Haohuang Wen (The Ohio State University and SE-RAN.ai), Vinod Yegneswaran (SRI and SE-RAN.ai), Phillip Porras (SRI and SE-RAN.ai), Ashish Gehani (SRI and SE-RAN.ai), Prakhar Sharma (SRI and SE-RAN.ai), Zhiqiang Lin (The Ohio State University and SE-RAN.ai)
The current mobile network is migrating towards a programmable, interoperable, and cloud-native architecture, known as OpenRAN. This enables software-defined services to be integrated as modular applications (xApps and rApps) in a centralized RAN Intelligent Controller (RIC). While prior research has demonstrated a few xApps on OpenRAN for security, optimization, etc., a critical development challenge remains. We observe that a fundamental obstacle is the Telemetry Gap: an OpenRAN application has to acquire the necessary analytic telemetry which may not be supported by the corresponding RAN vendors. Unfortunately, the OpenRAN standard does not specify how to address this challenge, and current solutions are typically vendor lock-in, significantly limiting their portability. To bridge this gap, we present our preliminary work on TELERAN, a fully vendor-agnostic agent that enables protocol-level fine-grained visibility and seamless O-RAN integration for virtual RAN nodes at the edge by utilizing extended Berkeley Packet Filter (eBPF). It is driven by two synergistic cross-layer components: (1) an eBPF-based programmable filter that brings in universal and efficient cellular packet filtering at the OS kernel level, and (2) a user-space parser that reconstructs packet semantics based on ASN.1 specifications, enabling operators to customize and program various RAN telemetry. We have implemented a prototype of TELERAN, demonstrating that its seamless integration to two leading open-sourced RAN implementations, OpenAirInterface and srsRAN, with zero source code modification. We also show that TELERAN can be programmed for a wide range of telemetry types for both performance and security analytics, further supporting diverse xApp use cases on OpenRAN.