Maria Michalopoulou

Towards characterizing primary usage in cellular networks: A traffic-based study

An in depth understanding of cellular traffic, and thus of spectrum usage, is essential for estimating better opportunities offered by a secondary use of spectrum. Due to the fact that user activity in cellular networks is expected to exhibit a highly dynamic behavior in space and time, there is a significant benefit to be gained from measurement-driven studies. A careful analysis of spatio-temporal traffic patterns provides us insights not only in the efficiency of cellular networks, but these models can be used to model future use of and need for secondary spectrum. Towards this direction our aim in this paper is to present a part of our on-going work on the analysis of real-world traffic data collected within a country-wide GSM cellular network for a period of four weeks.

Studying the Relationships between Spatial Structures of Wireless Networks and Population Densities

In this paper we show how to quantify dependency between the node distributions of wireless networks and the underlying population densities. Furthermore, we show that a quantitative analysis of this relation can be beneficial for understanding and generating realistic network models. Towards this direction we argue that spatial statistics is an appropriate method for this purpose. As a case study we analyze the correlations of GSM-900 and UMTS base stations, with the underlying population densities of Germany. We find that there is a significant statistical similarity between the locations of GSM-900 base stations and population, due to the immense penetration of the network, and a less tight relation in the case of UMTS network. We consider the problem of covering a given population pattern and use the concept of fractal dimension to show how the number of required base stations changes when their maximum coverage range decreases, according to the population pattern.

Game theory for wireless networking: Is a Nash equilibrium always a desirable solution?

During the last decade there has been a great interest in applying game theory to several problems of wireless networking. The main focus of the existing research so far has been to ensure and prove that the game shall eventually reach a Nash equilibrium solution. In this paper our aim is to question whether a Nash equilibrium must be used as an allpurpose treatment without taking into consideration additional factors. Specifically, by exploiting the concept of equilibrium phase transitions from statistical mechanics we argue that in the case of a dynamic wireless environment - where the system might be constantly drifting from the Nash equilibrium due to external factors - the Nash equilibrium might be a particularly unstable outcome in terms of the overall performance and hence not a desirable outcome.

Self-organizing multiple access with minimal information: Networking in El Farol bar

In this paper we propose a novel self-organizing resource allocation scheme with minimal feedback information based on minority game (MG). Designing wireless protocols with minimal feedback is especially important, because the price for information exchange in wireless networks is high and reduces the overall capacity of the system. This is especially true for networks that may rely strongly on self-organizing communication schemes, such as wireless ad hoc networks and cognitive radio networks. We apply an MG resource allocation scheme to schedule user transmissions in the network. We combine our MG scheduling model with CSMA (Collision Avoidance Multiple Access) protocol in order to minimize the number of collisions in dense wireless networks. Simulation results demonstrate that MG enhanced version of the CSMA protocol achieves higher channel utilization compared to a standard slotted CSMA without causing serious additional overhead in terms of delay. The MG-slotted CSMA is shown as a paradigm of how self-organized, game based approaches can be used for distributed resource allocation in ad hoc, mesh and cognitive radio networks.

Addressing Phase Transitions in Wireless Networking Optimization

The general aim of this paper is to introduce the notion of phase transitions into wireless networking optimization. Although the theory of phase transitions from statistical physics has been employed in optimization theory, phase transitions in the context of optimization of wireless networks have not yet been considered. In wireless networking optimization, given one or more optimization objectives we often need to define mathematically an optimization task, so that a set of requirements is not violated. However, especially recent trends in wireless communications, such as self-organized networks, femto-cellular systems, and cognitive radios, calls for optimization approaches that can be implemented in a distributed and decentralized fashion. Thus we are interested to find utility-based approaches that can be practically employed in a self-organizing network. We argue that phase transitions can be identified and taken appropriately into account in order to eliminate the emergence of undesirable solutions that lie near the point where the phase transition occurs. As an example we present a simple power control problem for a macrocell-femtocell network scenario. We formulate a distributed framework of the problem where we model a phase transition effect by means of a dummy variable in order to exclude solutions lying in the one side of the phase transition.

A Mean Field Analysis of CSMA/CA Throughput

Due to the fact that Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocols are extensively used in commercial wireless networks, the analysis of this family of medium access control protocols has been a topic of great interest for the wireless networking community. In this paper, we present a mean field analysis for the throughput of a slotted CSMA/CA scheme in single-hop networks. The concept of mean field approximation originates in statistical mechanics and it is a widely used approximation method employed to address systems of many interacting particles that cannot be solved exactly. The idea is to simplify the treatment of a many-body system by describing the interaction of one particle with the other interacting particles by an average potential, the so-called mean field. In our analysis, the nodes of a wireless network constitute a set of interacting particles; the interaction of a certain node with the other nodes in the network is reduced to an averaged channel condition. We estimate the amount of busy slots and the network throughput with respect to the offered traffic. Our analysis is of a recursive form, in which at each step the amount of traffic is increased by a specific amount. Also, our model takes into accounts retransmissions of collided packets.

Employing Minority Games in self-organizing wireless networks: Dynamic channel allocation

Designing distributed protocols with limited requirements for exchange of feedback information between the nodes is a key element in the development of cognitive and self-organizing ad hoc networks. We argue that the Minority Game (MG) and its variations can constitute useful tools towards this direction. In particular, the MG formulation is well suited for addressing a variety of resource allocation problems in self-organizing wireless networks. In this paper we illustrate the employment of MGs by presenting a paradigm of an MG-based model for dynamic channel allocation.

The Critical Range in Clustered Ad Hoc Networks: An Analysis for Gaussian Distributed Nodes

Connectivity is one of the essential properties of wireless networks. Although it is influenced by many environmental factors, it exhibits a strong and fundamental dependence on the distances between the nodes. Therefore, the connectivity of wireless networks from distance perspective has been a subject of constant interest for the research community. However, despite the fact that the assumption of uniformly distributed nodes is highly unrealistic for commercial wireless networks, analytical work on the connectivity of non-uniform node distributions is almost non-existent. In many real cases wireless nodes may be placed in a way such that they form clusters. In this letter we consider a set of clustered nodes distributed according to a symmetric Gaussian distribution and we present an approximate estimation of the probability density function of the critical range of the cluster. This is, to the best of our knowledge, the first estimation of the critical range for a clustered distribution.

Wireless network characterization via phase diagrams

Like physical systems can exist in different phases, the concept of different phases (or states) can be also attributed to other types of systems, such as wireless networks. In this paper we employ the notion of phase diagrams to characterize a wireless network by means of a reduced number of simulation points. By reducing the amount of simulations - compared to fine-grained simulation-based analyses - we decrease the accuracy of the result, however, not in an arbitrary way. The idea is to derive the so-called phase diagram that fully characterizes the system with respect to the points of state changes. Typically, the different states of a system correspond to distinguishable macroscopic properties, therefore the phase transition points are in a qualitative sense points of particular significance for a system under study. We show how phase diagrams can be estimated from a limited set of simulation points by means of Support Vector Machines.