Cutting-edge large artificial intelligence (AI) models like ChatGPT drive a transformative revolution. These models, particularly in 6G networks, are poised to revolutionize intelligent services with cost-effective and flexible deployment. In a recent paper submitted to the arXiv* server, researchers explored wireless big AI models (wBAIMs) demand, design, and deployment, outlining its significance in building efficient, sustainable, and versatile wireless intelligence for 6G networks.
Study: Empowering 6G Networks: The Role of Wireless Big AI Models. Image credit: Who is Danny/Shutterstock
*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.
Background
The emerging paradigm of big AI models (BAIMs) is reshaping diverse domains, including natural language processing (NLP), owing to their vast parameters, extensive data, and powerful computational resources. BAIMs exhibit exceptional generalization and functional intelligence through fine-tuning, zero-shot, or few-shot learning, transcending task boundaries, and fostering innovative low-cost applications. This paradigm shift overcomes the limitations of previous deep neural networks, ushering in universal information processing capabilities.
As machine learning evolves, wireless communication and information processing technologies advance in tandem. There are three wireless communication technology levels: fundamental transmission, deep learning-aided communication, and advanced information processing with BAIMs. Integrating BAIMs in wireless networks holds immense promise for 6G, enabling multi-task intelligence, ubiquitous sensing and communication, and immersive scenarios.
Key wireless intelligence demands in 6G
The current wireless AI architectures fall short in meeting the diverse demands of 6G's visionary applications, highlighting the urgent need for a universal wBAIM. Wireless intelligence's indispensability in 6G is exemplified through pivotal intelligent use cases:
AI-Enhanced Wireless Transmission: Successful transmission necessitates analyzing user wireless status and adapting transceiver setups. Wireless AI must predict user movement and forecast accurate channels for high mobility transmission. In intricate environments, precise channel acquisition, path separation, and beamforming enable efficient, broad-scale transmission.
Intelligent Massive Access: Next-gen massive access relies on adaptive decisions, scenario synchronization, and intelligent signal-space mapping. Wireless intelligence captures user position, expediting beam establishment. For cell-free access, inter-cell collaboration optimizes user service selection.
Smart Scheduling and Management: 6G scheduling requires cooperative processing, cloud-edge fusion, and adaptive decision-making. Striking a balance between efficiency and fairness via resource allocation entails spatial and channel learning. Intelligent scheduling extends to urban management and emergency response.
Collaborative Intelligent Sensing: AI empowers accurate sensing in intricate settings. Collaborative processing of multi-modal, multi-user, and multi-scenario signals ensures high-quality sensing. Blending user observations with base station data enhances target reconstruction and large-scale environment sensing.
Interactive Metaverse Assistance: 6G empowers Metaverse with simulated wireless states, independent decision-making, and efficient virtual-real interactions. A comprehensive model simulates wireless data, guiding reactions and adjustments. High-efficiency transmission enhances virtual-real interaction.
Semantic Communication Goals: Semantic communication integrates user needs into transmission methods. Achieving this across users and scenarios requires substantial information capacity and wireless model intelligence, supporting diverse knowledge bases.
wBAIM in 6G Networks
Certain wireless AI technologies excel in specific cases, but their task-specific architecture limits efficiency and autonomous integration across diverse scenarios, incurring unsustainable data and training costs. Current wireless AI models focused on specific tasks create conflicts and barriers, falling short of 6G's intelligence needs. BAIMs, distinguished by their efficiency, versatility, sustainability, and extensibility, emerge as crucial for next-gen wireless networks, capable of capturing intricate wireless characteristics and seamlessly integrating them.
The wBAIM-based architecture aims for greater versatility and extensibility compared to current wireless frameworks. It centers on a unified deployment paradigm supported by pre-training. Pre-training, akin to BAIMs, characterizes wBAIMs, collaboratively trained on cloud and edge platforms. This functional intelligence leverages diverse wireless data, tailored training methods, and native wireless objectives, offering enhanced model intelligence and reduced overhead.
The focus extends beyond performance amplification, breaking barriers in tasks, scenarios, and scheduling for a comprehensive wireless system. wBAIM excels in integrated multitasking, unifying communication scenarios, and achieving network-wide scheduling through autonomous actions, synchronization, and cloud-guided instructions.
Despite successful BAIM implementations, wBAIMs face novel challenges in universal wireless intelligence, modality handling, abundant resource utilization, low-latency inference, interference management, and multi-access strategies. Exploring wBAIM-driven wireless architecture encompasses dataset creation, model design, training, and deployment steps that address challenges. It involves constructing informative wireless datasets, modeling wireless characteristics, employing centralized and distributed training, and ensuring low-cost, low-latency deployment through parameter quantization, adaptive inference, and radio-computation integration.
Exploring wBAIM-driven wireless architecture
Deep learning encompasses dataset creation, model design, training, and deployment. Realizing the envisioned wireless architecture involves steps addressing Section IV's challenges. Promising research directions within these processes are summarized. Constructing informative wireless datasets involves real data collection and augmentation techniques. Modeling wireless characteristics includes channel structure and context objective functions. Centralized and distributed training employ federated and split learning. Parameter quantization, adaptive inference, and radio-computation integration are vital for deployment.
Conclusion
In summary, researchers presented a perspective on 6G wireless networks' BAIMs, highlighting opportunities, challenges, and research directions. The convergence of AI technology and wireless evolution is anticipated. Development recommendations include establishing a unified multi-task intelligence model, addressing wireless-specific constraints, and synergizing software and hardware for seamless computing-communication support in network systems.
*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as definitive, used to guide development decisions, or treated as established information in the field of artificial intelligence research.
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