State-of-the-art algorithms for energy-efficient resource allocation in wireless networks are based on fractional programming theory, and are able to find the global maximum of the system energy efficiency only in noise-limited scenarios. In interference-limited scenarios , several sub-optimal solutions have been proposed, but an efficient framework to globally maximize energy-efficient metrics is still lacking. The goal of this work is to fill this gap, which will be achieved by merging fractional programming theory with monotonic optimization theory. The resulting optimization framework is useful for at least two main reasons. First, it sheds light on the ultimate energy-efficient performance of wireless networks. Second, it provides the means to benchmark the energy efficiency of practical, but sub-optimal, solutions.