# Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing

arXiv: https://arxiv.org/abs/2401.01531 · HTML: https://mlsanity.com/paper/2401.01531/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Adversarial Machine Learning in Wireless Communications using RF Data: A Review](https://mlsanity.com/paper/2012.14392/) (2020)
- [Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspects](https://mlsanity.com/paper/2212.09668/) (2022)
- [Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks](https://mlsanity.com/paper/2212.11205/) (2022)
- [Age of Information in Deep Learning-Driven Task-Oriented Communications](https://mlsanity.com/paper/2301.04298/) (2023)
- [Joint Sensing and Semantic Communications with Multi-Task Deep Learning](https://mlsanity.com/paper/2311.05017/) (2023)

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

- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm](https://mlsanity.com/paper/2512.04405/) (2025)
- [Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning](https://mlsanity.com/paper/2411.10385/) (2024)
- [Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AI](https://mlsanity.com/paper/2509.08151/) (2025)
- [Security-aware Semantic-driven ISAC via Paired Adversarial Residual Networks](https://mlsanity.com/paper/2509.20835/) (2025)
- [Goal-Oriented Multi-Agent Semantic Networking: Unifying Intents, Semantics, and Intelligence](https://mlsanity.com/paper/2512.01035/) (2025)
- [Privacy-Preserving Semantic Communications via Multi-Task Learning and Adversarial Perturbations](https://mlsanity.com/paper/2512.24452/) (2025)
- [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026)
- [When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization](https://mlsanity.com/paper/2605.05514/) (2026)
- [Wireless Backdoor Attack and Defense for Semantic Communications over Multiple Access Channel](https://mlsanity.com/paper/2606.30595/) (2026)
- [Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications](https://mlsanity.com/paper/2606.31973/) (2026)

## Abstract (arXiv)

This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) communication networks through the integration of multi-task learning. This approach employs deep neural networks representing a dedicated encoder at the transmitter and multiple task-specific decoders at the receiver, collectively trained to handle diverse tasks including semantic information preservation, source input reconstruction, and integrated sensing and communications. To extend the applicability from point-to-point links to multi-receiver settings, we envision the deployment of decoders at various receivers, where decentralized learning addresses the challenges of communication load and privacy concerns, leveraging federated learning techniques that distribute model updates across decentralized nodes. However, the efficacy of this approach is contingent on the robustness of the employed deep learning models. We scrutinize potential vulnerabilities stemming from adversarial attacks during both training and testing phases. These attacks aim to manipulate both the inputs at the encoder at the transmitter and the signals received over the air on the receiver side, highlighting the importance of fortifying semantic communications against potential multi-domain exploits. Overall, the joint and robust design of task-oriented communications, semantic communications, and integrated sensing and communications in a multi-task learning framework emerges as the key enabler for context-aware, resource-efficient, and secure communications ultimately needed in NextG network systems.