# AI-enabled Future Wireless Networks: Challenges, Opportunities and Open Issues

arXiv: https://arxiv.org/abs/2103.04536 · HTML: https://mlsanity.com/paper/2103.04536/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2021.

## Built on by (most cited first, 11 of 11)

- [Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis](https://mlsanity.com/paper/2210.04604/) (2022)
- [Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul](https://mlsanity.com/paper/2211.07466/) (2022)
- [The Internet of Senses: Building on Semantic Communications and Edge Intelligence](https://mlsanity.com/paper/2212.10748/) (2022)
- [Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks](https://mlsanity.com/paper/2402.08022/) (2024)
- [Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models](https://mlsanity.com/paper/2502.07211/) (2025)
- [Deep-Learning-Based Device Fingerprinting for Increased LoRa-IoT Security: Sensitivity to Network Deployment Changes](https://mlsanity.com/paper/2208.14964/) (2022)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks](https://mlsanity.com/paper/2209.07367/) (2022)
- [Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity](https://mlsanity.com/paper/2301.05391/) (2023)
- [Strategic Demand-Planning in Wireless Networks: Can Generative-AI Save Spectrum and Energy?](https://mlsanity.com/paper/2407.02292/) (2024)
- [Intelligent Attacks and Defense Methods in Federated Learning-enabled Energy-Efficient Wireless Networks](https://mlsanity.com/paper/2504.18519/) (2025)

## Abstract (arXiv)

A plethora of demanding services and use cases mandate a revolutionary shift in the management of future wireless network resources. Indeed, when tight quality of service demands of applications are combined with increased complexity of the network, legacy network management routines will become unfeasible in 6G. Artificial Intelligence (AI) is emerging as a fundamental enabler to orchestrate the network resources from bottom to top. AI-enabled radio access and AI-enabled core will open up new opportunities for automated configuration of 6G. On the other hand, there are many challenges in AI-enabled networks that need to be addressed. Long convergence time, memory complexity, and complex behaviour of machine learning algorithms under uncertainty as well as highly dynamic channel, traffic and mobility conditions of the network contribute to the challenges. In this paper, we survey the state-of-art research in utilizing machine learning techniques in improving the performance of wireless networks. In addition, we identify challenges and open issues to provide a roadmap for the researchers.