Semantic communication and large language models for AGI based resource allocation in future wireless networks

Future wireless networks (WNs) must address unprecedented challenges in resource allocation (RA) driven by dynamic environments, diverse user demands, and heterogeneous service requirements. Emerging services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC) demand intelligent, context-aware resource management strategies beyond traditional methods. Semantic communication (SemCom), which prioritizes conveying intended meaning rather than raw data, offers a promising paradigm to enhance spectral efficiency, reduce communication overhead, and improve user satisfaction. In parallel, advancements in artificial general intelligence (AGI) and large language models (LLMs) introduce new capabilities in reasoning, semantic inference, and adaptive decision-making. This paper presents a unified conceptual and architectural framework that integrates SemCom, LLMs, and AGI for intelligent RA in future WNs. We first examine foundational concepts, then classify and compare methodologies across key performance metrics, and finally explore synergistic architectures that combine these technologies. We highlight open challenges, including semantic metric design, real-time AGI adaptation, scalable LLM deployment, and privacy-preserving semantic reasoning. Unlike prior works, this survey uniquely bridges the semantic and cognitive dimensions of RA, providing a comprehensive roadmap for building fully autonomous, semantic-aware, and resource-efficient wireless communication systems.