Interference-Aware Intelligent Scheduling for Virtualized Private 5G Networks

Private Fifth Generation (5G) Networks can quickly scale coverage and capacity for diverse industry verticals by using the standardized 3rd Generation Partnership Project (3GPP) and Open Radio Access Network (O-RAN) interfaces that enable disaggregation, network function virtualization, and hardware accelerators. These private network architectures often rely on multi-cell deployments to meet the stringent reliability and latency requirements of industrial applications. One of the main challenges in these dense multi-cell deployments is the interference to/from adjacent cells, which causes packet errors due to the rapid variations from air-interface transmissions. One approach towards this problem would be to use conservative modulation and coding schemes (MCS) for enhanced reliability, but it would reduce spectral efficiency and network capacity. To unlock the utilization of higher efficiency schemes, in this paper, we present our proposed machine-learning (ML) based interference prediction technique that exploits channel state information (CSI) reported by 5G User Equipments (UEs). This method is integrated into an in-house developed Next Generation RAN (NG-RAN) research platform, enabling it to schedule transmissions over the dynamic air-interface in an intelligent way. By achieving higher spectral efficiency and reducing latency with fewer retransmissions, this allows the network to serve more devices efficiently for demanding use cases such as mission critical Internet-of-Things (IoT) and extended reality applications. In this work, we also demonstrate our over-the-air (OTA) testbed with 8 cells and 16 5G UEs in an Industrial IoT (IIoT) Factory Automation layout, where 5G UEs are connected to various industrial components like automatic guided vehicles (AGVs), supply units, robotics arms, cameras, etc. Our experimental results show that our proposed Interference-aware Intelligent Scheduling (IAIS) method can achieve up to 39% and 70% throughput gains in low and high interference scenarios, respectively, compared to a widely adopted link-adaptation scheduling approach.