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

TL;DR

This paper investigates NextG semantic communications by uniting task-oriented and semantic paradigms within a multi-task deep learning framework, featuring a transmitter encoder and multiple decoders. It extends to multi-user setups via federated/distributed learning to address load and privacy, and analyzes security vulnerabilities and defenses against adversarial, poisoning, and backdoor attacks. A CIFAR-10 based demonstration over $SNR$-dependent AWGN and $Rayleigh$ channels shows joint optimization of reconstruction, semantic fidelity, and sensing within a single architecture. The work highlights potential for context-aware, secure, and resource-efficient 6G networks and outlines future directions, including AoI-based metrics and privacy-preserving FL protocols.

Abstract

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.

Figures (6)

• Figure 1: From conventional communications and sensing to task-oriented communications, semantic communications, and integrated sensing and communications.
• Figure 2: System model of multi-task learning for task-oriented communications, semantic communications, and integrated sensing and communications.
• Figure 3: Task accuracy vs. SNR (dB) of communication channel.
• Figure 4: Reconstruction loss vs. SNR (dB) of communication channel.
• Figure 5: Sensing accuracy vs. SNR (dB) of sensing channel (corresponding to channels to and from the target).