arXiv subjects

Yuanzhe Gong

Publications and source records attributed to Yuanzhe Gong.

5 recordsLinked to original sources

arXiv2026

Self-Interference-Aware AS-Assisted Tri-Hybrid Beamforming for Full-Duplex Massive MIMO

This paper proposes a tri-hybrid beamforming (tri-HBF) scheme with antenna-selection (AS)-based reconfigurable sub-arrays for full-duplex (FD) massive multiple-input multiple-output (mMIMO) systems. A sub-connected HBF architecture is adopted, where AS is performed in a group-wise manner to avoid excessive switch-network and routing complexity. An alternating optimization (AO) algorithm is developed to jointly optimize the i) active antenna subsets considering a self-interference (SI)-aware utility, ii) analog beamformers through projected gradient ascent (PGA), iii) digital precoders/combiners via SI-aware regularized zero-forcing (RZF) and minimum mean-square error (MMSE) updates, and iv) DL/UL power allocation by successive convex approximation (SCA). To capture realistic electromagnetic coupling in FD mMIMO operation, experimental SI channels based on an 8x8 Tx-8x8 Rx FD array prototype are incorporated into the study. The proposed AS-aided tri-HBF optimization scheme exhibits robust convergence across various base station configurations and effectively balances desired-signal enhancement, SI mitigation, and multi-user interference suppression in FD mMIMO operation. Illustrative results show that selective activation can outperform full-array activation, achieving a 21.3% higher average sum-rate and a more consistent performance across user realizations, with power-efficiency benefits by reducing the active paths. A comprehensive study is conducted to characterize how the number of activated antennas affects the achievable rate, user-channel coherence, and SI suppression gain. Compared with various selection baselines, it achieves a 45.1% improvement in average sum-rate, with average DL and UL rate gains of 36.9% and 82.9%, respectively. In addition, beam-level isolation better than 63 dB is achieved, further confirming the effectiveness of the proposed SI-aware design.

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arXiv2026

Sub-array Selection Optimization for Joint Self-Interference and Multi-User Interference Suppression in FD mMIMO

This paper proposes a beamforming optimization scheme with joint antenna sub-array selection (SAS) and angular perturbation-based nulling (APN) for full-duplex (FD) massive multiple-input multiple-output (mMIMO) systems, to simultaneously suppress self-interference (SI) and multi-user interference (MUI). A comprehensive over-the-air SI channel measurement campaign, conducted with an 8x8Tx-8x8Rx FD array prototype, reveals significant variations across sub-arrays at different spatial locations, as well as reconfigurable characteristics of the SI channel under diverse Tx and Rx sub-array configurations. To exploit the selective SI channels, a particle swarm optimization (PSO)-based algorithm is developed to jointly determine optimal sub-array indices and perturbed steering angles, thereby effectively nullifying potential interference. Selecting sub-arrays with inherently lower SI channels notably enhances the beam-level isolation, while the added selection flexibility among comparable SI channels ensures more uniform SI suppression across diverse DL/UL locations and significantly improves worst-case isolation. Experimental evaluation based on the measured SI channel demonstrates that the proposed SAS technique achieves residual Tx-Rx beam-level SI suppression improvements of 29.2 dB and 26.6 dB for the sample 1x2 and 1x4 sub-arrays, respectively. A worst-case improvement greater than 30.7 dB is observed. Overall, the joint SAS and APN optimization scheme achieves average beam-level isolation of 85.2 dB and 83.3 dB with the 1x2 and 1x4 sub-arrays, respectively. With the application of a baseband precoder, all tested sub-array configurations achieve average MUI suppression better than -181.3 dB. These results confirm the potential of the proposed optimization algorithm to successfully reduce interference to the noise floor, thereby guaranteeing reliable FD mMIMO operation.

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arXiv2025

Joint Positioning, Beamforming, and Power Allocation in Full-Duplex MIMO with Position-Reconfigurable Antenna Arrays

We consider a multi-user (MU) full-duplex (FD) multiple-input multiple-output (MIMO) communication system, in which the base station transceiver is equipped with transmit and receive position reconfigurable antennas (PRAs) to mitigate both MU-interference (MUI) and self-interference (SI) while enhancing the desired signal quality. We first formulate a joint design of beamforming, transmit power allocation, and antenna placement to maximize the weighted sum-rate of the considered PRA-based MU-FD-MIMO system under the constraints on reconfigurable region size, minimum antenna spacing, and transmit power. To address this highly non-convex problem, we then propose an alternating optimization (AO) framework that decomposes the original problem into subproblems and solves them iteratively. In particular, fractional programming techniques are used to separate the optimization variables from the logarithmic and ratio terms, while the block successive upper bound minimization method handles the non-convexity of PRA placement. Simulation results confirm a significant performance gain when integrating PRAs into the MU-FD-MIMO system and demonstrate the advantage of the proposed optimization framework.

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arXiv2025

A Deep Neural Network Codebook Approach for Near-Field Nulling Control Beam Focusing

This paper proposes a deep neural network (DNN) codebook approach for multi-user interference (MUI) mitigation in extremely large multiple-input multiple-output (XL-MIMO) systems operating in the near-field region. Unlike existing DNN-based nulling control beamforming (NCBF) methods that face scalability and complexity challenges, the proposed framework partitions the Fresnel region using correlation-based sampling and assigns a lightweight fully connected DNN model to each subsection. Each model is trained on beamforming weights generated using the linearly constrained minimum variance (LCMV) method, enabling accurate prediction of nulling control beam-focusing weights that simultaneously optimize the desired signal strength and suppress potential interference for both collinear and non-collinear user configurations. Simulation results show that the trained models achieve average phase and magnitude prediction errors of 0.085 radians and 0.52 dB, respectively, across 75 sample subsections. Full-wave simulations in Ansys HFSS further demonstrate that the proposed DNN codebook achieves interference suppression better than 31.64 dB, with a performance gap within 2 dB of the LCMV method, thereby validating its effectiveness in mitigating MUI while reducing computational complexity.

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arXiv2025

DNN-Based Nulling Control Beam Focusing for Near-Field Multi-User Interference Mitigation

This paper proposes a deep learning-based framework for near-field nulling control beam focusing (NCBF) in extra-large MIMO (XL-MIMO) systems to mitigate multi-user interference (MUI). A dual-estimator architecture comprising two fully connected deep neural networks (FCDNNs) is developed to separately predict the phase and magnitude components of NCBF weights, using locations of both desired and interfering users. The models are trained on a large dataset generated via a Linearly Constrained Minimum Variance (LCMV) beamforming algorithm to accommodate diverse user configurations, including both collinear and non-collinear scenarios. Illustrative results demonstrate that the proposed DNN models achieve high prediction accuracy, with test errors of only 0.067 radians for phase estimation and 0.206 dB for magnitude estimation. Full-wave simulations incorporating realistic element radiation patterns and inter-element coupling confirm the close agreement between the beam patterns produced by the DNN-predicted and LCMV-based NCBF schemes under practical deployment conditions. An average MUI suppression of 36.7 dB is achieved, with interference mitigation exceeding 17.5 dB across all tested cases. The proposed approach enables scalable and real-time beam focusing with effective interference suppression, offering a promising solution for future near-field multi-user wireless communications.

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