Listen-While-Talking: Toward dApp-based Real-Time Spectrum Sharing in O-RAN

This demo paper presents a dApp-based real-time spectrum sharing scenario where a 5th generation (5G) base station implementing the NR stack adapts its transmission and reception strategies based on the incumbent priority users in the Citizen Broadband Radio Service (CBRS) band. The dApp is responsible for obtaining relevant measurements from the Next Generation Node Base (gNB), running the spectrum sensing inference, and configuring the gNB with a control action upon detecting the primary incumbent user transmissions. This approach is built on dApps, which extend the O-RAN framework to the real-time and user plane domains. Thus, it avoids the need of dedicated Spectrum Access Systems (SASs) in the CBRS band. The demonstration setup is based on the open-source 5G OpenAirInterface (OAI) framework, where we have implemented a dApp interfaced with a gNB and communicating with a Commercial Off-the-Shelf (COTS) User Equipment (UE) in an over-the-air wireless environment. When an incumbent user has active transmission, the dApp will detect and inform the primary user presence to the gNB. The dApps will also enforce a control policy that adapts the scheduling and transmission policy of the Radio Access Network (RAN). This demo provides valuable insights into the potential of using dApp-based spectrum sensing with O-RAN architecture in next generation cellular networks.

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an open architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the user level. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective AI training. In this paper, we address this by introducing ns-O-RAN, a software framework that integrates a real-world, production-grade near-RT RIC with a 3GPP-based simulated environment on ns-3, enabling the development of xApps and automated large-scale data collection and testing of Deep Reinforcement Learning-driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture, combined with a state-of-the-art Convolutional Neural Network architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls its base stations. We evaluate the performance on a large-scale deployment, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.