Energy consumption has emerged as a critical concern in next-generation wireless communication networks due to the increasing demand for high data rates and seamless connectivity. Ultra-Dense Networks (UDNs) in fifth-generation (5G) systems have been identified as a promising solution to support this demand by deploying a large number of small cell base stations (SBSs) alongside macro base stations (MBSs). However, the dense deployment significantly increases overall power consumption, especially when SBSs remain active under low traffic conditions caused by user mobility.
To address this issue, this paper proposes a novel adaptive sleep mode optimization framework that integrates traffic prediction with the Grasshopper Optimization Algorithm (GOA). Specifically, historical traffic patterns are analyzed to predict future traffic loads at each base station, and these predicted loads are used as input to the GOA to optimally determine the operational mode (active, light sleep, deep sleep, or off) of SBSs under QoS and coverage constraints. This predictive optimization enables dynamic and energy-efficient network adaptation.
The proposed approach enhances the overall energy efficiency (EE) and spectral efficiency (SE) of a two-tier heterogeneous network. Simulation results demonstrate that the proposed method achieves up to 29% improvement in energy efficiency and 21% improvement in spectral efficiency compared to existing approaches.

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.