This thesis focuses on resources and power allocation problem in the fourth generation (4G) of cellular networks. To face the continuous growth of mobile users capacity requirements, operators need to densify their radio access network (RAN) infrastructure, to maximize the use of the available bandwidth in space. One of the major issues of this new architecture is the proximity of many base stations (BS) and the management of the interference they generate on each other's cell. Such constraints makes scientific community focus on Self-Optimized, Self-Organized Networks (SON) that allow network elements to optimize them-selves through decentralized decisions (no static network planning is required). A major interest of SON is their capability to scale to large and non-organized networks, as well as being able to adapt them-selves dynamically, by using distributed algorithms. In this context, this thesis proposes the study of two resource allocation problems. The first part of this thesis focuses on the optimisation of resource sharing, in the context of coordinated multi-points transmissions (CoMP). Performances of BS coordination are evaluated, using the uniform capacity criterion, as well as the trade-off between total capacity and fairness among users. We also propose a generalized and distributed method to select the set of coordination of BS, to optimize the capacity-fairness trade-off. In the second part of this thesis, we focus on optimizing the transmit power and resource allocation, in order to reduce electric consumption. We present two distributed algorithms: the first one is based on a stochastic optimisation (using Gibbs sampling), and tries to reach the global optimum state through decentralized decision. The second one is based on control theory, and uses target tracking as well as model predictive control to allocate resources and power in a dynamic channel scenario. In many cases, trade-offs are to be maid between opposite objectives when evaluating network performances (total throughput, fairness, energy consumption, etc.). In this thesis, we present most of the network performances using multi-objectives evaluations.
