Consolidate Viability and Information Theories for Task-Oriented Communications: A Homeostasis Solution (2311.14917v1)
Abstract: The next generation of cellular networks, 6G, is expected to offer a range of exciting applications and services, including holographic communication, machine-to-machine communication, and data sensing from millions of devices. There is an incremental exhaustion of the spectral resources. It is crucial to efficiently manage these resources through value-driven approaches that eliminate waste and continually enhance the communication process. These management principles align with the Task-Oriented Communications (TOC) philosophy. The aim is to allocate the minimum necessary communication resource according to the receiver's objective and continuously improve the communication process. However, it is currently unclear how to build knowledge of the receiver's goal and operate accordingly for efficient-resource utilization. Our management approach combines viability theory and transfer entropy to ensure that the actor remains within a viable space as per their goal and to gradually reduce the information exchange through knowledge accumulation. We discuss these theories in the context of TOC and examine their application in the plant process control case. Finally, we provide insights into future research directions from computational, performance, and protocol perspectives.
- E. C. Strinati and S. Barbarossa, “6G networks: Beyond Shannon towards semantic and goal-oriented communications,” Computer Networks, vol. 190, p. 107930, 2021.
- P. A. Stavrou and M. Kountouris, “The role of fidelity in goal-oriented semantic communication: A rate distortion approach,” IEEE Transactions on Communications, pp. 1–1, 2023.
- S. Ma, W. Qiao, Y. Wu, H. Li, G. Shi, D. Gao, Y. Shi, S. Li, and N. Al-Dhahir, “Task-oriented explainable semantic communications,” IEEE Transactions on Wireless Communications, pp. 1–1, 2023.
- M. K. Farshbafan, W. Saad, and M. Debbah, “Curriculum learning for goal-oriented semantic communications with a common language,” 2022. [Online]. Available: https://arxiv.org/abs/2204.10429
- Y. Gao, X. Wei, J. Chen, and L. Zhou, “Toward immersive experience: Evaluation for interactive network services,” IEEE Network, vol. 36, no. 1, pp. 144–150, 2022.
- T. Kim and S. Park, “Equivalent data information of sensory and motor signals in the human body,” IEEE Access, vol. 8, pp. 69 661–69 670, 2020.
- A. Deshpande and P. Varaiya, “Viable control of hybrid systems,” in Hybrid Systems II, P. Antsaklis, W. Kohn, A. Nerode, and S. Sastry, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995, pp. 128–147.
- C. V. Murudkar, K.-C. Chen, and R. D. Gitlin, “Network architecture for machine learning: A network operator’s perspective,” IEEE Comm. Magazine, vol. 60, no. 7, pp. 68–74, 2022.
- Z. Qin, X. Tao, J. Lu, W. Tong, and G. Y. Li, “Semantic communications: Principles and challenges,” 2022. [Online]. Available: https://arxiv.org/abs/2201.01389
- Q. Hu, G. Zhang, Z. Qin, Y. Cai, G. Yu, and G. Y. Li, “Robust semantic communications against semantic noise,” 2022. [Online]. Available: https://arxiv.org/abs/2202.03338
- T.-Y. Tung, S. Kobus, J. P. Roig, and D. Gündüz, “Effective communications: A joint learning and communication framework for multi-agent reinforcement learning over noisy channels,” IEEE Journal on Sel. Areas in Communications 39(8), 2021.
- D. Kim, S. Moon, D. Hostallero, W. J. Kang, T. Lee, K. Son, and Y. Yi, “Learning to schedule communication in multi-agent reinforcement learning,” 2019. [Online]. Available: https://arxiv.org/abs/1902.01554
- S. Karten, S. Agrawal, M. Tucker, D. Hughes, M. Lewis, J. Shah, and K. Sycara, “The enforcers: Consistent sparse-discrete methods for constraining informative emergent communication,” 2022. [Online]. Available: https://arxiv.org/abs/2201.07452
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