# A QoE-Aware Split Inference Accelerating Algorithm for NOMA-based Edge Intelligence

arXiv: https://arxiv.org/abs/2409.16537 · HTML: https://mlsanity.com/paper/2409.16537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services](https://mlsanity.com/paper/1801.08618/) (2018)
- [CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices](https://mlsanity.com/paper/2012.03257/) (2020)
- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses](https://mlsanity.com/paper/2302.08418/) (2023)
- [HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse](https://mlsanity.com/paper/2211.03300/) (2022)
- [Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence](https://mlsanity.com/paper/2007.09072/) (2020)
- [Deep Learning Based Caching for Self-Driving Car in Multi-access Edge Computing](https://mlsanity.com/paper/1810.01548/) (2018)
- [Impatient Queuing for Intelligent Task Offloading in Multi-Access Edge Computing](https://mlsanity.com/paper/2105.11727/) (2021)

## Abstract (arXiv)

Even the AI has been widely used and significantly changed our life, deploying the large AI models on resource limited edge devices directly is not appropriate. Thus, the model split inference is proposed to improve the performance of edge intelligence, in which the AI model is divided into different sub models and the resource-intensive sub model is offloaded to edge server wirelessly for reducing resource requirements and inference latency. However, the previous works mainly concentrate on improving and optimizing the system QoS, ignore the effect of QoE which is another critical item for the users except for QoS. Even the QoE has been widely learned in EC, considering the differences between task offloading in EC and split inference in EI, and the specific issues in QoE which are still not addressed in EC and EI, these algorithms cannot work effectively in edge split inference scenarios. Thus, an effective resource allocation algorithm is proposed in this paper, for accelerating split inference in EI and achieving the tradeoff between inference delay, QoE, and resource consumption, abbreviated as ERA. Specifically, the ERA takes the resource consumption, QoE, and inference latency into account to find the optimal model split strategy and resource allocation strategy. Since the minimum inference delay and resource consumption, and maximum QoE cannot be satisfied simultaneously, the gradient descent based algorithm is adopted to find the optimal tradeoff between them. Moreover, the loop iteration GD approach is developed to reduce the complexity of the GD algorithm caused by parameter discretization. Additionally, the properties of the proposed algorithms are investigated, including convergence, complexity, and approximation error. The experimental results demonstrate that the performance of ERA is much better than that of the previous studies.