In the last few years, it has been observed a drastic surge of data traffic demand fromudmobile personal devices (smartphones and tablets) over cellular networks [1]. Evenudthough a significant improvement in cellular bandwidth provisioning is expected withudLTE-Advanced systems, the overall situation is not expected to change significantly. Inudfact, the diffusion of M2M and IoT devices is expected to increase at an exponential paceud(the share of M2M devices is predicted to increase 5x by 2018 [1]) while the capacity ofudthe cellular network is expected to increase linearly [1]. In order to meet such a highuddemand and to increase the capacity of the channel, multiple offloading techniques areudcurrently under investigation, from modifications inside the cellular network architecture,udto integration of multiple wireless broadband infrastructures, to exploiting directudcommunications between mobile devices. All these approaches can be diveded in twoudmain classes:ud- To develop more sophisticated physical layer technologies (e.g. massive MIMO,udhigher-order modulation schemes, cooperative multi-period transmission/reception)ud- To offload part of the traffic from the cellular to another complementary network.udFrom this perspective the thesis contributes on both areas. On the one hand we discussudour investigations about the performance of the LTE channel capacity through the developmentudof a unified modelling framework of the MAC-level downlink throughput ofuda sigle LTE cell, which caters for wideband CQI feedback schemes, AMC and HARQudprotocols as defined in the LTE standard. Furthemore we also propose a solution, basedudon reinforcement learning, to improve the LTE Adaptive Modulation and coding Schemeud(MCS).udOn the other hand we have proposed and validated offloading mechanisms which areudminimally invasive for users' mobile devices, as they use only minimally their resources.udFurthemore, as opposed to most of the literature, we consider the case where requestsudfor content are non-synchronised, i.e. users request content at random points in time.
展开▼
