Abstract. This paper focuses in the analysis of 100% static and distributed inter-cell interference coordination techniques in the context of LTE networks. Several methods have been modeled and studied with the aim of deriving practical radio planning rules based on the joint effect of operational parameters and thresholds. The investigation places special emphasis on the efficiency vs. fairness tradeoff. Several metrics have been detected as interesting to allow not only the performance measurement from different point of view, but also to look at the effectiveness in the utilization of resources. Results show that similar levels of spectral efficiency can be achieved by means of a proper and accurate network tuning. On the other hand, interesting second order differences appear due to some inherent features of each approach. These can be exploited depending on the particular network operator needs.

In order to cope with the rapidly increasing service demand in cellular networks, more cells are needed with better resource usage efficiency. This poses challenges for the network planning since service demand in practical networks is not geographically uniform and, to cope with the non-uniform service demand, network deployments are becoming increasingly irregular. This paper introduces a new idea to deal with the non-uniform network topology. Rather than capturing the network character (e.g. load distribution) by means of stochastic methods, the proposed novel approach aims at transforming the analysis from the physical (irregular) domain to a canonical/dual (uniform) domain that simplifies the work due to its symmetry. To carry out this task, physical and canonical domains are connected using the conformal (Schwarz-Christoffel) mapping, that makes the rich and mature theory of Complex Analysis available. The main contribution of this paper is to introduce and validate the usability of conformal mapping in the load coupling analysis of cellular networks.

In cellular networks, users are grouped into different cells and served by different access points (base stations) that provide wireless access to services and applications. In general, the service demand is very heterogeneous, non-uniformly distributed, and dynamic. Consequently, radio access networks create very irregular topologies with more access points where service demand is concentrated. While this dynamism requires networks with the ability to adapt to time-varying conditions, the non-uniformity of the service demand makes the planning, analysis, and optimization difficult. In order to help with these tasks, a framework based on canonical domains and spatial mappings (e.g., conformal mapping) have recently been proposed. The idea is to carry out part of the planning in a canonical (perfectly symmetric) domain that is connected to the physical one (real-scenario) by means of a spatial transformation designed to map the access points consistently with the service demand. This paper continues the research in that direction by introducing additional tools and possibilities to that framework, namely the use of centroidal Voronoi algorithms and non-conformal composite mappings. Moreover, power optimization is also introduced to the framework. The results show the usability and effectiveness of the proposed method and its promising research perspectives. I. INTRODUCTION A. Context and motivationRadio access planning and optimization are fundamental tasks in cellular networks. Broadly speaking, planning refers to the tasks of determining the number, location, and configuration of access points to provide wireless access to users (and things) to services and applications, with a certain targeted Quality of Service (QoS). In particular, the problem of finding the number of access points is also referred to as dimensioning [1], and this initial step aims at providing the required capacity for the service demand volume that is expected. However, in practice, both dimensioning and sites positioning are very difficult problems because the service demand is not uniformly distributed and it is quite diverse and dynamic. Nowadays, taking into account the continuous evolution of radio access technologies, and the new concepts and paradigms that are expected for the fifth generation (5G) of cellular networks, the boundary between planning and optimization tasks becomes blurred. Indeed, according to the excellent work presented in [1], planning and optimization are iterative tasks that go hand-in-hand. In this line of thinking, the authors of [2] also pose the need for re-thinking planning. They emphasize the importance of distributing the service demand as evenly as possible among cells as a key criterion to achieve effective planning; a goal that in the opinion of the authors of [2] (and in our's) is a very valid way to enhance system performance.In our previous work [3], also motivated by the aforementioned ideas, a novel framework for planning and optimization based on the use of canonical domains and spatial transforma...

Cell Switch-Off (CSO) is recognized as a promising approach to reduce the energy consumption in next-generation cellular networks. However, CSO poses serious challenges not only from the resource allocation perspective but also from the implementation point of view. Indeed, CSO represents a difficult optimization problem due to its NP-complete nature. Moreover, there are a number of important practical limitations in the implementation of CSO schemes, such as the need for minimizing the real-time complexity and the number of on-off/off-on transitions and CSO-induced handovers. This article introduces a novel approach to CSO based on multiobjective optimization that makes use of the statistical description of the service demand (known by operators). In addition, downlink and uplink coverage criteria are included and a comparative analysis between different models to characterize intercell interference is also presented to shed light on their impact on CSO. The framework distinguishes itself from other proposals in two ways: 1) The number of on-off/off-on transitions as well as handovers are minimized, and 2) the computationally-heavy part of the algorithm is executed offline, which makes its implementation feasible. The results show that the proposed scheme achieves substantial energy savings in small cell deployments where service demand is not uniformly distributed, without compromising the Quality-of-Service (QoS) or requiring heavy real-time processing.

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