Albrecht J. Fehske

How much energy is needed to run a wireless network

In order to quantify the energy efficiency of a wireless network, the power consumption of the entire system needs to be captured. In this article, the necessary extensions with respect to existing performance evaluation frameworks are discussed. The most important addenda of the proposed energy efficiency evaluation framework (E3F) are a sophisticated power model for various base station types, as well as large-scale long-term traffic models. The BS power model maps the RF output power radiated at the antenna elements to the total supply power of a BS site. The proposed traffic model emulates the spatial distribution of the traffic demands over large geographical regions, including urban and rural areas, as well as temporal variations between peak and off-peak hours. Finally, the E3F is applied to quantify the energy efficiency of the downlink of a 3GPP LTE radio access network.

Energy Efficiency Aspects of Base Station Deployment Strategies for Cellular Networks

In the strive for lessening of the environmental impact of the information and communication industry, energy consumption of communication networks has recently received increased attention. Although cellular networks account for a rather small share of energy use, lowering their energy con- sumption appears beneficial from an economical perspective. In this regard, the deployment of small, low power base stations, alongside conventional sites is often believed to greatly lower the energy consumption of cellular radio networks. This paper investigates on the impact of deployment strategies on the power consumption of mobile radio networks. We consider layouts featuring varying numbers of micro base stations per cell in addition to conventional macro sites. We introduce the concept of area power consumption as a system performance metric and employ simulations to evaluate potential improvements of this metric through the use of micro base stations. The results suggest, that for scenarios with full traffic load, the use of micro base stations has a rather moderate effect on the area power consumption of a cellular network.

Energy Efficiency Improvements through Micro Sites in Cellular Mobile Radio Networks

Efforts to increase the energy efficiency of infor- mation and communication systems in general and cellular mobile radio networks in particular has recently gained mo- mentum. Besides positive environmental effects, lowering the energy consumption of mobile radio systems appears beneficial from an economical perspective. In this regard, the deployment of small, low power base stations, alongside conventional sites is often believed to greatly lower the energy consumption of cellular mobile radio networks. In this paper we investigate that matter in more detail from a deployment perspective. We evaluate potential improvements of the area power consumption achievable with network layouts featuring varying numbers of micro sites in addition to conventional macro sites for given system performance targets under full load conditions.

Bit per Joule efficiency of cooperating base stations in cellular networks

Energy consumption is lately receiving increased interest, and research efforts to assess the energy efficiency of cellular communication networks are made. This paper addresses the tradeoffs between gains in cell throughput that can be expected from coordinated multi point transmission and reception technologies and the increased energy consumption that they induce in cellular base stations. We explicitly consider effective transmission rates, taking into account the additional pilot, control and feedback overhead required for CoMP schemes, and determine the bit per Joule efficiency of network models for common propagation parameters under varying network densities and cooperation cluster sizes.

Traffic Demand and Energy Efficiency in Heterogeneous Cellular Mobile Radio Networks

Optimization of the energy efficiency is considered not only to positively contribute to the ecological assessment, but gains in importance from operators point of view as well, since energy costs for running a mobile radio network have an increasing share of the operational expenditure. From this perspective, the utilization of small, low power base stations is regarded as a promising strategy to enhance a networks throughput and to increase the energy efficiency. In this paper we investigate on the efficiency of homogeneous and heterogeneous networks consisting of a varying number of micro sites with regard to traffic load conditions.

Concurrent Load-Aware Adjustment of User Association and Antenna Tilts in Self-Organizing Radio Networks

Network operators expect a coordinated handling of parameter changes submitted to the operating networks configuration management entity by closed-loop self-organizing network (SON) techniques. For this reason, a major research goal for emerging SON technologies is to achieve coordinated results out of a plethora of independently or even concurrently running use-case implementations. In this paper, we extend current frameworks to compute desirable user associations by an interference model that explicitly takes base-station loads into account. With the aid of this model, we are able to make considerably more accurate estimations and predictions of cell loads compared with established methods. Based on the ability to predict cell loads, we derive algorithms that jointly adapt user-association policies and antenna-tilt settings for multiple cells. We demonstrate by detailed numerical evaluations of realistic networks that these algorithms can be applied to capacity and coverage optimization, mobility load balancing, and cell outage compensation use cases. As a result, rather than performing any heading or tailing coordination, the joint technique inherently comprises all three use cases, making their coordination redundant. For all scenarios studied, the joint optimization of tilts and user association improves quality of service in terms of the fifth percentile of user throughput compared with state-of-the-art techniques. The proposed models and techniques can be straightforwardly extended to other physical and soft parameters.

Small-Cell Self-Organizing Wireless Networks

Increasing the spatial reuse of frequency spectrum by deploying more access points has historically been the most effective means to improve the capacity of any cellular communication network. Todays mobile networks face a proliferation of data services and overall demand for data traffic that has been strongly increasing over several years. As a result, increasing network capacity through the deployment of small lower power nodes is of key importance for mobile network operators. Although such small access points are conceptually equivalent to conventional cellular base stations in many ways, the expected large number of small cells as well as their much more dynamic unplanned deployment raise a variety of challenges in the area of network management. This paper discusses such challenges and reviews state-of-the-art modeling as well as selected network management techniques.

Aggregation of variables in load models for interference-coupled cellular data networks

In order to meet increasing traffic demands, future generations of cellular networks are characterized by decreasing cell sizes at full frequency reuse. Due to inevitable inter-cell interference, load conditions in neighboring cells can no longer be considered independent. Unfortunately, the adequate flow level model for such a setup is analytically intractable. Utilizing aggregation techniques, which were originally proposed to analyze large state models in economics, we propose a framework to compute the average base station loads based on an approximation of the joint stationary distribution of the number of active flows in all cells. The technique proposed requires solving a system of linear equations whose dimension increases exponentially with the number of cells. Since such a system is essentially intractable for large networks, we propose a fixed point algorithm to compute approximate base station loads based on the notion of average interference. Numerical results validate the accuracy of both modeling techniques. The modeling approach presented in this paper is essential for accurate characterization of cell throughput as well as base station energy consumption under varying load conditions.

Energy saving and capacity gain of micro sites in regular LTE networks: downlink traffic layer analysis

We study the effect of deployment of low cost, low power micro base stations along with macro base stations on energy consumption and capacity of downlink LTE. [1] studied this problem, using spectral area efficiency as the performance metric. We show that the analysis proposed in [1] is inaccurate as the traffic layer specifications of LTE networks is not included in the analysis. We also investigate the effect of user association and frequency band allocation schemes on energy consumption and capacity of LTE networks. Specifically, we add the following three important elements to the analysis proposed in [1]: a traffic layer analysis that take both the physical and traffic layer specifications of LTE downlink into account; a threshold-based policy to optimally associate users to base stations; and an allocation scheme to better allocate the frequency band to macro and micro base stations. We investigate all combinations of these elements through numerical evaluation and observe that 1. there are important differences between traffic layer and physical layer analysis, 2. threshold-based user association policy improve the traffic capacity of the network by up to 33% without affecting the energy profile of the network, and 3. considerable energy saving and capacity gain can be achieved thought a careful allocation of the frequency band to macro and micro base stations. Finally, we determine the optimal network configuration and show that up to 46% saving in energy can be achieved compared to the case that no micro base stations are deployed in the network.

A Framework Enabling Spatial Analysis of Mobile Traffic Hot Spots

An enormous increase in data traffic demanded by mobile users calls for efficient deployment strategies such as multi-layer heterogeneous networks. However, placing small cells at the desired locations to offload as much traffic as possible from overlaying macro cells is a crucial task. In this regard, geo-location and user equipment positioning techniques help obtain spatial distributions of user locations and their respective traffic volumes. In this paper, we provide a tool capable of reducing errors that stem from spatial discretization of traffic data and that can autonomously detect hot spots given a certain threshold. Based on geo-located traffic in a 3G network in a dense urban city, we find that traffic in the area is approximately log-normally distributed and that the size of traffic hot spots are approximately Weibull distributed. Based on our statistical findings, we observe that utilizing 4 small cells per km2 covering 3.2% of the total area and around 34% of the total traffic volume is a very meaningful deployment strategy; however, deploying more small cells in larger hot zones becomes increasingly costly in terms of the ratio of area covered and traffic demand serviced.