  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.
