Dynamic SINR-Guided Iterative Interference Cancellation for ODDM Systems in Doubly Dispersive Channels

TL;DR

This paper proposes a dynamic SINR-guided iterative interference cancellation method for ODDM systems in doubly dispersive channels, improving low-complexity detection and performance in high-mobility environments.

Contribution

It introduces a novel SINR-guided iterative detection algorithm that accounts for off-grid delays and Doppler shifts, enhancing ODDM signal detection efficiency.

Findings

The proposed method achieves better convergence than traditional approaches.

It maintains low complexity while improving error performance.

Simulation results validate the effectiveness of the algorithm.

Abstract

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently gained significant attention as a promising candidate to promote the communication reliability in high-mobility environments. Low complexity signal detection is one of the most significant challenges for ODDM over general physical channels, due to the large channel spreading caused by the fractional delay and Doppler shifts. In this paper, we investigate the low-complexity data detection for ODDM system by utilizing iterative interference cancellation. Based on the theoretical analysis of signal to interference plus noise ratio (SINR) during the iteration, a dynamic SINR-guided approach is proposed to provide a better initialization result. Specifically, we analyze the SINR of each time domain sample before initial estimate with consideration of off-grid delay and Doppler shifts. The iteration is then started from the multi-carrier symbol index which has the best SINR. The corresponding interference is then eliminated for other time domain samples while the SINR for symbol awaiting detection is also updated. Based on the updated SINR, the next multi-carrier symbol index is selected for the same processing until all data symbols have been initialized. Finally, we update the SINR synchronously until the end of the initialization. Simulation experiments indicate that our proposed algorithms demonstrate satisfying convergence and error performance while avoiding the huge complexity introduced by full linear minimum mean squared error (LMMSE) initialization.