  In this paper, we design a new flexible smart software-defined radio access
network (Soft-RAN) architecture with traffic awareness for sixth generation
(6G) wireless networks. In particular, we consider a hierarchical resource
allocation model 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
procedures to intelligently perform decision-making. In this paper, our aim is
to make the network more scalable and more flexible in terms of conflicting
performance indicators such as achievable data rate, overhead, and complexity
indicators. To this end, we introduce a new metric, i.e.,
throughput-overhead-complexity (TOC), for the proposed machine learning-based
algorithm, which supports 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
(SAC) 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 recent
state-of-the-art network designs.
