Complex event recognition (CER) refers to identify-ing specific patterns composed of several primitive events in event stores. Since full-scanning event stores to identify primitive events that hold query constraint conditions incurs costly I/O overhead, a mainstream and practical approach is to use index techniques to obtain these events. However, prior index-based approaches suffer from significant I/O and sorting overhead when processing the query with high predicate selectivity or long query window, which leads to high query latency. To address this issue, we propose ACER, a Range Bitmap-based index, to accelerate CER. Firstly, ACER achieves a low index space overhead by grouping the events with the same type into a cluster and compressing the cluster data, reducing I/O overhead when reading indexes. Secondly, ACER builds Range Bitmaps for queried attributes and ensures that the events of each cluster in the index block are chronologically ordered. Then, ACER can always obtain ordered query results for a specific event type through merge operations, avoiding sorting overhead. Most importantly, ACER avoids unnecessary disk accesses in indexes and events via window-wise filtering, thus reducing the I/O overhead further. Lastly, we propose an enhanced version of ACER (ACER-E) by optimizing the read/write operation of index blocks and variable query order. Our extensive experiments demonstrate that ACER and ACER-E reduce the query latency by up to one order of magnitude compared with SOTA techniques.

In the rapidly evolving landscape of telecommunications, Radio Access Network (RAN) optimization is critical for maintaining high network performance and adapting to diverse service requirements. Traditionally, RAN optimization has relied heavily on manual adjustments by human experts, lacking in intelligent decision-making model. However, applying decision-making model to RAN optimization is challenged by high interaction costs and feedback delays. To address these challenges, we introduce a World Model aided Parameter Adjustment Decision and Evaluation System (WMDE), utilizing a World Model framework with offline reinforcement learning to adjust RAN parameters. WMDE, integrating the Transformer-Informed Adjustment Decision Net (TADNet) and the Causal Adjustment Effect Evaluation Net (CAENet). The WMDE system sidesteps real-time network interaction in model training with CAENet's causal estimation, cutting interaction costs. Meanwhile, TADNet employs its Transformer structure and data processing to provide a long-term, global perspective on adjustment effects, reducing feedback delay issues. Utilizing real-world operational RAN parameter adjustment data, our experiments validate the effectiveness of WMDE in decision-making for RAN parameter adjustment.

Due to the growth of densification and multi-band services in emerging cellular networks, the next generation NodeB will confront with a plethora of handover-related Configuration and Optimization Parameters (COPs). Adjusting the handover-related COPs will directly impact the networks Key Performance Indicators (KPIs). Thus, accurately estimating the effect of the handover-related COPs adjustment on KPI values and modeling the COP-KPI relationship can provide guidelines for intelligent network management. Existing studies have applied machine learning to model the intricate relationship between COP and KPI, but these methods merely exploit their correlations. In this paper, considering that there exist the causal effects between COPs and KPIs that go beyond the correlation analysis of them, we propose the causal inference aided handover parameter adjusting effect estimation and design the Causal-Stable Decomposition Network (CSDnet). Specifically, we propose the causal-stable deconstructor module based on self-attention mechanism to characterize the complex causality-stability of KPI induced by the COP adjustment. To ensure the training, we propose the gradient matching loss and the consistency constraint loss. Then, we design the stable decoder and the causal decoder that incorporate the causal information into COP-KPI modeling. Extensive experiments on the COP-KPI dataset collected from the real-world cellular networks verify the effectiveness of CSDnet.

The development of 5G network technology has given rise to a large number of Configuration and Optimization Parameters (COPs). Adjusting COPs can help to achieve optimal Key Performance Indicators (KPIs) that have an impact on the network, hence improve user Quality of Service (QoS). Configuring parameters manually to be optimal is unrealistic. Based on data correlation, machine learning (ML) models are investigated to learn KPIs behavior with COPs adjustment. However, there are spurious associations between the COP and KPI generated by intermediate variables in realistic scenarios. Traditional ML models are insufficient to give solutions. Thus, we combine the causal inference to estimate the COP adjustment effect. Specifically, we designed a model named Dynamic Causal Deconfounder Network (DCDN). DCDN learns complex and long-term dependencies among indicators through the self-attention mechanism. And from the perspective of causal inference, we reduce spurious associations based on the idea of adversarial for predicting the KPI value accurately. Extensive experiments on the COP-KPI dataset collected from real-world cellular networks validate the effectiveness of the model.