Energy-aware basestation operation algorithm in MU-MIMO cellular networks with specific energy

Abstract

The energy consumption in communication networks has become a major concern due to its environmental issue and economic impact. This thesis studies a way to improve the energy efficiency of a cellular network beyond 4G with multi user massive MIMO air interface. We introduce a greedy BS-user association algorithm and a heuristic BS-OFF algorithm with rate adaptation, by taking into account the network traffic load. We define, as network energy efficiency metric, the traffic-specific energy, i.e., average basestation energy consumption per bit, and present a greedy algorithm based on the specific energy. To simulation in order to prove the performance of our proposed algorithm, we need reference models, basestation deployment model, user traffic and user demand model, and basestation power model. In this thesis we use the real topology of basestations for deployment model, and consider the population in the Manchester city to apply to the daily user traffic pattern for traffic model. The power model of basestation is used of LTE-BS power model to simulate our proposed algorithm. We compare the performance of proposed algorithm with conventional system, which associates to the basestation with the maximum signal strength. Simulations show that the proposed algorithms can improve en-ergy efficiency substantially, by a factor of 2.24 in an example network model.