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Today's radio access networks (RANs) are monolithic entities which oftenoperate statically on a given set of parameters for the entirety of theiroperations. To implement realistic and effective spectrum sharing policies,RANs will need to seamlessly and intelligently change their operationalparameters. In stark contrast with existing paradigms, the new O-RANarchitectures for 5G-and-beyond networks (NextG) separate the logic thatcontrols the RAN from its hardware substrate, allowing unprecedented real-timefine-grained control of RAN components. In this context, we propose theChannel-Aware Reactive Mechanism (ChARM), a data-driven O-RAN-compliantframework that allows (i) sensing the spectrum to infer the presence ofinterference and (ii) reacting in real time by switching the distributed unit(DU) and radio unit (RU) operational parameters according to a specifiedspectrum access policy. ChARM is based on neural networks operating directly onunprocessed I/Q waveforms to determine the current spectrum context. ChARM doesnot require any modification to the existing 3GPP standards. It is designed tooperate within the O-RAN specifications, and can be used in conjunction withother spectrum sharing mechanisms (e.g., LTE-U, LTE-LAA or MulteFire). Wedemonstrate the performance of ChARM in the context of spectrum sharing amongLTE and Wi-Fi in unlicensed bands, where a controller operating over a RANIntelligent Controller (RIC) senses the spectrum and switches cell frequency toavoid Wi-Fi. We develop a prototype of ChARM using srsRAN, and leverage theColosseum channel emulator to collect a large-scale waveform dataset to trainour neural networks with. Experimental results show that ChARM achievesaccuracy of up to 96% on Colosseum and 85% on an over-the-air testbed,demonstrating the capacity of ChARM to exploit the considered spectrumchannels.

ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic Control