LITE: Lightweight Channel Gain Estimation with Reduced X-Haul CSI Signaling in O-RAN
2026-04-09 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors address the problem of efficiently sending channel information in a wireless network system called CF-MaMIMO within O-RAN, where current methods use too much data and computing power. They propose LITE, a new method combining a simple data compressor at one network part and a smart predictor at another, which reduces data size by half and simplifies the model while keeping prediction accuracy high. Their approach performs better than previous models and runs much faster, making it practical for real-time use in O-RAN systems. Overall, the authors show a way to forecast wireless channel conditions using less bandwidth and less computing, enabling better network performance.
Cell-Free Massive MIMOOpen Radio Access Network (O-RAN)Channel State Information (CSI)AutoencoderBidirectional LSTMSqueeze-and-ExcitationCompressionChannel-gain predictionNear-Real-Time RAN Intelligent ControllerTensorRT
Authors
David Goez, Marco Piazzola, Giulia Costa, Achiel Colpaert, Rodney Martinez Alonso, Esra Aycan Beyazit, Nina Slamnik-Krijestorac, Johann M. Marquez-Barja, Miguel Camelo Botero
Abstract
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.