# A Survey on Integrated Sensing, Communication, and Computation arXiv: https://arxiv.org/abs/2408.08074 ยท HTML: https://mlsanity.com/paper/2408.08074/ ## 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 (most cited first, 30 of 102) - [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016) - [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014) - [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015) - [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015) - [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017) - [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019) - [Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training](https://mlsanity.com/paper/1908.06066/) (2019) - [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016) - [Stacked Attention Networks for Image Question Answering](https://mlsanity.com/paper/1511.02274/) (2015) - [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023) - [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018) - [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018) - [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019) - [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019) - [What Makes Multi-modal Learning Better than Single (Provably)](https://mlsanity.com/paper/2106.04538/) (2021) - [Generating Images from Captions with Attention](https://mlsanity.com/paper/1511.02793/) (2015) - [Analyzing the Behavior of Visual Question Answering Models](https://mlsanity.com/paper/1606.07356/) (2016) - [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021) - [Translating Videos to Natural Language Using Deep Recurrent Neural Networks](https://mlsanity.com/paper/1412.4729/) (2014) - [Towards an Intelligent Edge: Wireless Communication Meets Machine Learning](https://mlsanity.com/paper/1809.00343/) (2018) - [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022) - [Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)](https://mlsanity.com/paper/1412.6632/) (2014) - [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021) - [Dynamic Memory Networks for Visual and Textual Question Answering](https://mlsanity.com/paper/1603.01417/) (2016) - [On Analog Gradient Descent Learning over Multiple Access Fading Channels](https://mlsanity.com/paper/1908.07463/) (2019) - [Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness](https://mlsanity.com/paper/2004.00490/) (2020) - [Cross-media Structured Common Space for Multimedia Event Extraction](https://mlsanity.com/paper/2005.02472/) (2020) - [Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices](https://mlsanity.com/paper/2109.15258/) (2021) ## Built on by (most cited first, 12 of 12) - [Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework](https://mlsanity.com/paper/2504.05187/) (2025) - [Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2508.15185/) (2025) - [Empowering Intelligent Low-altitude Economy with Large AI Model Deployment](https://mlsanity.com/paper/2505.22343/) (2025) - [Differentiable Projection-based Learn to Optimize in Wireless Network-Part I: Convex Constrained (Non-)Convex Programming](https://mlsanity.com/paper/2502.00053/) (2025) - [Integrated Computation and Communication with Fiber-optic Transmissions](https://mlsanity.com/paper/2503.02165/) (2025) - [GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks](https://mlsanity.com/paper/2505.05224/) (2025) - [Secure mmWave Beamforming with Proactive-ISAC Defense Against Beam-Stealing Attacks](https://mlsanity.com/paper/2508.02856/) (2025) - [FAWN: A MultiEncoder Fusion-Attention Wave Network for Integrated Sensing and Communication Indoor Scene Inference](https://mlsanity.com/paper/2509.14968/) (2025) - [EPARA: Parallelizing Categorized AI Inference in Edge Clouds](https://mlsanity.com/paper/2511.00603/) (2025) - [Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory](https://mlsanity.com/paper/2603.23967/) (2026) - [CRB-Guided Framework Design and Resource Allocation for Indoor mmWave ISCC Systems](https://mlsanity.com/paper/2605.29939/) (2026) - [AI-Empowered UAV-Assisted Backscatter Localization and ISAC for Zero-Energy IoT: A Comprehensive Survey](https://mlsanity.com/paper/2606.23125/) (2026) ## 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.