Multicast-native DU–CU deployment and x-haul scheduling for 5G-Advanced optical-wireless converged networks

Real-time immersive media has sparked a wave of application innovations (e.g., live streaming), fueling the rapid prosperity of multicast services supported in 5G-Advanced radio access networks (5G-A RANs). However, multicast will involve transmitting multiple copies of service data within the RAN, resulting in resource overprovisioning for both baseband processing and optical x-haul transmission. This challenge will increase the expenditure for RAN infrastructure, necessitating considerable concern from telecom operators. Despite its significance, this issue has not been thoroughly explored in existing literature. To address this issue, this paper proposes a mixed-resource-sharing (MRS) scheme, enabling identical multicast requests from the same or different active antenna units to share common processing and transmission resources. By building upon this scheme, we propose a mixed-integer linear programming model to optimize baseband-function deployment and x-haul scheduling, with the aim of minimizing the number of activated processing pools, consumed processing and bandwidth resources, as well as utilized wavelengths. We then develop an MRS-enabled heuristic to further adapt this scheme to large-scale network paradigms. For validation, we compare our proposals with the traditional solution in the existing literature, which simply emulates multicast as multiple independent unicasts. Simulations are conducted in both small-scale and large-scale networks across different cases. Numerical results demonstrate that our proposals outperform the existing solution, particularly in terms of processing resource saving, with improvements exceeding 12%.