Sensing-Enhanced Channel Estimation for Near-Field XL-MIMO Systems
Published in IEEE J. Sel. Areas Commun., 2024
Publications
You can also find my articles on my Google Scholar profile.
Journal Papers
Hybrid Knowledge-Data Driven Channel Semantic Acquisition and Beamforming for Cell-Free Massive MIMO
Published in IEEE J. Sel. Topics Signal Process., 2023
This article focuses on advancing outdoor wireless systems to better support ubiquitous extended reality (XR) applications, and close the gap with current indoor wireless transmission capabilities. We propose a hybrid knowledge-data driven method for channel semantic acquisition and multi-user beamforming in cell-free massive multiple-input multiple-output (MIMO) systems. Specifically, we firstly propose a data-driven multiple layer perceptron (MLP)-Mixer-based auto-encoder for channel semantic acquisition, where the pilot signals, CSI quantizer for channel semantic embedding, and CSI reconstruction for channel semantic extraction are jointly optimized in an end-to-end manner. Moreover, based on the acquired channel semantic, we further propose a knowledge-driven deep-unfolding multi-user beamformer, which is capable of achieving good spectral efficiency with robustness to imperfect CSI in outdoor XR scenarios. By unfolding conventional successive over-relaxation (SOR)-based linear beamforming scheme with deep learning, the proposed beamforming scheme is capable of adaptively learning the optimal parameters to accelerate convergence and improve the robustness to imperfect CSI. The proposed deep unfolding beamforming scheme can be used for access points (APs) with fully-digital array and APs with hybrid analog-digital array. Simulation results demonstrate the effectiveness of our proposed scheme in improving the accuracy of channel acquisition, as well as reducing complexity in both CSI acquisition and beamformer design. The proposed beamforming method achieves approximately 96% of the converged spectrum efficiency performance after only three iterations in downlink transmission, demonstrating its efficacy and potential to improve outdoor XR applications.
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces
Published in IEEE Trans. Veh. Technol., 2020
Channel estimation with sparse signal reconstruction and neural network refinement.
Conference Papers
Low-Complexity Near-Field Localization with XL-MIMO Sectored Uniform Circular Arrays
Published in Proc. IEEE Globecom, 2024
DPSS-based dictionary design for near-field XL-MIMO channel estimation
Published in Proc. IEEE Int. Conf. Commun. (ICC), 2024
Transformer-based Joint Source Channel Coding for Textual Semantic Communication
Published in Proc. IEEE Int. Conf. Commun. China (ICCC), 2023
TODO: Add the abstract here.
Model-Driven Deep Learning Based Precoding for FDD Cell-Free Massive MIMO with Imperfect CSI
Published in Proc. IEEE International Wireless Communications and Mobile Computing (IWCMC), 2022
TODO: Add the abstract here.
Articles
Large-scale scattering-augmented optical encryption
Published in Nat. Commun., 2024
Data proliferation in the digital age necessitates robust encryption techniques to protect information privacy. Optical encryption leverages the multiple degrees of freedom inherent in light waves to encode information with parallel processing and enhanced security features. However, implementations of large-scale, high-security optical encryption have largely remained theoretical or limited to digital simulations due to hardware constraints, signal-to-noise ratio challenges, and precision fabrication of encoding elements. Here, we present an optical encryption platform utilizing scattering multiplexing ptychography, simultaneously enhancing security and throughput. Unlike optical encoders which rely on computer-generated randomness, our approach leverages the inherent complexity of light scattering as a natural unclonable function. This enables multi-dimensional encoding with superior randomness. Furthermore, the ptychographic configuration expands encryption throughput beyond hardware limitations through spatial multiplexing of different scatterer regions. We propose a hybrid decryption algorithm integrating model- and data-driven strategies, ensuring robust decryption against various sources of measurement noise and communication interference. We achieved optical encryption at a scale of ten-megapixel pixels with 1.23 µm resolution. Communication experiments validate the resilience of our decryption algorithm, yielding high-fidelity results even under extreme transmission conditions characterized by a 20% bit error rate. Our encryption platform offers a holistic solution for large-scale, high-security, and cost-effective cryptography.
LEO Satellite Constellations for 5G and Beyond: How Will They Reshape Vertical Domains?
Published in IEEE Commun. Mag., 2021
The rapid development of communication technologies in the past decades has provided immense vertical opportunities for individuals and enterprises. However, conventional terrestrial cellular networks have unfortunately neglected the huge geographical digital divide, since high-bandwidth wireless coverage is concentrated in urban areas. To meet the goal of “connecting the unconnected”, integrating low Earth orbit (LEO) satellites with the terrestrial cellular networks has been widely considered as a promising solution. In this article, we first introduce the development roadmap of LEO sa tellite constellations (SatCons), including early attempts in LEO satellites with the emerging LEO constellations. Further, we discuss the unique opportunities of employing LEO SatCons for the delivery of integrating 5G networks. Specifically, we present their key performance indicators, which offer important guidelines for the design of associated enabling techniques, and then discuss the potential impact of integrating LEO SatCons with typical 5G use cases, where we engrave our vision of various vertical domains reshaped by LEO SatCons. Technical challenges are finally provided to specify future research directions.