A Low-Complexity Range Estimation with Adjusted Affine Frequency Division Multiplexing Waveform

Affine frequency division multiplexing (AFDM) is a recently proposed communication waveform for time-varying channel scenarios. As a chirp-based multicarrier modulation technique it can not only satisfy the needs of multiple scenarios in future mobile communication networks but also achieve good performance in radar sensing by adjusting the built-in parameters, making it a promising air interface waveform in integrated sensing and communication (ISAC) applications. In this paper, we investigate an AFDM-based radar system and analyze the radar ambiguity function of AFDM with different built-in parameters, based on which we find an AFDM waveform with the specific parameter c2 owns the near-optimal time-domain ambiguity function. Then a low-complexity algorithm based on matched filtering for high-resolution target range estimation is proposed for this specific AFDM waveform. Through simulation and analysis, the specific AFDM waveform has near-optimal range estimation performance with the proposed low-complexity algorithm while having the same bit error rate (BER) performance as orthogonal time frequency space (OTFS) using simple linear minimum mean square error (LMMSE) equalizer.

Digital Self-Interference Cancellation With Robust Multi-layered Total Least Mean Squares Adaptive Filters

In simultaneous transmit and receive (STAR) wireless communications, digital self-interference (SI) cancellation is required before estimating the remote transmission (RT) channel. Considering the inherent connection between SI channel reconstruction and RT channel estimation, we propose a multi-layered M-estimate total least mean squares (m-MTLS) joint estimator to estimate both channels. In each layer, our proposed m-MTLS estimator first employs an M-estimate total least mean squares (MTLS) algorithm to eliminate residual SI from the received signal and give a new estimation of the RT channel. Then, it gives the final RT channel estimation based on the weighted sum of the estimation values obtained from each layer. Compared to traditional minimum mean square error (MMSE) estimator and single-layered MTLS estimator, it demonstrates that the m-MTLS estimator has better performance of normalized mean squared difference (NMSD). Besides, the simulation results also show the robustness of m-MTLS estimator even in scenarios where the local reference signal is contaminated with noise, and the received signal is impacted by strong impulse noise.