Open Radio Access Network (O-RAN) is positioned to play a pivotal role in shaping the future of telecommunications networks through open interfaces and virtualization, allowing interoperability between different vendors. As a key departure from single-operator managed RAN, a remote RAN intelligence controller (RIC) queries the gNB for the Key Performance Indicators (KPIs) that are required for making RAN control decisions, often leveraging advanced machine learning (ML) models. However, this repeated querying increases control traffic overhead on the so called E2 interface connecting the gNB to the RIC. To address this challenge, we utilize a method from Explainable Artificial Intelligence (XAI), specifically SHapley Additive exPlanations (SHAP), which quantifies the contribution of each requested KPI to a model's prediction. Furthermore, we explore two different methods of choosing the most discriminative KPIs influencing model's performance, so that a smaller subset of KPIs may be queried, thus lowering the overhead on the E2 interface. Our analysis reveals that a model trained for the task of traffic classification using as input only the fraction of the top contributing KPIs identified by SHAP reduces control traffic overhead by up to 33% with only 7% reduction in ML classification accuracy.

Spectrum sharing allows different protocols of the same standard (e.g., 802.11 family) or different standards (e.g., LTE and DVB) to coexist in overlapping frequency bands. As this paradigm continues to spread, wireless systems must also evolve to identify active transmitters and unauthorized waveforms in real time under intentional distortion of preambles, extremely low signal-to-noise ratios and challenging channel conditions. We overcome limitations of correlation-based preamble matching methods in such conditions through the design of T-PRIME: a Transformer-based machine learning approach. T-PRIME learns the structural design of transmitted frames through its attention mechanism, looking at sequence patterns that go beyond the preamble alone. The paper makes three contributions: First, it compares Transformer models and demonstrates their superiority over traditional methods and state-of-the-art neural networks. Second, it rigorously analyzes T-PRIME’s real-time feasibility on DeepWave’s AIR-T platform. Third, it utilizes an extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training, which is released along with the code for community use. Results reveal nearly perfect (i.e. > 98%) classification accuracy under simulated scenarios, showing 100% detection improvement over legacy methods in low SNR ranges, 97% classification accuracy for OTA single-protocol transmissions and up to 75% double-protocol classification accuracy in interference scenarios.

The RF environment in a secure space can be compromised by intentional transmissions of hard-to-detect underlay signals that overlap with a high-power baseline transmission. Specifically, we consider the case where a direct sequence spread spectrum (DSSS) signal is the underlay signal hiding within a baseline 4G Long-Term Evolution (LTE) signal. As compared to overt actions like jamming, the DSSS signal allows the LTE signal to be decodable, which makes it hard to detect. ICARUS presents a machine learning based framework that offers choices at the physical layer for inference with inputs of (i) in-phase and quadrature (IQ) samples only, (ii) cycle-frequency features obtained via cyclostationary signal processing (CSP), and (iii) fusion of both, to detect the underlay DSSS signal and its modulation type within LTE frames. ICARUS chooses the best inference method considering both the expected accuracy and the computational overhead. ICARUS is rigorously validated on multiple real-world datasets that include signals captured in cellular bands in the wild and the NSF POWDER testbed for advanced wireless research (PAWR). Results reveal that ICARUS can detect DSSS anomalies and its modulation scheme with 98-100% and 67 − 99% accuracy, respectively, while completing inference within 3 − 40 milliseconds on an NVIDIA A100 GPU platform.