A comprehensive review of adaptive modulation and coding techniques for spectrum efficiency and interference mitigation in satellite communication

This paper provides a detailed analysis of Advanced Adaptive Modulation and Coding (AMC) techniques in satellite communication and compare it with the conventional frequency reuse system such as Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA), which rely on fixed resource allocation, AMC dynamically modifies modulation and coding rates, optimizing throughput during favorable conditions and ensuring reliability in adverse scenarios. In optimal conditions, spectral efficiency improves by 30-50% due to AMC's adaptability, as governed by Signal to Noise Ratio (SNR) and Bit Error Rate (BER) thresholds. This paper also demonstrates that its integration with machine learning (ML), specifically, deep learning (DL) and deep reinforcement learning (DRL) enables prediction and adaption, which can address challenges such as channel fading and dynamic signal variations in Low Earth Orbit (LEO) satellite network. Traditional methods are outperformed by these data driven approaches for further improving throughput and reliability. While AMC requires sophisticated hardware, complex algorithms, and efficient feedback systems,