Growing diversity of agents in current communication networks and increasing capacitiesof concurrent technologies in the network environment has lead to the considerationof a novel distributed approach of the network management. In this evolvednetwork environment the increasing need for bandwidth and rare channel resources,opposes to reduction of the total energy consumption.This thesis focuses on application of distributed mechanisms and learning methodsto allow for more autonomy in the heterogeneous network, this in order to improveits performances. We are mainly interested in energy efficient stochastic mechanismsthat will operate in a distributed fashion by taking advantage of the computationalcapabilities of all the agents and entities of the network. We rely on application ofGame theory to study different types of complex systems in the distributed wirelessnetworks with dynamic interconnectivity.Specifically, we use the stochastic reinforcement learning tools to address issuessuch as, distributed user-network association that allows achieving an efficient dynamicand decentralized radio resource management. Then, we combine access selectionprocedures with distributed optimization to address the inter-cells interferencescoordination (ICIC) for LTE-advanced networks using dynamic power control and designof fractional frequency reuse mechanisms. Moreover we address in non-hierarchicalnetworks, more precisely in Delay Tolerant Networks (DTNs), decentralized methodsrelated to minimization of the end-to-end communication delay. In this framework weare interested, in addition to Nash equilibrium, to the notion of evolutionary stableequiliria in the different context of Evolutionary Games, Markov Decision EvolutionaryGames and Minority Games. As the major parts of our work includes testing andvalidations by simulations, eventually we present several implementations and integrationsmaterials for edition of simulation platforms and test beds
