  Opportunistic spectrum access has the potential to increase the efficiency of
spectrum utilization in cognitive radio networks (CRNs). In CRNs, both spectrum
sensing and resource allocation (SSRA) are critical to maximizing system
throughput while minimizing collisions of secondary users with the primary
network. However, many works in dynamic spectrum access do not consider the
impact of imperfect sensing information such as mis-detected channels, which
the additional information available in joint SSRA can help remediate. In this
work, we examine joint SSRA as an optimization which seeks to maximize a CRN's
net communication rate subject to constraints on channel sensing, channel
access, and transmit power. Given the non-trivial nature of the problem, we
leverage multi-agent reinforcement learning to enable a network of secondary
users to dynamically access unoccupied spectrum via only local test statistics,
formulated under the energy detection paradigm of spectrum sensing. In doing
so, we develop a novel multi-agent implementation of hybrid soft actor critic,
MHSAC, based on the QMIX mixing scheme. Through experiments, we find that our
SSRA algorithm, HySSRA, is successful in maximizing the CRN's utilization of
spectrum resources while also limiting its interference with the primary
network, and outperforms the current state-of-the-art by a wide margin. We also
explore the impact of wireless variations such as coherence time on the
efficacy of the system.
