Published Papers

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

Published: IEEE Transactions on Signal Processing , February, 2025

We explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading channels. We propose one centralized and two distributed OBE schemes: Centralized OBE (C-OBE), Distributed OBE based on Global channel statistics (DG-OBE), and Distributed OBE based on Local channel statistics (DL-OBE), which maximize their respective SE expressions. OBE matrices in these schemes are tailored based on varying levels of channel statistics.

Citation: Zhe Wang, Jiayi Zhang, Emil Björnson, Dusit Niyato, Bo Ai, "Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels," IEEE Transactions on Signal Processing, accepted, 2025.
Paper Link: https://arxiv.org/pdf/2407.18531

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

Published: IEEE Transactions on Wireless Communications , January, 2025

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," IEEE Transactions on Wireless Communications, accepted, 2025.
Paper Link: https://arxiv.org/pdf/2401.15280.pdf

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

Published: IEEE Wireless Communications , November, 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," IEEE Wireless Communications, accepted, 2024.
Paper Link: https://arxiv.org/pdf/2407.10147

Hybrid-Field Channel Estimation for XL-MIMO Systems with Stochastic Gradient Pursuit Algorithm

Published: IEEE Transactions on Signal Processing , May, 2024

We introduce two stochastic gradient pursuit (SGP)-based schemes for the XL-MIMO hybrid-field channel estimation in two scenarios. For the first scenario in which the prior knowledge of the specific proportion of the number of near-field and far-field channel paths is known, the scheme can effectively leverage the angular-domain sparsity of the far-field channels and the polar-domain sparsity of the near-field channels such that the channel estimation in these two fields can be performed separately. For the second scenario in which the proportion is not available, we propose an off-grid SGP-based channel estimation scheme, which iterates through the values of the proportion parameter based on a criterion before performing the hybrid-field channel estimation.

Citation: Hao Lei, Jiayi Zhang, Zhe Wang, Bo Ai, and Derrick Wing Kwan Ng, “Hybrid-Field Channel Estimation for XL-MIMO Systems with Stochastic Gradient Pursuit Algorithm,” IEEE Transactions on Signal Processing, accepted, 2024.
Paper Link: https://arxiv.org/pdf/2405.15345

Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks

Published: IEEE Wireless Communications , April, 2024

We review the basic properties of near-field communications and focus on the corresponding “resource allocation” problems. First, we identify available resources in near-field communication systems and highlight their distinctions from far-field communications. Then, we summarize optimization tools, such as numerical techniques and machine learning methods, for addressing near-field resource allocation, emphasizing their strengths and limitations. Finally, several important research directions of near-field communications are pointed out for further investigation.

Citation: Bokai Xu, Jiayi Zhang, Hongyang Du, Zhe Wang, Yuanwei Liu, Dusit Niyato, Bo Ai, and Khaled B. Letaief, “Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks,” IEEE Wireless Communications, accepted, 2024.
Paper Link: https://arxiv.org/pdf/2310.17868.pdf

A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications

Published: IEEE Communications Surveys & Tutorials , January, 2024

We provide a comprehensive survey for XL-MIMO technology, including hardware design, channel modeling, signal processing, application scenarios, and future research directions. It is worth noting that the low-complexity signal processing schemes and deep learning empowered signal processing schemes are reviewed and highlighted to promote the practical implementation of XL-MIMO.

Citation: Zhe Wang, Jiayi Zhang, Hongyang Du, Dusit Niyato, Shuguang Cui, Bo Ai, Mérouane Debbah, Khaled B. Letaief, and H. Vincent Poor, "A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications," IEEE Communications Surveys & Tutorials, accepted, 2024.
Paper Link: https://arxiv.org/pdf/2307.07340.pdf

Cell-Free XL-MIMO Meets Multi-Agent Reinforcement Learning: Architectures, Challenges, and Future Directions

Published: IEEE Wireless Communications , October, 2023

In this article, we first review the essential opportunities and challenges induced by XL-MIMO systems. We then propose the enhanced paradigm of cell-free XL-MIMO, which incorporates multi-agent reinforcement learning (MARL) to provide a distributed strategy for tackling the problem of high-dimension signal processing and costly energy consumption.

Citation: Zhilong Liu, Jiayi Zhang, Ziheng Liu, Hongyang Du, Zhe Wang, Dusit Niyato, Mohsen Guizani, and Bo Ai, "Cell-Free XL-MIMO Meets Multi-Agent Reinforcement Learning: Architectures, Challenges, and Future Directions," IEEE Wireless Communications, accepted, 2023.
Paper Link: https://arxiv.org/pdf/2307.02827.pdf

Extremely Large-Scale MIMO: Fundamentals, Challenges, Solutions, and Future Directions

Published: IEEE Wireless Communications , April, 2023

To illustrate the differences and similarities among these schemes, we comprehensively review existing XL-MIMO hardware designs and characteristics in this article. Then, we thoroughly discuss the research status of XL-MIMO from channel modeling, performance analysis, and signal processing. Several existing challenges are introduced and respective solutions are provided. We then propose two case studies for the hybrid propagation channel modeling and the effective degrees of freedom (EDoF) computations for practical scenarios.

Citation: Zhe Wang, Jiayi Zhang, Hongyang Du, Wei E. I. Sha, Bo Ai, Dusit Niyato, and Mérouane Debbah, "Extremely Large-Scale MIMO: Fundamentals, Challenges, Solutions, and Future Directions," IEEE Wireless Communications, accepted, 2023.
Paper Link: https://arxiv.org/pdf/2209.12131.pdf

Low-Complexity Precoding for Extremely Large-Scale MIMO Over Non-Stationary Channels

Published: International Conference on Communications (ICC) 2023 , February, 2023

We investigate a low-complexity precoding algorithm, e.g., randomized Kaczmarz (rKA), taking into account the spatial non-stationary properties in XL-MIMO systems. Furthermore, we propose a novel mode of randomization, i.e., sampling without replacement rKA (SwoR-rKA), which enjoys a faster convergence speed than the rKA algorithm. Besides, the closed-form expression of SE considering the interference between subarrays in downlink XL-MIMO systems is derived.

Citation: Bokai Xu, Zhe Wang, Huahua Xiao, Jiayi Zhang, Bo Ai, and Derrick Wing Kwan Ng, “Low-Complexity Precoding for Extremely Large-Scale MIMO Over Non-Stationary Channels,” International Conference on Communications (ICC), 2023.
Paper Link: https://arxiv.org/pdf/2302.00847.pdf

Uplink Precoding Design for Cell-Free Massive MIMO with Iteratively Weighted MMSE

Published: IEEE Transactions on Communications , December, 2022

To further improve the SE performance for CF mMIMO systems with multi-antenna UE, we design the uplink precoding schemes based on the weighted sum SE maximization. Since the weighted sum SE maximization problem is not jointly over all optimization variables, two efficient uplink precoding schemes based on I-WMMSE algorithms are proposed for two processing schemes investigated.

Citation: Zhe Wang, Jiayi Zhang, Hien Quoc Ngo, Bo Ai, and Mérouane Debbah, “Uplink Precoding Design for Cell-Free Massive MIMO with Iteratively Weighted MMSE,” IEEE Transactions on Communications, vol. 70, no. 3, pp. 1646-1664, Mar. 2023.
Paper Link: https://arxiv.org/pdf/2301.02417.pdf

Uplink Performance of Cell-Free Massive MIMO with Multi-Antenna Users Over Jointly-Correlated Rayleigh Fading Channels

Published: IEEE Transactions on Wireless Communications , February, 2022

We study four uplink implementations for CF mMIMO systems with multi-antenna users, from fully centralized processing to fully distributed processing. We observe that the system performance may not always benefit from the number of antennas per user.

Citation: Zhe Wang, Jiayi Zhang, Bo Ai, Chau Yuen, and Mérouane Debbah, "Uplink Performance of Cell-Free Massive MIMO with Multi-Antenna Users Over Jointly-Correlated Rayleigh Fading Channels," IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7391–7406, Sep. 2022.
Paper Link: https://arxiv.org/pdf/2110.04962.pdf

Uplink Performance of Cell-Free Massive MIMO Over Spatially Correlated Rician Fading Channels

Published: IEEE Communications Letters , December, 2020

We analyze the uplink spectral efficiency for CF mMIMO systems over spatially correlated Rician fading channels. This channel is practical due to the consideration of phase-shift and spatial correlation. We investigate the uplink spectral efficiency (SE) with maximum ratio (MR)/local minimum mean squared error (LMMSE) combining and optimal large-scale fading decoding based on the phase-aware MMSE, phase-aware element-wise MMSE, and linear MMSE (LMMSE) estimators.

Citation: Zhe Wang, Jiayi Zhang, Emil Björnson, and Bo Ai, “Uplink Performance of Cell-Free Massive MIMO Over Spatially Correlated Rician Fading Channels,” IEEE Communications Letters, vol. 25, no. 4, pp. 1348–1352, Apr. 2021.
Paper Link: https://arxiv.org/pdf/2110.05796.pdf