This paper proposes computationally efficient algorithms to maximize the energy efficiency in multi-carrier wireless interference networks, by a suitable allocation of the system radio resources, namely the transmit powers and subcarrier assignment. The problem is formulated as the maximization of the system Global Energy-Efficiency subject to both maximum power and minimum rate constraints. This leads to a challenging non-convex fractional problem, which is tackled through an interplay of fractional programming, learning, and game theory. The proposed algorithmic framework is provably convergent and has a complexity linear in both the number of users and subcarriers, whereas other available solutions can only guarantee a polynomial complexity in the number of users and subcarriers. Numerical results show that the proposed method performs similarly as other, more complex, algorithms.
@article{arxiv.1802.09591,
title = {A Learning Approach for Low-Complexity Optimization of Energy Efficiency in Multi-Carrier Wireless Networks},
author = {Salvatore D'Oro and Alessio Zappone and Sergio Palazzo and Marco Lops},
journal= {arXiv preprint arXiv:1802.09591},
year = {2018}
}