Resource Allocation Scheduling for Delay Optimization in 6g Terahertz Communication Using Deep Reinforcement Learning

The desire for greater bandwidth is a problem for all network operators as the number of devices and bandwidth-hungry applications grows. These requirements cannot be provided by existing 5G cellular connectivity because spectrum resources are few and constrained. The increasing network density and number of devices puts a higher amount of traffic burden on the base station, which must be offloaded in order to decrease latency and satisfy mobile device users. A resource allocation scheduling method may pave the way for the effective use of restricted resources, which fulfils the aim of cellular communication and helps to reduce delay. Thus, proper resource allocation scheduling in Terahertz communication is therefore responsible for reducing latency in a communication system as well as increasing data rate, throughput, and system sum-rate.