Measurement-based coverage function for green femtocell networks

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Prediction and comparison of downlink electric-field and uplink localised SAR values for realistic indoor wireless planning

In this paper, for the first time a heuristic network calculator for both whole-body exposure due to indoor base station antennas or access points (downlink exposure) and localised exposure due to the mobile device (uplink exposure) in indoor wireless networks is presented. As an application, three phone call scenarios are investigated (Universal Mobile Telecommunications System (UMTS) macrocell, UMTS femtocell andWiFi voice-over-IP) and compared with respect to the electric-field strength and localised specific absorption rate (SAR) distribution. Prediction models are created and successfully validated with an accuracy of 3 dB. The benefits of the UMTS power control mechanisms are demonstrated. However, dependent on the macrocell connection quality and on the user's average phone call connection time, also the macrocell solution might be preferential from an exposure point of view for the considered scenario

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

Energy-Efficient selective activation in Femtocell Networks

Provisioning the capacity of wireless networks is difficult when peak load is significantly higher than average load, for example, in public spaces like airports or train stations. Service providers can use femtocells and small cells to increase local capacity, but deploying enough femtocells to serve peak loads requires a large number of femtocells that will remain idle most of the time, which wastes a significant amount of power. To reduce the energy consumption of over-provisioned femtocell networks, we formulate a femtocell selective activation problem, which we formalize as an integer nonlinear optimization problem. Then we introduce GREENFEMTO, a distributed femtocell selective activation algorithm that deactivates idle femtocells to save power and activates them on-the-fly as the number of users increases. We prove that GREENFEMTO converges to a locally Pareto optimal solution and demonstrate its performance using extensive simulations of an LTE wireless system. Overall, we find that GREENFEMTO requires up to 55% fewer femtocells to serve a given user load, relative to an existing femtocell power-saving procedure, and comes within 15% of a globally optimal solution

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

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future