  The flexibility offered by reconfigurable wireless networks, provide new
opportunities for various applications such as online AR/VR gaming,
high-quality video streaming and autonomous vehicles, that desire
high-bandwidth, reliable and low-latency communications. These applications
come with very stringent Quality of Service (QoS) requirements and increase the
burden over mobile networks. Currently, there is a huge spectrum scarcity due
to the massive data explosion and this problem can be solved by helps of
Reconfigurable Wireless Networks (RWNs) where nodes have reconfiguration and
perception capabilities. Therefore, a necessity of AI-assisted algorithms for
resource block allocation is observed. To tackle this challenge, in this paper,
we propose an actor-critic learning-based scheduler for allocating resource
blocks in a RWN. Various traffic types with different QoS levels are assigned
to our agents to provide more realistic results. We also include mobility in
our simulations to increase the dynamicity of networks. The proposed model is
compared with another actor-critic model and with other traditional schedulers;
proportional fair (PF) and Channel and QoS Aware (CQA) techniques. The proposed
models are evaluated by considering the delay experienced by user equipment
(UEs), successful transmissions and head-of-the-line delays. The results show
that the proposed model noticeably outperforms other techniques in different
aspects.
