  Next generation wireless networks are expected to be extremely complex due to
their massive heterogeneity in terms of the types of network architectures they
incorporate, the types and numbers of smart IoT devices they serve, and the
types of emerging applications they support. In such large-scale and
heterogeneous networks (HetNets), radio resource allocation and management
(RRAM) becomes one of the major challenges encountered during system design and
deployment. In this context, emerging Deep Reinforcement Learning (DRL)
techniques are expected to be one of the main enabling technologies to address
the RRAM in future wireless HetNets. In this paper, we conduct a systematic
in-depth, and comprehensive survey of the applications of DRL techniques in
RRAM for next generation wireless networks. Towards this, we first overview the
existing traditional RRAM methods and identify their limitations that motivate
the use of DRL techniques in RRAM. Then, we provide a comprehensive review of
the most widely used DRL algorithms to address RRAM problems, including the
value- and policy-based algorithms. The advantages, limitations, and use-cases
for each algorithm are provided. We then conduct a comprehensive and in-depth
literature review and classify existing related works based on both the radio
resources they are addressing and the type of wireless networks they are
investigating. To this end, we carefully identify the types of DRL algorithms
utilized in each related work, the elements of these algorithms, and the main
findings of each related work. Finally, we highlight important open challenges
and provide insights into several future research directions in the context of
DRL-based RRAM. This survey is intentionally designed to guide and stimulate
more research endeavors towards building efficient and fine-grained DRL-based
RRAM schemes for future wireless networks.
