LITE: Lightweight Channel Gain Estimation with Reduced X-Haul CSI Signaling in O-RAN
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917399879483392 |
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| author | Goez, David Piazzola, Marco Costa, Giulia Colpaert, Achiel Alonso, Rodney Martinez Beyazit, Esra Aycan Slamnik-Krijestorac, Nina Marquez-Barja, Johann M. Botero, Miguel Camelo |
| author_facet | Goez, David Piazzola, Marco Costa, Giulia Colpaert, Achiel Alonso, Rodney Martinez Beyazit, Esra Aycan Slamnik-Krijestorac, Nina Marquez-Barja, Johann M. Botero, Miguel Camelo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08458 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LITE: Lightweight Channel Gain Estimation with Reduced X-Haul CSI Signaling in O-RAN Goez, David Piazzola, Marco Costa, Giulia Colpaert, Achiel Alonso, Rodney Martinez Beyazit, Esra Aycan Slamnik-Krijestorac, Nina Marquez-Barja, Johann M. Botero, Miguel Camelo Networking and Internet Architecture 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. |
| title | LITE: Lightweight Channel Gain Estimation with Reduced X-Haul CSI Signaling in O-RAN |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2604.08458 |