separation of control and data plans, have been implemented to make networks more flexible. Cloud radio access network (C-RAN) architecture has been suggested for this purpose, which is based on separating base band units (BBU) from several base stations and assembling these in one place. In this work, a novel approach to realize this process is based on SDN and C-RAN, which also distributes the control elements of the CN and locates them alongside the BBU to obtain the lowest possible load. The performance of this proposed architecture was evaluated against traditional architecture using MATLAB simulation, and. the results of this assessment indicated a major reduction in signalling load as compared to that seen in the traditional architecture. Overall, the number of signalling messages exchanged between control entities was decreased by 53.19 percent as compared to that seen in the existing architecture.

Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units and are separated from the remote radio heads (RRHs). Based on their radio capacity, the RRHs may form one or many clusters: a single cluster when all RRHs have the same capacity and multi-clusters where RRHs of the same radio capacity are grouped in the same cluster. Each RRH services the so-called multiservice traffic, i.e., calls from many service classes with various radio and computational resource requirements. Calls arrive in the RRHs according to a quasi-random process. This means that new calls are generated by a finite number of mobile users. Arriving calls require simultaneously computational and radio resource units in order to be accepted in the system, i.e., in the serving RRH. If their requirements are met, then these calls are served in the (serving) RRH for a service time which is generally distributed. Otherwise, call blocking occurs. We start with the single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time congestion probabilities via a convolution algorithm whose accuracy is validated with the aid of simulation. Furthermore, the previous model is generalized to include the more complex case of more than one clusters.

Improved cloud radio access network based fair network model in internet pricing

In this study, the pricing scheme that will be formed is a model from the previous research model involving model of cloud-radio access network (C-RAN) and fair network management models. This model combines the benefits of internet service provider (ISP) and service quality (QoS) obtained by internet users, one of which is fair network factors. The model used is a nonlinear equation and is solved by the LINGO 13.0 program to get the optimal solution. The results show that the pricing scheme with regard to service quality generates maximum revenue for ISPs. Based on the improved C-RAN model that are classified into 2 cases, the optimal results in the improved model, the optimal value is found in the pricing scheme in case 1 of by conducting numerical computation using hotspot traffic from local server.

Cloud radio access network fronthaul solution using optimized dynamic bandwidth allocation algorithm

In order to address the challenges that have come with the exploding demand for higher speed, traffic growth and mobile wireless devices, Mobile network operators have decided to move to the notion of small cells based on cloud radio access network. The merits of cloud based RAN includes the ease of infrastructure deployment and network management as well as the fact that its performance are optimized and it is cost effective the merits of cloud based RAN includes the ease of infrastructure deployment and network management as well as the fact that its performance are optimized and it is cost effective. Notwithstanding, cloud radio access network comes with so many strict requirements to be fulfilled for its fronthaul network. In this paper, we have presented these requirements for a 5G fronthaul network. Particular interest on the time division multiplex passive optical network’s challenge of latency was treated by proposing an optimized version of the round robin dynamic bandwidth allocation algorithm. Results obtained show an improvement in the latency of the original algorithm which meets the fronthaul requirement. Other test parameters like jitter and BER were also improved by our proposed optimized algorithm.