LAUSR

In the 5G era and beyond, new applications (APPs) have more stringent requirements for low latency. The cloud radio access network (C-RAN) with TDM-PON-based mobile fronthaul (MFH) and multi-access edge computing (MEC) has been regarded as an efficient architecture for serving latency-critical APPs. Many studies have focused on latency optimization issues in the MFH or MEC segment. However, the ultimate latency from the end-user’s perspective is end-to-end (E2E) latency, which includes the latency in both the MFH and MEC segments. When coordinating MFH with MEC, the E2E latency optimization may be mismatched and inefficient. To address this issue, we propose an APP-aware MAC scheduling scheme that allocates uplink time resources based on the MEC-backed conditions and APPs’ traffic dynamics. Accordingly, we introduce an AI-based traffic prediction approach to capture traffic fluctuations in various APPs. Simulations show that our proposed scheme improves the performances of latency violation ratio and average E2E latency by up to 30% and 8%, respectively.