Edge-enhanced graph neural network for DU-CU placement and lightpath provision in X-Haul networks

Abstract

The emerging mobile services have imposed several new challenges on the radio access network (RAN), which stimulates its evolution from cloud-RAN to next-generation RAN (NG-RAN). In NG-RAN, baseband functions are redefined as the radio unit (RU), distributed unit (DU), and central unit (CU), which are, respectively, connected by optical front/mid/backhaul (X-Haul) networks. The DU-CU placement and lightpath provision need an elaborate strategy for allocating resources tailored to user requests. However, most existing methods fail to fully perceive network conditions and then make inappropriate solutions, which may result in over-consumption of processing and transmission resources. Therefore, we propose a reinforcement-learning-based DU-CU placement and lightpath provision strategy using an edge-enhanced graph neural network, i.e, EGNN-RL. The EGNN is leveraged to adequately exploit graph-structured features in X-Haul networks, while proximal-policy-optimization-based RL is introduced to maintain policy stability. In addition, this problem is formulated as an integer linear programming model to find the optimal solution. We validate the proposed strategy under different service types (i.e., enhanced mobile broadband, ultra-reliable low latency communication, and massive machine connections) and different numbers of services, respectively. The results show that our scheme can achieve higher resource efficiency compared with existing methods. Moreover, it also adapts to new networks with the same nodes through fine-tuning the model. This can decrease nearly 15% of the retaining time and obtain similar performance with retaining.