The proliferation of transmissions in the RF spectrum demands robust and responsive signal analysis techniques to detect and label malicious activity. Traditional methods struggle to balance sensitivity and accuracy in real-time. This paper introduces Nomad, a modular system that combines global spectral pattern recognition with localized energy detection and classic signal processing methods for efficient and accurate RF analysis. Nomad's innovative architecture facilitates rapid development and deployment, while its intuitive visualizations provide actionable insights into even weak signals within complex RF environments.

NextG cellular networks must support diverse applications, such as interactive media streaming or robot control that have strict requirements on throughput, latency and reliability. These requirements must be met via optimizing wireless resources by utilizing application layer information, such as media streaming stall counts or robot pose estimates, along with network information, such as channel qualities and backlogs.

RAN Intelligent Control (RIC) has developed in parallel with Open Radio Access Networks (O-RAN) as a means of utilizing newly available interfaces. Focus has been largely on non-realtime (non-RT: > 1 sec) dealing with RAN management and offline training, and near-realtime (near-RT: 10 ms to 1 sec) dealing with UE load balancing and RAN configuration. We contend that the true power of RIC can be unleashed only with realtime (RT: < 100 μs) measurement, optimization, and control of RAN resources, corresponding to the cellular transmission time interval (TTI: 125 μs to 1 ms).