Ben Hadj Alaya - Feki, Afef (2008) Metrology in wireless mobile networks for enhanced radio resource management. PhD thesis Télécommunications , Telecom ParisTech - Département Signal Image TSI, ENST p.162.

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Abstract

This thesis report describes my research work on "Metrology in wireless mobile networks for enhanced radio resource management". The main goal of the thesis is to propose effective approaches dealing with information collection, processing and utilization in cognitive radio context. These works are motivated by the necessity for wireless networks to cope with the increasing demand of throughput and services on one hand, and the inefficient use of spectral resources on the other hand.

In chapter 1, we propose to integrate advanced radio signal processing in Radio Resource Management (RRM) mechanisms in order to realize an effective and easy-to-implement exploitation of radio measurements, leading to an overall RRM improvement. Thus, we propose a complete framework that performs a signal processing analysis, based on non-parametric regression and smoothing methods, on real radio measurements (GSM and UMTS).

In chapter 2, we propose a novel concept in cognitive networks that provides awareness on the radio environment through geo-localized interference measurements: the Interference Cartography. The purpose of this innovative concept is to combine location information with radio measurements carried out over heterogeneous radio networks, and to provide a complete view of the environment to be used in autonomous decision making in a cognitive context. This is achieved by aggregating the already existing radio measurements that circulate in wireless networks forming the heterogeneous network panorama, combining this aggregated information with geo-localization information, performing advanced signal processing techniques to render the information complete and reliable, and updating the information to provide a viable picture of the environment for efficient detection, analysis and decision.

In chapter 3, our interest turns to the access issues in cognitive networks. Thus, we propose to provide secondary users, wishing to send data in an opportunistic manner, with appropriate tools to achieve this complicated operation. The proposed approach is inspired from the Multi Armed Bandit (MAB) problem: a reinforcement learning technique that guides the secondary user thanks to appropriate rules and policies, in order to realize simultaneously 1- The exploration of its external environment to detect unused spectrum portions and 2- The exploitation of the acquired knowledge to decide on the best channel to access. Our study deals with the application of this approach on IEEE802.11 systems for the realization of opportunistic spectrum access, as well as the tuning of the algorithms’ parameters for better performances in non-stationary environments.

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