OPTIMAL 5G RESOURCE ALLOCATION FOR ULTRA-RELIABLE LOW LATENCY COMMUNICATION (URLLC) AND ENHANCED MOBILE BROADBAND (eMBB) USE CASES

Abstract

Due to the development of wireless networks for communication, a new issue has emerged. Users have become more organized (especially in 5G) by applications to ensure relatively easy spectrum access. The coexistence of eMBB and URLLC traffic has given rise to puncturing issues. The burstiness of URLLC traffic, which affects eMBB traffic by forcing its packets to wait until the spectrum is briefly free of URLLC traffic, is the main cause of this puncturing. In addition to Q-learning, resource block allocation was done in addition to joint power in order to get around this issue. The scheduling of resources was done using Q-learning in order to get the best multiplexing possible without puncturing eMBB resources. As a result, a scheduling pattern was created that enhanced reliability by increasing throughput and reducing latency. The suggested algorithm was implemented using MATLAB 5G and Deep Learning toolboxes. The algorithm (OLRT-Q) was compared to three other algorithms and there were some favourable conclusions. According to analysis, a 16% throughput boost over LRT-Q and a 47.7% increase over LR-Q at 2 Mbps (the case with the highest load) were recorded. At 1.5 Mbps, we noticed an increase of 12.2% and 33.16% in performance over LRT-Q and LRQ, respectively. It outperformed LRT-Q and LR-Q in the case of a 1 Mbps load scenario by 9.44% and 19.09%, respectively. There is an improvement in throughput by 13.36% compared to LR-Q and a 9.58% increase compared to LRT-Q under the lowest load scenario permitted by the standard (0.5 Mbps).