# Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

arXiv: https://arxiv.org/abs/2003.00866 · HTML: https://mlsanity.com/paper/2003.00866/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Low-Latency Federated Learning over Wireless Channels with Differential Privacy](https://mlsanity.com/paper/2106.13039/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design](https://mlsanity.com/paper/2105.06256/) (2021)
- [Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization](https://mlsanity.com/paper/2101.11799/) (2021)
- [DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey](https://mlsanity.com/paper/2503.09956/) (2025)
- [Blockchained Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2305.04513/) (2023)
- [Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy](https://mlsanity.com/paper/2304.04164/) (2023)
- [On the Combination of AI and Wireless Technologies: 3GPP Standardization Progress](https://mlsanity.com/paper/2407.10984/) (2024)
- [Knowledge Distillation For Wireless Edge Learning](https://mlsanity.com/paper/2104.06374/) (2021)
- [Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning](https://mlsanity.com/paper/2310.14456/) (2023)
- [A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization](https://mlsanity.com/paper/2310.09665/) (2023)
- [Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning](https://mlsanity.com/paper/2607.19759/) (2026)

## Abstract (arXiv)

Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.