Shabbir Amna, Rizvi Safdar, Alam Muhammad Mansoor, Mohd Su'ud Mazliham
Electronic Engineering, NED University of Engineering and Technology Karachi, Karachi, Pakistan.
Multimedia University, Cyberjaya, Malaysia.
PeerJ Comput Sci. 2024 Dec 12;10:e2475. doi: 10.7717/peerj-cs.2475. eCollection 2024.
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.
随着无线行业的指数级扩张，对提升数据网络吞吐量、容量和覆盖范围的需求变得至关重要。异构超密集网络（UDN）已成为满足这些对高数据速率和容量不断增长的需求的一种有前景的解决方案。然而，在UDN中有效部署和管理小小区存在重大挑战，特别是在不同的流量负载以及为最小化能耗而高效利用资源的必要性的情况下，尤其是在高干扰水平的环境中。小小区部署不当会导致过多干扰，从而导致盈利能力欠佳和能源资源利用效率低下。应对这些挑战需要创新方法，例如针对小小区的数据驱动部署策略和高效节能资源（EER）管理。利用数据驱动方法，运营商可以根据实时流量模式和环境条件优化小小区部署位置和配置，从而在最小化能耗的同时最大化网络性能。本研究调查了一种数据驱动机制在提高异构UDN中小小区平均可实现数据速率方面的有效性。我们提出的方法——数据驱动机会睡眠策略（D-DOSS），采用基于随机几何的异构网络（HetNet）数学模型，无线网络将评估战略性小小区部署对网络性能在节能方面的影响。蒙特卡罗模拟结果表明，D-DOSS通过将能源效率（EE）提高20%并实现平均数据速率高出15%，优于传统策略。此外，D-DOSS在5 dB的信号干扰加噪声比（SINR）阈值下实现了50%的覆盖概率，明显优于随机睡眠模式（RSM）和负载感知睡眠（LAS）策略。总体而言，我们的研究结果强调了数据驱动部署和管理策略在优化HetNet UDN性能方面的重要性。通过采用此类方法，无线运营商可以满足对高速数据传输不断增长的需求，同时在无线网络运营中实现更高的EE和可持续性。
