Swinburne College, Shandong University Of Science and Technology, Jinan, 250031, China
Email: 1824860015@qq.com
Manuscript received July 4, 2026; accepted August 1, 2026; published August 26, 2026
Abstract—The evolution of sixth-generation (6G) mobile communication systems introduces new challenges for signal processing technologies. The incorporation of emerging techniques such as ultra-massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RIS), and integrated sensing and communications (ISAC) necessitates a paradigm shift in signal processing from conventional optimization methods toward intelligent approaches. This paper presents a comparative analysis of key technologies in intelligent signal processing for 6G based on a literature survey. Focusing on three core enabling technologies, the study compares traditional optimization-based methods with artificial intelligence (AI)-driven approaches in terms of their performance characteristics and application boundaries in tasks including channel estimation, beamforming, and signal detection. The findings indicate that AI-based methods offer superior performance and lower computational latency in complex scenarios, yet they continue to face challenges related to interpretability, generalization, and standardization. Model-driven deep unfolding, graph neural networks, and generative AI represent important directions for the future development of intelligent signal processing. This paper aims to provide a reference framework for theoretical research and system design in intelligent signal processing for 6G.
Keywords—6G, intelligent signal processing, artificial intelligence, ultra-massive MIMO, reconfigurable intelligent surfaces, integrated sensing and communications
Cite: Chensong Zhang, "A Comparative Analysis of Key Technologies in Intelligent Signal Processing for 6G," International Journal of Engineering and Technology, vol. 18, no. 3, pp. 133-137, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (
CC BY 4.0).
