Nowadays, network operators are basically facing two problems: the fast number of subscribers growth and the network congestion. To solve these problems, network operators increase the number of cells, make cells smaller and reserve a subpart of frequencies to Femto cells to offload data traffic. As a consequence, networks manual planning demands a lot of efforts and its cost increases. Self-configuring and self-optimizing mechanisms would be vital to operators to reduce manual planning. This article focuses on the application of these mechanisms in LTE and more specifically on the procedure of Automated configuration of Physical Cell Ids (PCIs). This procedure aims at avoiding conflicts in PCIs allocation. In this article, we first evaluate the performances of 3 relabeling algorithms applied on graphs representing real LTE Macro networks: Random Relabeling algorithm (RR), Smallest available Value algorithm (SV), Distance 3 neighbour label algorithm (D3). Then, we evaluate the performances of the best algorithm applied on graphs representing real LTE networks where Femto cells are connected to Macro cells. Here, we answer the following question: is the selected relabeling algorithm still efficient when we extend its application to Femto cells?

In wireless access network optimization, today's main challenges reside in traffic offload and in the improvement of both capacity and coverage networks. The operators are interested in solving their localized coverage and capacity problems in areas where the macro network signal is not able to serve the demand for mobile data. Thus, the major issue for operators is to find the best solution at reasonable expanses. The femto cell seems to be the answer to this problematic. In this work 1 , we focus on the problem of sharing femto access between a same mobile operator's customers. This problem can be modeled as a game where service requesters customers (SRCs) and service providers customers (SPCs) are the players.This work addresses the sharing femto access problem considering only one SPC using game theory tools. We consider that SRCs are static and have some similar and regular connection behavior. We also note that the SPC and each SRC have a software embedded respectively on its femto access, user equipment (UE).After each connection requested by a SRC, its software will learn the strategy increasing its gain knowing that no information about the other SRCs strategies is given. The following article presents a distributed learning algorithm with incomplete information running in SRCs software. We will then answer the following questions for a game with N SRCs and one SPC: how many connections are necessary for each SRC in order to learn the strategy maximizing its gain? Does this algorithm converge to a stable state? If yes, does this state a Nash Equilibrium and is there any way to optimize the learning process duration time triggered by SRCs software?

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