# Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI

arXiv: https://arxiv.org/abs/2207.00969 · HTML: https://mlsanity.com/paper/2207.00969/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 60 references that are in the map.
- First posted on arXiv 3 Jul 2022.

## Builds on (most cited first, 30 of 38)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Convergence of Edge Computing and Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/1907.08349/) (2019)
- [One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://mlsanity.com/paper/2001.05713/) (2020)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Communication-Efficient Edge AI: Algorithms and Systems](https://mlsanity.com/paper/2002.09668/) (2020)
- [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018)
- [Wireless Network Intelligence at the Edge](https://mlsanity.com/paper/1812.02858/) (2018)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Communication-Computation Trade-Off in Resource-Constrained Edge Inference](https://mlsanity.com/paper/2006.02166/) (2020)
- [Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices](https://mlsanity.com/paper/2109.15258/) (2021)
- [What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence](https://mlsanity.com/paper/2110.00196/) (2021)
- [Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning](https://mlsanity.com/paper/1903.03472/) (2019)
- [Resource Allocation for Multiuser Edge Inference with Batching and Early Exiting (Extended Version)](https://mlsanity.com/paper/2204.05223/) (2022)
- [Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges](https://mlsanity.com/paper/2104.11457/) (2021)
- [Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inference](https://mlsanity.com/paper/2105.13618/) (2021)
- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://mlsanity.com/paper/2104.09027/) (2021)
- [Deep Task-Based Quantization](https://mlsanity.com/paper/1908.06845/) (2019)
- [Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning](https://mlsanity.com/paper/2003.04544/) (2020)
- [Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication](https://mlsanity.com/paper/2201.08512/) (2022)
- [Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference](https://mlsanity.com/paper/1907.12475/) (2019)
- [Dynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlines](https://mlsanity.com/paper/2005.12235/) (2020)
- [Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model](https://mlsanity.com/paper/2104.10378/) (2021)
- [Task-Based Analog-to-Digital Converters](https://mlsanity.com/paper/2009.14088/) (2020)
- [BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization](https://mlsanity.com/paper/2010.00195/) (2020)
- [Reconfigurable Intelligent Surface for Green Edge Inference](https://mlsanity.com/paper/1912.00820/) (2019)
- [Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels](https://mlsanity.com/paper/2010.04061/) (2020)

## Built on by (most cited first, 30 of 33)

- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [An overview of domain-specific foundation model: key technologies, applications and challenges](https://mlsanity.com/paper/2409.04267/) (2024)
- [RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks](https://mlsanity.com/paper/2403.16397/) (2024)
- [Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing](https://mlsanity.com/paper/2403.12400/) (2024)
- [Task-Oriented Over-the-Air Computation for Multi-Device Edge AI](https://mlsanity.com/paper/2211.01255/) (2022)
- [Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges](https://mlsanity.com/paper/2405.17454/) (2024)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing](https://mlsanity.com/paper/2412.06861/) (2024)
- [Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN](https://mlsanity.com/paper/2402.02729/) (2024)
- [Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting](https://mlsanity.com/paper/2307.15316/) (2023)
- [Collaborative Edge AI Inference over Cloud-RAN](https://mlsanity.com/paper/2404.06007/) (2024)
- [Revisiting Outage for Edge Inference Systems](https://mlsanity.com/paper/2504.03686/) (2025)
- [Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2508.15185/) (2025)
- [Integrated Sensing-Communication-Computation for Edge Artificial Intelligence](https://mlsanity.com/paper/2306.01162/) (2023)
- [Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI](https://mlsanity.com/paper/2306.06603/) (2023)
- [Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edge](https://mlsanity.com/paper/2407.09873/) (2024)
- [Robust DNN Partitioning and Resource Allocation Under Uncertain Inference Time](https://mlsanity.com/paper/2503.21476/) (2025)
- [Communication Efficient Cooperative Edge AI via Event-Triggered Computation Offloading](https://mlsanity.com/paper/2501.02001/) (2025)
- [Task-oriented Over-the-air Computation for Edge-device Co-inference with Balanced Classification Accuracy](https://mlsanity.com/paper/2407.00955/) (2024)
- [Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks](https://mlsanity.com/paper/2504.03052/) (2025)
- [EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning](https://mlsanity.com/paper/2310.13367/) (2023)
- [Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis](https://mlsanity.com/paper/2409.11240/) (2024)
- [Integrating Semantic Communication and Human Decision-Making into an End-to-End Sensing-Decision Framework](https://mlsanity.com/paper/2412.05103/) (2024)
- [Joint Transmission and Deblurring: A Semantic Communication Approach Using Events](https://mlsanity.com/paper/2501.09396/) (2025)
- [Dynamic Pricing for On-Demand DNN Inference in the Edge-AI Market](https://mlsanity.com/paper/2503.04521/) (2025)
- [Orchestrating Multimodal DNN Workloads in Wireless Neural Processing](https://mlsanity.com/paper/2603.02109/) (2026)
- [Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems](https://mlsanity.com/paper/2603.02579/) (2026)
- [Channel-Adaptive Edge AI: Maximizing Inference Throughput by Adapting Computational Complexity to Channel States](https://mlsanity.com/paper/2603.03146/) (2026)
- [Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks](https://mlsanity.com/paper/2604.10662/) (2026)
- [Task-Semantic Graph-Driven Distributed Agent Networking for Underwater Target Tracking](https://mlsanity.com/paper/2605.15528/) (2026)

## Abstract (arXiv)

This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge. In this system, multiple ISAC devices perform radar sensing to obtain multi-view data, and then offload the quantized version of extracted features to a centralized edge server, which conducts model inference based on the cascaded feature vectors. Under this setup and by considering classification tasks, we measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain, which is defined as the distance of two classes in the Euclidean feature space under normalized covariance. To maximize the discriminant gain, we first quantify the influence of the sensing, computation, and communication processes on it with a derived closed-form expression. Then, an end-to-end task-oriented resource management approach is developed by integrating the three processes into a joint design. This integrated sensing, computation, and communication (ISCC) design approach, however, leads to a challenging non-convex optimization problem, due to the complicated form of discriminant gain and the device heterogeneity in terms of channel gain, quantization level, and generated feature subsets. Remarkably, the considered non-convex problem can be optimally solved based on the sum-of-ratios method. This gives the optimal ISCC scheme, that jointly determines the transmit power and time allocation at multiple devices for sensing and communication, as well as their quantization bits allocation for computation distortion control. By using human motions recognition as a concrete AI inference task, extensive experiments are conducted to verify the performance of our derived optimal ISCC scheme.