AI and Deep Learning for THz Ultra-Massive MIMO: From Model-Driven Approaches to Foundation Models

2024-12-13

arXiv.org

AI Summary

Addressing the “computationally intractable, analytically unmodelable, and experimentally unmeasurable” challenges in terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems, this paper proposes an AI-driven three-tier collaborative framework. First, it introduces a novel model-driven deep learning paradigm comprising four sequential steps to enhance interpretability and generalizability. Second, it establishes the world’s first generative foundation model for THz UM-MIMO channel state information (CSI), built upon score-based generative modeling, enabling unified joint transmitter–receiver design. Third, it pioneers the integration of large language models (LLMs) into wireless communications—establishing a cross-domain transfer paradigm for CSI estimation, resource optimization, and protocol semantic understanding. Complementing these advances, the work proposes a scalable foundation model architecture and a joint training framework. Collectively, this research provides theoretical foundations and methodological guidance for 6G intelligent air interfaces.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In this paper, we explore the potential of artificial intelligence (AI) to address challenges in terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) systems. We identify three key challenges for transceiver design:"hard to compute,""hard to model,"and"hard to measure,"and argue that AI can provide promising solutions. We propose three research roadmaps for AI algorithms tailored to THz UM-MIMO systems. The first, model-driven deep learning (DL), emphasizes leveraging domain knowledge and using AI to enhance bottleneck modules in established signal processing or optimization frameworks. We discuss four steps: algorithmic frameworks, basis algorithms, loss function design, and neural architecture design. The second roadmap presents channel station information (CSI) foundation models to unify transceiver module design by focusing on the wireless channel. We propose a compact foundation model to estimate wireless channel score functions, serving as a prior for designing transceiver modules. We outline four steps: general frameworks, conditioning, site-specific adaptation, and joint design of CSI models and model-driven DL. The third roadmap explores applying pre-trained large language models (LLMs) to THz UM-MIMO systems, with applications in estimation, optimization, searching, network management, and protocol understanding. Finally, we discuss open problems and future research directions.

Problem

• AI solutions for THz UM-MIMO transceiver design challenges
• Model-driven DL for enhancing signal processing frameworks
• CSI foundation models for unified transceiver module design

Innovation

• Model-driven deep learning enhances signal processing
• CSI foundation models unify transceiver design
• Pre-trained LLMs optimize THz UM-MIMO systems