  A main challenge of 5G and beyond wireless systems is to efficiently utilize
the available spectrum and simultaneously reduce the energy consumption. From
the radio resource allocation perspective, the solution to this problem is to
maximize the energy efficiency instead of the throughput. This results in the
optimal benefit-cost ratio between data rate and energy consumption. It also
often leads to a considerable reduction in throughput and, hence, an
underutilization of the available spectrum. Contemporary approaches to balance
these metrics based on multi-objective programming theory often lack
operational meaning and finding the correct operating point requires careful
experimentation and calibration. Instead, we propose the novel concept of
hierarchical resource allocation where conflicting objectives are ordered by
their importance. This results in a resource allocation algorithm that strives
to minimize the transmit power while keeping the data rate close the maximum
achievable throughput. In a typical multi-cell scenario, this strategy is shown
to reduces the transmit power consumption by 65% at the cost of a 5% decrease
in throughput. Moreover, this strategy also saves energy in scenarios where
global energy efficiency maximization fails to achieve any gain over throughput
maximization.
