Abstract:

The 6th Generation Mobile Communication Technology (6G) imposes high requirements on spectral efficiency and connection density, making Demodulation Reference Signal (DMRS) pilot overhead a critical factor limiting system performance. Reducing DMRS density releases resources for data transmission and provides great room for port multiplexing, but it also exposes the receiver to challenges such as degraded channel estimation accuracy and colored noise. Toward 6G, 3GPP RAN1 has initiated the “low-density DMRS with AI receiver” study, which leverages Artificial Intelligence (AI) to enhance the receiver’s signal processing capability under low-density DMRS and proposes seven AI receiver architectures in total. From the perspective of AI receiver taxonomy, this paper first reviews recent advances in four directions: AI-based channel estimation, MIMO detection, constellation demapping, and multi-module joint optimization. It then systematically summarizes the definitions, taxonomy, advantages, and limitations of the seven AI receiver architectures, and on this basis conducts link-level simulation verification for representative architectures. The simulation results show that AI receivers still maintain superior decoding performance under low-density DMRS.

Key words: 6G, pilot overhead, AI receiver, low-density DMRS