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The continuous growth of mobile traffic and limited spectrum resources limits the capacity and data rate. Heterogeneous Networks (HetNet) is a solution with multiple radio interfaces in smartphones to realize such demands. Simultaneous data transfer on Long Term Evolution (LTE) and WiFi has gained attention for data offloading in 5G HetNet. Maintaining the average throughput and minimum delay for LTE users is still a challenge in data offloading owing to the mobility and load in the network. This study explores the benefits of Software-Defined Networking (SDN) based multipath for data offloading schemes for LTE-WiFi integrated networks to maintain the user’s average throughput based on channel quality classification. We classify future link qualities using deep learning models such as Long Short-Term Memory Networks (LSTM) and Bidirectional Long Short-Term Memory Networks (BLSTM). The received signal strength indicator (RSSI) and packet data rate (PDR) are parameters used in BLSTM. The results of the prediction were compared with those of state-of-the-art methods. We obtained a 2.1% better prediction than the state-of-the-art methods. The predicted results were used to offload the data using LTE and WiFi. The performance of HetNet was compared with the state-of-the-art method for average throughput, and with the proposed method, a 6.29% improvement was observed.

The back-haul networks of 5G are formed by heterogeneous links which need to handle massive traffic. The service providers are not able to provide good QoS for their users. The technology like Software Defined Networks(SDN) and Network Slicing helps a little for a service provider to providing QoS for multiple links. The service providers face a challenge in the efficient utilization of resources to fulfill the QoS requirement of users to comply with the growth and thereby increasing the revenue. These problems require an accurate traffic model to determine the steady-state of the system. The proposed model uses an architecture that has the combination of two technologies: SDN and network slicing, which empowers an administrator a flexible, programmable network, and the best management of network resources. Heterogeneous application is well managed by creating multiple logical networks called slicing. The slicing can be modeled using multi-class queuing networks. These technologies encourage service providers to fulfill QoS and revenue growth. To leverage the benefits of these technologies in allocating QoS is to identify the performance of the system, which requires a precise model of traffic to decide the steady-state condition of the framework. In this paper, we focus on SDN and slicing in mobile networks and quantify the performance measure considering an in-band OpenFlow architecture for a single node and homogeneous traffic class, which is further extended to the multi-class heterogeneous class queuing model and analyzed. The results obtained help a service provider to monitor the utilization of resources in every node by every class of core network, which in turn helps to allocate the resources precisely to fulfill QoS requirements.