Adaptive Modulation and Coding in High-Mobility Environments: Challenges, Algorithms, and Emerging Trends for 5G/6G Systems

Adaptive Modulation and Coding (AMC) is a cornerstone technique in modern wireless communications, enabling dynamic optimization of spectral efficiency while maintaining reliable link quality. However, the high-mobility environment—characterized by rapid Doppler spreading, inter-carrier interference (ICI), and volatile channel state information (CSI)—presents severe challenges that fundamentally limit the performance of conventional AMC frameworks. This paper presents a comprehensive survey and performance analysis of AMC strategies tailored for high-mobility scenarios including high-speed railways (HSR), vehicular-to-everything (V2X) communications, unmanned aerial vehicles (UAVs), and fifth/sixth generation (5G/6G) networks. We review the theoretical foundations of AMC, examine state-of-the-art algorithmic innovations spanning rule-based systems, machine learning (ML)-assisted frameworks, deep reinforcement learning (DRL), and transformer-based architectures. Extensive simulation results using Extended Vehicular A (EVA) and CDL-C channel models demonstrate that a reinforcement learning-based AMC (RL-AMC) scheme achieves a spectral efficiency gain of up to 33% over conventional threshold-based AMC under Doppler spreads exceeding 1,500 Hz. We further identify open research directions including reconfigurable intelligent surface (RIS)-assisted AMC, joint Doppler-AMC co-design, and semantic communication paradigms for 6G systems.