Deep Reinforcement Learning for Radio Resource Allocation and Management
in Next Generation Heterogeneous Wireless Networks: A Survey
Abstract
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.