Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions
Abstract: Recently, big artificial intelligence models (BAIMs) represented by chatGPT have brought an incredible revolution. With the pre-trained BAIMs in certain fields, numerous downstream tasks can be accomplished with only few-shot or even zero-shot learning and exhibit state-of-the-art performances. As widely envisioned, the big AI models are to rapidly penetrate into major intelligent services and applications, and are able to run at low unit cost and high flexibility. In 6G wireless networks, to fully enable intelligent communication, sensing and computing, apart from providing other intelligent wireless services and applications, it is of vital importance to design and deploy certain wireless BAIMs (wBAIMs). However, there still lacks investigation on architecture design and system evaluation for wBAIM. In this paper, we provide a comprehensive discussion as well as some in-depth prospects on the demand, design and deployment aspects of the wBAIM. We opine that wBAIM will be a recipe of the 6G wireless networks to build high-efficient, sustainable, versatile, and extensible wireless intelligence for numerous promising visions. Then, we provide the core characteristics, principles, and pilot studies to guide the design of wBAIMs, and discuss the key aspects of developing wBAIMs through identifying the differences between the existing BAIMs and the emerging wBAIMs. Finally, related research directions and potential solutions are outlined.
- OpenAI, “ChatGPT,” https://openai.com/blog/chatgpt.
- “On the opportunities and risks of foundation models,” arXiv preprint arXiv:2108.07258, 2021.
- ITU Radiocommunication Study Groups, “Framework and overall objectives of the future development of IMT for 2030 and beyond,” Switzerland, 2023.
- “Fingerprint-based localization for massive MIMO-OFDM system with deep convolutional neural networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 10846–10857, 2019.
- “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 748–751, Oct. 2018.
- Y. S. Nasir and D. Guo, “Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2239–2250, 2019.
- “Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services,” arXiv preprint arXiv:2303.16129, 2023.
- “Language models are few-shot learners,” Advances in neural information processing systems, vol. 33, pp. 1877–1901, 2020.
- “MIMO-GAN: Generative MIMO channel modeling,” in ICC 2022-IEEE International Conference on Communications. IEEE, 2022, pp. 5322–5328.
- “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
- “Viewing channel as sequence rather than image: A 2-D Seq2Seq approach for efficient MIMO-OFDM CSI feedback,” IEEE Transactions on Wireless Communications, pp. 1–1, 2023.
- “C-GRBFnet: A physics-inspired generative deep neural network for channel representation and prediction,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 8, pp. 2282–2299, 2022.
- “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
- “Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,” IEEE Journal of Selected Topics in Signal Processing, vol. 17, no. 1, pp. 9–39, January 2023.
- “A bargaining game for personalized, energy efficient split learning over wireless networks,” in Proceeding IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023, pp. 1–6.
Sponsor
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.