Energy Efficient Resource Allocation and Utilization in Future Heterogeneous Cellular Network

Future Mobile Heterogeneous Cellular Networks are emerging as promising technology in terms of high speed, low latency and ubiquitous connectivity. Providing energy efficient services in exponentially increasing user size and rigorous utilization of mobile services is a key challenge for mobile operators. The mobile operators deployed dense small cells to enhance the network capacity for providing the network services to maximum users. Instead of fully utilize of the existing deployment, operators leads to enhance the number of small cell base stations to enhance the network coverage. When the number of small cells increases, the energy consumption of the cellular network also increases. Thus a resource efficient, cost effective and energy efficient solution is required to control the deployment of new base station that consequently enhance the energy efficiency. In this paper, an efficient resource allocation and utilization model is proposed using Cognitive Fusion Centre (CFC). Where the CFC has Resource State Information (RSI) of the network resources and manages the free available resources. It helps in generating resource segment to facilitate the incoming users at peak hours. The propose solution can be deployed to any dense environment for maximum resource utilization

Traffic-aware cell management for green ultra-dense small cell networks

To reduce the power consumption of fifth-generation ultradense small-cell networks, base stations can be switched to low-power sleep modes when local traffic levels are low. In this paper, a novel sleep mode control algorithm is proposed to control such sleep modes. The algorithm innovates a concept called traffic-aware cell management (TACM). It involves cell division, cell death, and cell migration to represent adaptations of networks, where the state transitions of base stations are controlled. Direction of arrival (DOA) is adopted for distributed decision making. The TACM algorithm aims at reducing the network power consumption while alleviating the impacts of applying sleep modes, such as mitigating system overheads and reducing user transmission power. The TACM algorithm is compared with a recent consolidated baseline scheme by simulation on networks with unbalanced traffic distributions and with base stations at random locations. In contrast, the TACM algorithm shows a significant improvement in mitigating system overheads due to the absence of load information exchange overhead and up to 72 times less switching frequency. Up to 81% network power consumption can be reduced compared with the baseline scheme if considering high energy consumption of switching transient states. In addition, at a low traffic level, average uplink transmission power is reduced by 79% comparatively. Furthermore, the impact of important performance-governing parameters of the TACM algorithm is analyzed. The insensitivity to the estimation accuracy of DOA is also demonstrated. The results show that the proposed TACM algorithm has a comprehensive advantage of power reduction and overhead mitigation over the baseline scheme