Submitted Papers

Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels

Uploaded: arXiv , July, 2024

This paper delineates the distinctions between near-field and far-field propagation, highlighting the unique EM characteristics introduced by having large antenna arrays. It thoroughly examines the challenges these new near-field characteristics pose for user localization and channel estimation and provides a comprehensive review of new algorithms developed to address them.

Citation: Zhe Wang, Jiayi Zhang, Emil Björnson, Dusit Niyato, Bo Ai, "Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels," arxiv: 2407.18531, 2024.
Paper Link: https://arxiv.org/pdf/2407.18531

Near-Field User Localization and Channel Estimation for XL-MIMO Systems: Fundamentals, Recent Advances, and Outlooks

Uploaded: arXiv , July, 2024

This paper delineates the distinctions between near-field and far-field propagation, highlighting the unique EM characteristics introduced by having large antenna arrays. It thoroughly examines the challenges these new near-field characteristics pose for user localization and channel estimation and provides a comprehensive review of new algorithms developed to address them.

Citation: Hao Lei, Jiayi Zhang, Zhe Wang, Huahua Xiao, Bo Ai, and Emil Björnson, "Near-Field User Localization and Channel Estimation for XL-MIMO Systems: Fundamentals, Recent Advances, and Outlooks," arxiv: 2407.10147, 2024.
Paper Link: https://arxiv.org/pdf/2407.10147

Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision

Uploaded: arXiv , April, 2024

We study generative artificial intelligence (AI) agent-enabled next-generation MIMO design. Firstly, we provide an overview of the development, fundamentals, and challenges of the next-generation MIMO. Then, we propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents with the aid of large language model (LLM) and retrieval augmented generation (RAG). Next, we comprehensively discuss the features and advantages of the generative AI agent framework. More importantly, to tackle existing challenges of next-generation MIMO, we discuss generative AI agent-enabled next-generation MIMO design, from the perspective of performance analysis, signal processing, and resource allocation.

Citation: Zhe Wang, Jiayi Zhang, Hongyang Du, Ruichen Zhang, Dusit Niyato, Bo Ai, and Khaled B. Letaief, "Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision," 2404.08878, 2024.
Paper Link: https://arxiv.org/pdf/2404.08878.pdf

Analytical Framework for Effective Degrees of Freedom in Near-Field XL-MIMO

Uploaded: arXiv , January, 2024

We develop an effective degrees of freedom (EDoF) performance analysis framework specifically tailored for near-field XL-MIMO systems. We explore five representative distinct XL-MIMO hardware designs, including uniform planar array (UPA)-based with point antennas, two-dimensional (2D) continuous aperture (CAP) plane-based, UPA-based with patch antennas, uniform linear array (ULA)-based, and one-dimensional (1D) CAP line segment-based XL-MIMO systems. Our analysis encompasses two near field channel models: the scalar and dyadic Green’s function-based channel models.

Citation: Zhe Wang, Jiayi Zhang, Wenhui Yi, Hongyang Du, Dusit Niyato, Bo Ai, and Derrick Wing Kwan Ng, "Analytical Framework for Effective Degrees of Freedom in Near-Field XL-MIMO," arXiv:2401.15280, 2024.
Paper Link: https://arxiv.org/pdf/2401.15280.pdf