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This research proposes an AI-driven receiver architecture using Transformer encoders to optimize power allocation in superimposed DMRS and data transmission systems. By integrating iterative channel estimation with detection, the approach demonstrates improved spectral efficiency over conventional non-overlapped methods within MIMO-OFDM frameworks. The work represents a significant advancement in applying deep learning models directly to physical layer signal processing challenges.
Optimal Power Allocation and AI Receiver Design for Superimposed DMRS and Data Transmission
arXiv:2608.13809v1 Announce Type: cross Abstract: In this paper, we consider transmissions with superimposed (SI) demodulation-reference-symbol (DMRS) and data in orthogonal frequency-division multiplexing (OFDM) based multiple-input multiple-output (MIMO) systems. First, we derive an analytical framework to characterize the iterative behavior between the mean-square errors (MSEs) of channel estimation (CE) and MIMO detection (MD) within an iterative CE and detection (ICED) process. This framework is subsequently utilized to optimize power allocation and pilot patterns between the DMRS and data symbols for SI-DMRS transmission. Second, we design an artificial intelligence (AI) based receiver built upon Transformer encoders for SI-DMRS transmissions, which incorporates an iterative CE and detection (ICED) structure. Simulation results demonstrate that the proposed AI-ICED receiver, combined with SI-DMRS, effectively increases spectral efficiency (SE) compared to conventional systems using non-overlapped DMRS and data symbols.
Abstract:In this paper, we consider transmissions with superimposed (SI) demodulation-reference-symbol (DMRS) and data in orthogonal frequency-division multiplexing (OFDM) based multiple-input multiple-output (MIMO) systems. First, we derive an analytical framework to characterize the iterative behavior between the mean-square errors (MSEs) of channel estimation (CE) and MIMO detection (MD) within an iterative CE and detection (ICED) process. This framework is subsequently utilized to optimize power allocation and pilot patterns between the DMRS and data symbols for SI-DMRS transmission. Second, we design an artificial intelligence (AI) based receiver built upon Transformer encoders for SI-DMRS transmissions, which incorporates an iterative CE and detection (ICED) structure. Simulation results demonstrate that the proposed AI-ICED receiver, combined with SI-DMRS, effectively increases spectral efficiency (SE) compared to conventional systems using non-overlapped DMRS and data symbols.
Information Theory (cs.IT); Artificial Intelligence (cs.AI)
arXiv:2608.13809 [cs.IT]
(or arXiv:2608.13809v1 [cs.IT] for this version)
https://doi.org/10.48550/arXiv.2608.13809 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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