  A wireless network operator typically divides the radio spectrum it possesses
into a number of subbands. In a cellular network those subbands are then reused
in many cells. To mitigate co-channel interference, a joint spectrum and power
allocation problem is often formulated to maximize a sum-rate objective. The
best known algorithms for solving such problems generally require instantaneous
global channel state information and a centralized optimizer. In fact those
algorithms have not been implemented in practice in large networks with
time-varying subbands. Deep reinforcement learning algorithms are promising
tools for solving complex resource management problems. A major challenge here
is that spectrum allocation involves discrete subband selection, whereas power
allocation involves continuous variables. In this paper, a learning framework
is proposed to optimize both discrete and continuous decision variables.
Specifically, two separate deep reinforcement learning algorithms are designed
to be executed and trained simultaneously to maximize a joint objective.
Simulation results show that the proposed scheme outperforms both the
state-of-the-art fractional programming algorithm and a previous solution based
on deep reinforcement learning.
