mlsanityData to 14 Oct 2026

A Survey on Integrated Sensing, Communication, and Computation

arXiv 2408.08074 ↗First posted 15 Aug 2024cs.IT cs.AI cs.LGcited by 12

Not on the map. Why?

Abstract (arXiv)

The forthcoming generation of wireless technology, 6G, aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modules: Sensing for information acquisition, communication for information sharing, and computation for information processing and decision-making. These modules are intricately linked, especially in complex tasks such as edge learning and inference. However, the performance of these modules is interdependent, creating a resource competition for time, energy, and bandwidth. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge, but they fall short of meeting the extreme performance requirements. To overcome these limitations, it is essential to develop new techniques that comprehensively integrate sensing, communication, and computation. This integrated approach, known as Integrated Sensing, Communication, and Computation (ISCC), offers a systematic perspective for enhancing task performance. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then discusses the benefits, functions, and challenges of ISCC. Subsequently, the state-of-the-art signal designs for ISCC, along with network resource management strategies specifically tailored for ISCC are explored. Furthermore, this paper discusses the exciting research opportunities that lie ahead for implementing ISCC in future advanced networks, and the unresolved issues requiring further investigation. ISCC is expected to unlock the full potential of intelligent connectivity, paving the way for groundbreaking applications and services.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 102 of its 373 references that are in the map. First posted on arXiv 15 Aug 2024.

Builds on

102 of 373 references.

References · 102

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  3. 2015VQA: Visual Question Answeringcited by 2,506
  4. 2015Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Modelscited by 1,362
  5. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  6. 2019VL-BERT: Pre-training of Generic Visual-Linguistic Representationscited by 954
  7. 2019Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-trainingcited by 451
  8. 2016Hierarchical Question-Image Co-Attention for Visual Question Answeringcited by 278
  9. 2015Stacked Attention Networks for Image Question Answeringcited by 273
  10. 2023Multimodal Deep Learningcited by 258
  11. 2018Federated Learning via Over-the-Air Computationcited by 219
  12. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
38 more
  1. 2019F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Cloudscited by 171
  2. 2019Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Cloudscited by 165
  3. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 160
  4. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 153
  5. 2021What Makes Multi-modal Learning Better than Single (Provably)cited by 153
  6. 2015Generating Images from Captions with Attentioncited by 136
  7. 2016Analyzing the Behavior of Visual Question Answering Modelscited by 119
  8. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
  9. 2014Translating Videos to Natural Language Using Deep Recurrent Neural Networkscited by 95
  10. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 95
  11. 2022Semantic Communications for Future Internet: Fundamentals, Applications, and Challengescited by 95
  12. 2014Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)cited by 94
  13. 2021Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyondcited by 91
  14. 2016Dynamic Memory Networks for Visual and Textual Question Answeringcited by 62
  15. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 62
  16. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  17. 2020Cross-media Structured Common Space for Multimedia Event Extractioncited by 61
  18. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
  19. 2022DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Datasetcited by 53
  20. 2021What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligencecited by 50
  21. 2022A survey of multimodal deep generative modelscited by 47
  22. 2021The Road Towards 6G: A Comprehensive Surveycited by 40
  23. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
  24. 2022Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AIcited by 33
  25. 2015What's Cookin'? Interpreting Cooking Videos using Text, Speech and Visioncited by 31
  26. 2017SAR image despeckling through convolutional neural networkscited by 30
  27. 2021Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoffcited by 30
  28. 2021Federated Edge Learning with Misaligned Over-The-Air Computationcited by 30
  29. 2023Multi-modal Machine Learning in Engineering Design: A Review and Future Directionscited by 27
  30. 2023Real-Time Digital Twins: Vision and Research Directions for 6G and Beyondcited by 26
  31. 2023Green Edge AI: A Contemporary Surveycited by 25
  32. 2014Majority Vote of Diverse Classifiers for Late Fusioncited by 23
  33. 2016Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusioncited by 23
  34. 2015Weakly-Supervised Alignment of Video With Textcited by 21
  35. 2019ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communicationscited by 21
  36. 2015EmoNets: Multimodal deep learning approaches for emotion recognition in videocited by 20
  37. 2019Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learningcited by 20
  38. 2021Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challengescited by 20

50 most cited of 102 All 102 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 9; 2026: 3.

12 papers.

  1. 2025Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Frameworkcited by 5
  2. 2025Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learningcited by 5
  3. 2025Empowering Intelligent Low-altitude Economy with Large AI Model Deploymentcited by 4
  4. 2025Differentiable Projection-based Learn to Optimize in Wireless Network-Part I: Convex Constrained (Non-)Convex Programmingcited by 0
  5. 2025Integrated Computation and Communication with Fiber-optic Transmissionscited by 0
  6. 2025GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networkscited by 0
  7. 2025Secure mmWave Beamforming with Proactive-ISAC Defense Against Beam-Stealing Attackscited by 0
  8. 2025FAWN: A MultiEncoder Fusion-Attention Wave Network for Integrated Sensing and Communication Indoor Scene Inferencecited by 0
  9. 2025EPARA: Parallelizing Categorized AI Inference in Edge Cloudscited by 0
  10. 2026Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factorycited by 0
  11. 2026CRB-Guided Framework Design and Resource Allocation for Indoor mmWave ISCC Systemscited by 0
  12. 2026AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Surveycited by 0