Traffic-Aware Resource Scheduling With Carrier Aggregation for 230MHz Wireless Private Network

With the growing demand for high-frequency data collection and interactive services in the distribution substation areas, local wireless communication networks face significant challenges in capacity, coverage, security, and reliability. Although existing 230 MHz wireless private networks have adopted carrier aggregation, adaptive modulation and other technologies to improve spectrum utilization, the effectiveness is limited due to the characteristics of distribution area services. To address this issue, this paper proposes a traffic-aware approach for resource scheduling with carrier aggregation in 230 MHz wireless private networks. The approach first calculates the average arrival time and size of historical data for different users (or terminals) and then uses Q-Learning algorithm to activate component carriers and allocate resource blocks based on the actual needs of different users. Simulation results show that the proposed approach could achieve better spectrum utilization and lower power consumption of terminals while meeting the differentiated requirements of users.