The increased number of cellular network subscribers is giving rise to the network densification in next generation networks further increasing the greenhouse gas emission and the operational cost of network. Such issues have ignited a keen interest in the deployment of energy-efficient communication technologies rather than modifying the infrastructure of cellular networks. In cellular network largest portion of the power is consumed at the Base stations (BSs). Hence application of energy saving techniques at the BS will help reduce the power consumption of the cellular network further enhancing the energy efficiency (EE) of the network. As a result, BS sleep/wake-up techniques may significantly enhance cellular networks' energy efficiency. In the proposed work traffic and interference aware BS sleeping technique is proposed with an aim of reducing the power consumption of network while offering the desired Quality of Service (QoS) to the users. To implement the BS sleep modes in an efficient manner the prediction of network traffic load is carried out for future time slots. The Long Short term Memory model is used for prediction of network traffic load. Simulation results show that the proposed system provides significant reduction in power consumption as compared with the existing techniques while assuring the QoS requirements. With the proposed system the power saving is enhanced by approximately 2% when compared with the existing techniques. His proposed system will help in establishing green communication networks with reduced energy and power consumption.

The massive connections and the real time control applications have different requirement on delay, energy, rate and reliability of the system. In order to meet the diversified 5G requirements, network slicing technique guarantees on the wide scale applications. In this paper, we have proposed a dynamic resource allocation system with two time scale. The one time scale is used for the resource allocation in the system and the other is used for optimized use of latency and power. Lyapunov drift function is used for the balance between the power consumption and the user satisfaction. Further, Grey Wolf Optimization (GWO) is used for the resource allocation strategy so as to gain the reliability of the system with heterogeneous requirements. The proposed methodology shows the improvement of 27% in user satisfaction and 17.5% in power consumption. The proposed framework can be utilized for the rate as well as latency sensitive applications in 5G.