Deep Reinforcement Learning Based Resource Allocation Method in Future Wireless Networks with Blockchain Assisted MEC Network

We present a blockchain-assisted mobile edge computing architecture for adaptive resource distribution in wireless communication systems, where the blockchain acts as an overhead system that provide command and control functionalities. In this context, achieving consensus across nodes while also ensuring the functionality of both MEC and blockchain systems is a big difficulty. Furthermore, resource distribution, frame size, and the number of sequential blocks generated by each contributor are important to Blockchain aided MEC functionality. As a result, a strategy for dynamic resource distribution and block creation is presented. To strengthen the efficiency of the overlapped blockchain system and enhance the quality of services (QoS) of the clients in the technologies to facilitate MEC system, spectrum allocation, frame size, and number of developing blocks for each distributor are framed as a joint optimization method that takes into account time-varying communication channels and MEC server saturation is defined. We use deep reinforcement learning (RAMBAN) to address this issue because standard approaches are ineffective. The simulation findings demonstrate that the efficacy of the suggested strategy when compared to different baseline approaches.