LITE: Lightweight Channel Gain Estimation with Reduced X-Haul CSI Signaling in O-RAN
This work addresses bandwidth and latency constraints for O-RAN deployments, offering a deployment-ready solution, though it appears incremental as it builds on existing compression and prediction methods.
The paper tackles the problem of frequent Channel State Information (CSI) exchanges straining bandwidth in Cell-Free Massive MIMO O-RAN systems by proposing LITE, a lightweight pipeline that achieves 50% CSI compression, reduces model complexity by 83.39%, improves accuracy by 5% over a baseline, and incurs only 6% accuracy loss while achieving a 4.6x throughput gain.
Cell-Free Massive Multiple-Input Multiple-Output (CF-MaMIMO) in Open Radio Access Network (O-RAN) promises high spectral efficiency but is limited by frequent Channel State Information (CSI) exchanges, which strain fronthaul/midhaul/backhaul (X-haul) bandwidth and exceed the capabilities of existing approaches relying on uncompressed CSI or heavy predictors. To overcome these constraints, we propose LITE, a lightweight pipeline combining a 1-D convolutional Autoencoder (AE) at the O-RAN Distributed Unit (O-DU) with a Squeeze-and-Excitation (SE)-enhanced Bidirectional Long Short-Term Memory (BiLSTM) predictor at the Near-Real-Time RAN Intelligent Controller (Near-RT-RIC), enabling short-horizon trajectory-unaware forecasting under strict transport and processing budgets. LITE applies 50% CSI compression and an asymmetric SE-BiLSTM, reducing model complexity by 83.39% while improving accuracy by 5% relative to a baseline BiLSTM. With compression-aware training, the Lightweight Intelligent Trajectory Estimator (LITE) incurs only 6% accuracy loss versus the BiLSTM baseline, outperforming independent and end-to-end strategies. A TensorRT-optimized implementation achieves 147k Queries per Second (QPS), a 4.6x throughput gain. These results demonstrate that LITE delivers X-haul-efficient, low-latency, and deployment-ready channel-gain prediction compatible with O-RAN splits.