TAPS: Three-Dimensional Amplitude-Phase-Spatial IQ Compression

The emergence of Open Radio Access Networks (O-RAN) promises intelligent sensing applications over 5G and beyond mobile networks provided 5G IQ samples for reference signals can be accessed from the RAN stack (DU) at the applications running over RAN Intelligent Controller (RICs). However, reference signals like DMRS generate multi-Gbps data streams per RU and across multiple RUs can easily overwhelm the network connection between the DU and RIC in typical deployments. Existing compression approaches—transform coding assuming signal sparsity, compressive sensing with high computational overhead, and machine-learning models requiring bespoke hardware or extensive training—either degrade sensing fidelity or violate sub-second near-RT RIC latency deadlines. We present TAPS, a Three-Dimensional Amplitude–Phase–Spatial algorithm that leverages (i) cross-symbol amplitude correlation, (ii) linear phase structure across subcarriers, and (iii) spatial redundancy across antennas, all within a CPU-only, O-RAN telemetry pipeline. With DMRS data collected from an outdoor 5G O-RAN testbed, TAPS delivers 10-30× data reduction and reduces reconstruction error by 50–67% relative to state-of-the-art baselines, while maintaining end-to-end compression and decompression latency below 100 ms, thereby overcoming the technical bottlenecks for high-fidelity sensing over O-RAN based 5G and beyond mobile networks.