A Robust and Resilient Load Balancing Framework for SoftRAN-Based HetNets With Hybrid Energy Supplies
Heterogeneous networks (HetNets) have been widely accepted as a promising architecture to fulfill the ever-increasing demand for capacity expansion. However, the energy consumed by the dense underlay of the large number of micro base stations that is required to achieve capacity expansion, exacerbat...
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Veröffentlicht in: | IEEE eTransactions on network and service management 2020-09, Vol.17 (3), p.1403-1417 |
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Sprache: | eng |
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Zusammenfassung: | Heterogeneous networks (HetNets) have been widely accepted as a promising architecture to fulfill the ever-increasing demand for capacity expansion. However, the energy consumed by the dense underlay of the large number of micro base stations that is required to achieve capacity expansion, exacerbates the energy inefficiency of cellular networks. Hybrid energy sources, i.e., the grid and green energy sources, can be used to meet the HetNets excessive demand for energy. In such networks, traffic load balancing becomes crucial to balance the trade-off between green energy utilization and quality of service (QoS) provisioning. Leveraging software-defined radio access networks (SoftRAN) and considering inaccuracy of vital network measurements, we develop an autonomous, robust and resilient load balancing framework. The framework consists of two major modules. First, the H_{\infty } regulator module, which guides the temporal utilization of green energy and distribution of network loads among base stations (BSs) in order to achieve long-term average QoS provisioning. Second, a user association module that optimizes user association and its corresponding traffic loads to minimize the network traffic latency while respecting loads proposed by the H_{\infty } regulator. Extensive performance evaluations demonstrate the efficacy of the proposed framework in autonomously balancing the trade-off between green energy consumption and traffic latency. Furthermore, performance evaluations confirm the robustness of the proposed framework to estimation inaccuracy and its resilience to sudden changes in network parameters. |
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ISSN: | 1932-4537 1932-4537 |
DOI: | 10.1109/TNSM.2020.2991339 |