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.

翻译：开放式无线电接入网（O-RAN）中的无蜂窝大规模多输入多输出（CF-MaMIMO）系统虽能提供高频谱效率，却受限于频繁的信道状态信息（CSI）交换——这不仅加重了前传/中传/回传（X-haul）带宽负担，更超出依赖未压缩CSI或重型预测器的现有方法的能力边界。为突破这些限制，我们提出LITE轻量级流水线：在O-RAN分布式单元（O-DU）部署一维卷积自编码器（AE），并于近实时RAN智能控制器（Near-RT-RIC）集成经挤压激励（SE）增强的双向长短期记忆（BiLSTM）预测器，从而在严苛传输与处理预算约束下实现短时域无轨迹感知预测。LITE采用50% CSI压缩与非对称SE-BiLSTM架构，相较于基准BiLSTM，模型复杂度降低83.39%，预测精度提升5%。经压缩感知训练后，轻量级智能轨迹估计器（LITE）相比BiLSTM基准仅产生6%的精度损失，表现优于独立与端到端策略。基于TensorRT优化的实现达到每秒14.7万次查询（QPS），吞吐量提升4.6倍。实验结果表明，LITE可提供兼容O-RAN架构切分的X-haul高效、低时延、就绪即用的信道增益预测方案。