  In this paper, we design a new smart softwaredefined radio access network
(RAN) architecture with important properties like flexibility and traffic
awareness for sixth generation (6G) wireless networks. In particular, we
consider a hierarchical resource allocation framework for the proposed smart
soft-RAN model, where the software-defined network (SDN) controller is the
first and foremost layer of the framework. This unit dynamically monitors the
network to select a network operation type on the basis of distributed or
centralized resource allocation architectures to perform decision-making
intelligently. In this paper, our aim is to make the network more scalable and
more flexible in terms of achievable data rate, overhead, and complexity
indicators. To this end, we introduce a new metric, throughput overhead
complexity (TOC), for the proposed machine learning-based algorithm, which
makes a trade-off between these performance indicators. In particular, the
decision making based on TOC is solved via deep reinforcement learning (DRL),
which determines an appropriate resource allocation policy. Furthermore, for
the selected algorithm, we employ the soft actor-critic method, which is more
accurate, scalable, and robust than other learning methods. Simulation results
demonstrate that the proposed smart network achieves better performance in
terms of TOC compared to fixed centralized or distributed resource management
schemes that lack dynamism. Moreover, our proposed algorithm outperforms
conventional learning methods employed in other state-of-the-art network
designs.
