Reducing the power consumption in LTE-advanced wireless access networks by a capacity based deployment tool

As both the bit rate required by applications on mobile devices and the number of those mobile devices are steadily growing, wireless access networks need to be expanded. As wireless networks also consume a lot of energy, it is important to develop energy-efficient wireless access networks in the near future. In this study, a capacity-based deployment tool for the design of energy-efficient wireless access networks is proposed. Capacity-based means that the network responds to the instantaneous bit rate requirements of the users active in the selected area. To the best of our knowledge, such a deployment tool for energy-efficient wireless access networks has never been presented before. This deployment tool is applied to a realistic case in Ghent, Belgium, to investigate three main functionalities incorporated in LTE-Advanced: carrier aggregation, heterogeneous deployments, and Multiple-Input Multiple-Output (MIMO). The results show that it is recommended to introduce femtocell base stations, supporting both MIMO and carrier aggregation, into the network (heterogeneous deployment) to reduce the network's power consumption. For the selected area and the assumptions made, this results in a power consumption reduction up to 70%. Introducing femtocell base stations without MIMO and carrier aggregation can already result in a significant power consumption reduction of 38%

Modelling the energy efficiency of microcell base stations

The power consumption of wireless access networks will become a major issue in the coming years. Therefore, it is important to have a realistic idea about the power consumption of each element in those access networks. In this paper, an energy efficiency model for microcell base stations is proposed. Based on this model, the energy efficiency of microcell base stations is compared for various wireless technologies, namely mobile WiMAX, HSPA and LTE. The power consumption of microcell base stations is about 70-77% lower than for macrocell base stations but a macrocell base station is more energy-efficient than a microcell base station for the same bit rates. However, for the considered case and assuming our parameters are correct, a reduction in power consumption can be obtained by using microcell base stations to fill coverage holes

A novel design approach for NB-IoT networks using hybrid teaching-learning optimization

In this paper, we present and address the problem of designing green LTE networks with Internet of Things (IoT) nodes. We consider the new NarrowBand-IoT (NB-IoT) wireless technology that will emerge in current and future access networks. The main objective is to reduce power consumption by responding to the instantaneous bit rate demand by the user and the IoT node. In this context, we apply emerging evolutionary algorithms to the above problem. More specifically, we apply the Teaching-Learning-Optimization (TLBO), the Jaya algorithm, and a hybrid algorithm. This hybrid algorithm named TLBO-Jaya uses concepts from both algorithms in an effective way. We compare and discuss the preliminary results of these algorithms

Designing energy-efficient wireless access networks: LTE and LTE-advanced

As large energy consumers, base stations need energy-efficient wireless access networks. This article compares the design of Long-Term Evolution (LTE) networks to energy-efficient LTE-Advanced networks. LIE-Advanced introduces three new functionalities - carrier aggregation, heterogeneous networks, and extended multiple-input, multiple-output (MIMO) support. The authors develop a power consumption model for LIE and LIE-Advanced macrocell and femtocell base stations, along with an energy efficiency measure. They show that LIE-Advanced's carrier aggregation and MIMO improve networks' energy efficiency up to 400 and 450 percent, respectively