Energy efficiency optimization for 2D antenna arrays in self-organizing wireless networks

Nowadays telecommunication systems consume a huge amount of energy. While energy efficiency methods have already been studied for linear antennas, this paper focuses on 2D antenna arrays. An antenna array with many elements gives the network operator a high degree of flexibility in network optimization, which can be used not only for improving the quality of service, but also for increasing the energy efficiency. The energy efficiency can be improved by adapting the array shape, i.e., by switching off certain elements of the array. This work presents three novel solutions for the energy efficiency optimization, which are based on Q-learning, biological neural networks, and random drop, respectively. Simulation results demonstrate that significant improvements in terms of energy efficiency can be achieved, while the 5 th percentile of user throughput and coverage performances decrease only marginally.