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

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%

Application of opposition-based learning concepts in reducing the power consumption in wireless access networks

The reduction of power consumption in wireless access networks is a challenging and important issue. In this paper, we apply Opposition-Based Learning (OBL) concepts for reducing the power consumption of LTE base stations. More specifically, we present a new Modified Biogeography Based Optimization (BBO) algorithm enhanced with OBL techniques. We apply both the original BBO and the new Modified Opposition BBO (MOBBO) to network design cases to the city of Ghent, Belgium, with 75 possible LTE base station locations. We optimize the network towards two objectives: coverage maximization and power consumption minimization. Preliminary results indicate the advantages and applicability of our approach

Evaluation of the potential for energy saving in macrocell and femtocell networks using a heuristic introducing sleep modes in base stations

In mobile technologies two trends are competing. On the one hand, the mobile access network requires optimisation in energy consumption. On the other hand, data volumes and required bit rates are rapidly increasing. The latter trend requires the deployment of more dense mobile access networks as the higher bit rates are available at shorter distance from the base station. In order to improve the energy efficiency, the introduction of sleep modes is required. We derive a heuristic which allows establishing a baseline of active base station fractions in order to be able to evaluate mobile access network designs. We demonstrate that sleep modes can lead to significant improvements in energy efficiency and act as an enabler for femtocell deployments

Analytical Evaluation of Coverage-Oriented Femtocell Network Deployment

This paper proposes a coverage-oriented femtocell network deployment scheme, in which the femtocell base stations (BSs) can decide whether to be active or inactive depending on their distances from the macrocell BSs. Specifically, as the areas close to the macrocell BSs already have satisfactory cellular coverage, the femtocell BSs located inside such areas are kept to be inactive. Thus, all the active femtocells are located in the poor macrocell coverage areas. Based on a stochastic geometric framework, the coverage probability can be analyzed with tractable results. Surprisingly, the results show that the proposed scheme, although with a lower defacto femtocell density, can achieve better coverage performance than that keeping all femtocells in the entire network to be active. The analytical results further identify the achievable optimal performance of the new scheme, which provides mobile operators a guideline for femtocell deployment and operation.Comment: 6 pages, 7 figures, published in IEEE International Conference on Communications (ICC'13