  Power allocation in spectrum sharing systems is challenging due to excessive
interference that the secondary system could impose on the primary system.
Therefore, an interference threshold constraint is considered to regulate the
secondary system's activity. However, the primary receivers should measure the
interference and inform the secondary users accordingly. These cause design
complexities, e.g., due to transceiver's hardware impairments, and impose a
substantial signaling overhead. We set our main goal to mitigate these
requirements in order to make the spectrum sharing systems practically
feasible. To cope with the lack of a model we develop a coexisting deep
reinforcement learning approach for continuous power allocation in both
systems. Importantly, via our solution, the two systems allocate power merely
based on geographical location of their users. Moreover, the inter-system
signaling requirement is reduced to exchanging only the number of primary users
that their QoS requirements are violated. We observe that compared to a
centralized agent that allocates power based on full (accurate) channel
information, our solution is more robust and strictly guarantees QoS
requirements of the primary users. This implies that both systems can operate
simultaneously with almost-zero inter-system signaling overhead.
