QoS-Aware Cross Layer Optimization for 5G NR

In this paper, an adaptive cross-layer optimization algorithm (ACLOA) based on 5G quality of service (QoS) enhancement is proposed. The algorithm focuses on optimizing the data transmission performance in both uplink and downlink by dynamically monitoring and adjusting the network behavior. In the downlink, after the QoS flow is assigned to the data bearer in the SDAP layer, the system adjusts the buffer sizes and timer settings in the PDCP and RLC layers correspondingly based on the real-time monitored QoS parameters. Throughout the data transmission process, continuous QoS performance feedback drives fast and targeted adjustments across layers, optimizing the end-user experience. For uplink transmissions, upon receiving PDU data, the PHY layer extracts embedded QoS flow identifiers (QFIs) and communicates the requirements to the upstream layer, which triggers adaptive modifications of buffer sizes and timer settings at the RLC and PDCP layers, which in turn optimizes performance. Experimental results show that ACLOA’s ability to efficiently allocate network resources in real time utilizes real-time data to maintain peak performance in a variety of network scenarios and service demands, ultimately improving user satisfaction.