A Survey of Radio Resource Scheduling for 6G and Future Wireless Networks

The evolution of wireless communication is rapidly advancing beyond Fifth Generation (5G) systems toward Sixth Generation (6G) and future network paradigms. These networks aim to deliver unprecedented data rates, ultra-reliable low-latency communication (URLLC), massive connectivity, and seamless integration of terrestrial and non-terrestrial infrastructures. Efficient radio resource scheduling (RRS) is essential to meeting these demands while ensuring optimal performance in increasingly complex and heterogeneous environments. This survey presents a comprehensive and structured overview of RRS strategies for 5G, 6G, and beyond. Anchored in a unified taxonomy framework, it systematically classifies scheduling approaches across key dimensions, including scheduling methodology, network architecture, and service types. The paper explores a wide spectrum of techniques-from traditional heuristic algorithms to advanced solutions based on multiple-input multiple-output (MIMO), millimeter-wave (mmWave), network slicing, and cross-layer optimization. Special emphasis is placed on the transformative role of machine learning (ML) and artificial intelligence (AI), including supervised learning (SL), reinforcement learning (RL), and deep learning (DL)-based models for intelligent, adaptive scheduling. The survey also discusses emerging challenges such as joint sensing and communication scheduling, edge computing and localized resource allocation (RA), digital twin-assisted scheduling, multi-carrier scheduling, and quantum-assisted scheduling. By highlighting state-of-the-art techniques, open research gaps, and future directions, this survey serves as a valuable reference for researchers and practitioners aiming to develop scalable, secure, and intelligent RRS solutions for next-generation wireless systems.