Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Real-Time (Near-RT) latency and computational constraints. While Transformer-based models are effective for sequence modeling, their quadratic complexity limits scalability in Near-RT RAN Intelligent Controller (RIC) analytics. This paper investigates a post-Transformer design paradigm for efficient radio telemetry forecasting. We propose a quantum-inspired many-body state-space tensor network that replaces self-attention with stable structured state-space dynamics kernels, enabling linear-time sequence modeling. Tensor-network factorizations in the form of Tensor Train (TT) / Matrix Product State (MPS) representations are employed to reduce parameterization and data movement in both input projections and prediction heads, while lightweight channel gating and mixing layers capture non-stationary cross-Key Performance Indicator (KPI) dependencies. The proposed model is instantiated as an agentic perceive-predict xApp and evaluated on a bespoke O-RAN KPI time-series dataset comprising 59,441 sliding windows across 13 KPIs, using Reference Signal Received Power (RSRP) forecasting as a representative use case. Our proposed Linear Quantum-Inspired State-Space (LiQSS) model is 10.8x-15.8x smaller and approximately 1.4x faster than prior structured state-space baselines. Relative to Transformer-based models, LiQSS achieves up to a 155x reduction in parameter count and up to 2.74x faster inference, without sacrificing forecasting accuracy.

翻译：第六代（6G）开放无线接入网络（O-RAN）中的主动与代理控制需要在严格的近实时（Near-RT）延迟和计算约束下实现控制级预测。尽管基于Transformer的模型在序列建模方面效果显著，但其二次复杂度限制了在近实时RAN智能控制器（RIC）分析中的可扩展性。本文研究了一种后Transformer设计范式，用于高效无线遥测预测。我们提出了一种量子启发的多体状态空间张量网络，该网络将自注意力机制替换为稳定的结构化状态空间动力学核，实现了线性时间序列建模。采用张量列（TT）/矩阵乘积态（MPS）形式的张量网络分解来减少输入投影和预测头的参数化及数据移动，同时利用轻量级通道门控与混合层捕获非平稳跨关键绩效指标（KPI）依赖关系。该模型实例化为一个感知-预测代理xApp，并在包含59,441个滑动窗口（涵盖13个KPI）的定制O-RAN KPI时间序列数据集上，以参考信号接收功率（RSRP）预测为代表性用例进行评估。我们提出的线性量子启发状态空间（LiQSS）模型相比先前的结构化状态空间基线模型体积缩小10.8倍至15.8倍，速度提升约1.4倍。相较于基于Transformer的模型，LiQSS在不牺牲预测精度的前提下，参数量最高减少155倍，推理速度最高提升2.74倍。