Optimized energy management and small cell activation in ultra-dense networks through a data-driven approach

Abstract

With the exponential expansion of the wireless industry, the demand for improved data network throughput, capacity, and coverage has become critical. Heterogeneous ultra-dense networks (UDNs) have emerged as a promising solution to meet these escalating requirements for high data rates and capacity. However, effectively deploying and managing small cells within UDNs presents significant challenges, particularly amidst varying traffic loads and the necessity for efficient resource utilization to minimize energy consumption, especially in environments with high interference levels. Inadequate deployment of small cells can lead to excessive interference, resulting in suboptimal profitability and inefficient energy resource utilization. Addressing these challenges demands innovative approaches such as data-driven deployment strategies and efficient energy efficient resource (EER) management for small cells. Leveraging data-driven methodologies, operators can optimize small cell deployment locations and configurations based on real-time traffic patterns and environmental conditions, thereby maximizing network performance while minimizing energy consumption. This research investigates the effectiveness of a data-driven mechanism in enhancing the average achievable data rate of small cells within Heterogeneous UDNs. Our proposed approach Data Driven Opportunistic Sleep Strategy (D-DOSS) employs stochastic geometry based mathematical model for the heterogeneous networks (HetNets) wireless network will assess the impact of strategic small cell deployment on network performance in respect of energy savings. The results from Monte Carlo simulations reveal that D-DOSS outperforms traditional strategies by improving energy efficiency (EE) by 20% and achieving a 15% higher average data rate. Additionally, D-DOSS achieves a coverage probability of 50% at a signal-to-interference-plus-noise ratio (SINR) threshold of 5 dB, significantly better than random sleep mode (RSM) and load aware sleep (LAS) strategies. Overall, our findings underscore the significance of data-driven deployment and management strategies in optimizing the performance of HetNets UDNs. By embracing such approaches, wireless operators can meet the escalating demands for high-speed data transmission while achieving greater EE and sustainability in wireless network operations.

Keywords

• UDN
• Small cell deployment
• Energy optimization
• Data-driven approaches
• Stochastic geometry
• Network performance