Gongpu Wang

Xinyi Wang, Yuxin Li, Yinghui Ye et al.

In ambient backscatter communication (AmBC), strong direct interference from the ambient source poses a major challenge to reliable symbol detection. Although previous studies have shown that employing a low-noise amplifier (LNA) in conventional point-to-point communication improves symbol detection performance at low-to-moderate transmission power, it remains unclear whether this improvement also holds for AmBC. To respond it, in this work, we investigate the symbol detection performance of an AmBC receiver that is equipped with an LNA and adopts the energy detection (ED) to recover tag's information. Particularly, we first propose a new AmBC symbol detection framework that incorporates LNA parameters. On this basis, we derive the bit error rate (BER) of the ED and employ the deflection coefficient (DC) to demonstrate that the detection performance can be enhanced by the LNA at low-to-moderate ambient source transmission power. Then, we derive the near-optimal detection threshold to minimize the BER and propose a method to estimate the required parameters for this threshold by leveraging the tag's pilot symbols.

Zhongnian Li, Chao Zheng, Jian Xiao et al.

This paper investigates joint channel estimation and positioning in near-field sparse extra-large multiple-input multiple-output (XL-MIMO) orthogonal frequency division multiplexing (OFDM) systems. To achieve cooperative gains between channel estimation and positioning, we propose a deep learning-based two-stage framework comprising positioning and channel estimation. In the positioning stage, the user's coordinates are predicted and utilized in the channel estimation stage, thereby enhancing the accuracy of channel estimation. Within this framework, we propose a U-shaped Mamba architecture for channel estimation and positioning, termed as CP-Mamba. This network integrates the strengths of the Mamba model with the structural advantages of U-shaped convolutional networks, enabling effective capture of local spatial features and long-range temporal dependencies of the channel. Numerical simulation results demonstrate that the proposed two-stage approach with CP-Mamba architecture outperforms existing baseline methods. Moreover, sparse arrays (SA) exhibit significantly superior performance in both channel estimation and positioning accuracy compared to conventional compact arrays.