A survey of intelligent OFDM receivers for channel estimation in model driven 5G and AI enabled 6G systems
TL;DR
A comparative analysis between classical 5G and AI/ML-based 6G channel estimation methods is presented in terms of bit error rate performance, adaptability, and complexity, followed by a discussion on the advantages and limitations of intelligent channel estimation for future wireless networks.
Abstract
Orthogonal Frequency Division Multiplexing (OFDM) is the core physical-layer modulation technique used in modern wireless communication systems due to its high spectral efficiency and robustness against frequency-selective fading. In fifth-generation (5G) systems, channel estimation for OFDM is mainly performed using classical pilot-based methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) estimation. Although these techniques are computationally efficient and well standardized, their performance degrades in practical fading environments, particularly under high mobility, sparse pilot configurations, and higher-order modulation schemes. This survey reviews the OFDM physical layer and traditional channel estimation techniques in 5G systems and analyzes their limitations using performance results over AWGN and Rayleigh fading channels. The paper further examines recent artificial intelligence and machine learning (AI/ML)-based channel estimation approaches proposed for sixth-generation (6G) systems, highlighting their improved robustness and adaptability in dynamic channel conditions. A comparative analysis between classical 5G and AI/ML-based 6G channel estimation methods is presented in terms of bit error rate performance, adaptability, and complexity, followed by a discussion on the advantages and limitations of intelligent channel estimation for future wireless networks.