arXiv subjects

Boyu Ning

Publications and source records attributed to Boyu Ning.

At least 19 recordsLinked to original sources

arXiv2026

Bending Beam for THz Wireless Networks: Fundamental, Design Issue, and Prototype

Bending beams, characterized by their non diffracting and self-healing properties in the near field, offer a new approach to bypass blockage in terahertz (THz) wireless communication and sensing. However, the investigations of bend ing beams in the context of wireless communications still remain at an early stage. This article provides a state-of-the-art review of the fundamentals and key application scenarios of bending beams in THz wireless communications and sensing. We first present and compare the existing beamforming design and practical hardware implementation methods for bending beams. Next, we discuss potential applications of bending beams in wireless communica tions and sensing and identify their associated challenges, such as blocked channel modeling, bending beam training, codebook design, etc. Finally, a hardware demonstration of bending beam over THz frequency bands is presented, validating the advantages of bending beam over conventional beamfocusing.

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arXiv2026

Deep Learning-Empowered Movable-Antenna Position Optimization with Partial CSI

Movable antennas (MAs) are a promising technology to improve wireless data rates by dynamically adjusting their positions to avoid deep fading. However, finding the optimal MA positions requires full channel state information (CSI) for all possible locations within the movement region, creating massive channel estimation overhead. This paper proposes a deep neural network (DNN)-based learning framework to predict the optimal positions of multiple transmit MAs in a multi-user multiple-input single-output (MISO) system, entirely bypassing explicit channel estimation.First, we analyze a single-user MISO case, revealing a complex, highly nonlinear mapping between the optimal MA positions and the channel power gains from a specific subset of locations in the transmit region to the user. Because this mapping cannot be mathematically characterized for practical channel models, we train a DNN via supervised learning to capture it. The pre-trained DNN can then determine optimized MA positions in real-time relying only on partial power measurements from the transmit region.Extending this to multi-user scenarios is challenging due to complex rate expressions and the lack of globally optimal position solutions to use as training labels. To overcome this, we develop an unsupervised training framework that directly maximizes the multi-user sum-rate. This framework utilizes an attention-based architecture to extract latent features from the partial channel measurements and effectively manage inter-user interference. Simulation results show that our proposed approach achieves near-optimal performance in single-user systems and surpasses conventional CSI-based alternating optimization algorithms in multi-user environments.

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arXiv2026

Unlocking the Potential of Movable Antennas: General and Practical Antenna Position Optimization

Recently, movable antenna (MA) has attracted wide attention in wireless communications due to its potential in enhancing wireless communication performance via local movement within a confined region. However, antenna position optimization (APO) has emerged as a major challenge for MAs, due to the lack of a tractable, analytical, and accurate channel model in terms of antenna positions. Although existing works have developed various algorithms for APO, most of them are based on simplified theoretical channel models, which limit their generality. To address this challenge, in this article, we present more general and effective APO algorithms for different purposes, categorized as continuous APO and discrete APO, respectively. Continuous APO is mainly applied for flexible array signal processing to boost large-scale communication performance, while discrete APO is applied for small-scale multi-path channel reshaping. Specifically, the discrete APO discretizes the antenna movement region into multiple sampling points and employs discrete algorithms to determine the optimal MA positions based on the point-wise channel state information (CSI), without the need for an analytical channel model. To reduce the overhead for CSI acquisition, we also present more efficient learning-based APO algorithms that operate without requiring full point-wise CSI. Finally, we compare the application scenarios of the proposed algorithms and validate their effectiveness with numerical results.

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arXiv2026

Mixture-of-Experts Diffusion Models for Adaptive Massive MIMO Channel Estimation via Variational Bayesian Inference

Channel estimation is essential to massive multiple-input multiple-output (MIMO) systems. While recent generative model-based approaches using lightweight diffusion models (DMs) have achieved superior performance, they typically rely on a single data-driven prior, which limits their adaptability to varying channel distributions in real-world scenarios. To address this deficiency, we propose a mixture-of-experts (MoE) diffusion model (DM) framework combined with variational Bayesian inference. Specifically, our approach employs multiple pre-trained DMs, with each trained on a specific type of propagation channels. We then propose a probabilistic graphical model in which the channel is modeled as a latent variable drawn from one of these candidate generative priors with a certain probability. By integrating variational Bayesian inference with DM-based data priors, the underlying channel along with the expert indicator variable are jointly inferred, thus enabling automatic model adaptation for channel estimation. The effectiveness of our approach is evaluated on 3GPP CDL channels. Simulation results demonstrate that our proposed approach achieves a clear performance improvement over the standard DM-based method that employs a single prior trained on aggregated data from all channel types, particularly when the channel samples from different propagation environments are imbalanced.

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arXiv2026

Joint Communication and Trajectory Design for Movable Antenna Systems

Movable antennas (MAs) have attracted significant attention in wireless communications due to their ability to reconfigure channel conditions by flexibly adjusting the antenna positions within a confined region. However, MA movement generally incurs a non-negligible delay, which may significantly limit the data transmission time at optimized positions. To tackle this challenge, this paper investigates a new joint communication and trajectory optimization problem, where each MA transmits while moving along an optimized trajectory to prolong the effective data transmission time. Focusing on a single-MA system, our goal is to maximize the average data rate by optimizing the MA's positions over time, subject to its maximum velocity constraints. However, this continuous-time antenna position optimization problem is highly non-convex and challenging to solve. To tackle this challenge, we first consider a special case with two channel paths and derive the optimal MA trajectory in closed form. For other general cases, we ingeniously reformulate the average rate maximization problem into a fixed-hop shortest path problem in graph theory by sampling the antenna movement region into a multitude of discrete points, and solve it optimally. Simulation results demonstrate that our proposed algorithm can significantly improve the data rate compared to other baseline schemes.

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arXiv2026

Terahertz Beam Squint Mitigation via Six-Dimensional Movable Antennas

Analog beamforming holds great potential for future terahertz (THz) communications due to its ability to generate high-gain directional beams with low-cost phase shifters. However, conventional analog beamforming may suffer substantial performance degradation in wideband systems due to the beam squint effect. Instead of relying on high-cost true-time delayers, we propose an efficient six-dimensional movable antenna (6DMA) architecture to mitigate the beam-squint effect. In particular, we study a wideband wide-beam coverage problem in this paper, aiming to maximize the minimum beamforming gain over a given range of azimuth/elevation angles and frequencies by jointly optimizing the analog beamforming vector, the MA positions within a two-dimensional (2D) region, and the three-dimensional (3D) rotation angles of the antenna array. However, this problem is non-convex and intractable to solve optimally due to the coupling of the spatial and frequency domains and that of the antenna weights, positions and rotation. To tackle this problem, we first derive an optimal solution to it in a special case with azimuth or elevation angle coverage only. It is shown that rotating a uniform linear array (ULA) is sufficient to achieve global optimality and eliminate beam-squint effects. While for other general cases, an alternating optimization (AO) algorithm is proposed to obtain a high-quality suboptimal solution, where the antennas' beamforming weights, positions, and rotation angles are alternately optimized by combining successive convex approximation (SCA), sequential update with Gibbs sampling (GS), and hybrid coarse- and fine-grained search. Simulation results demonstrate that our proposed scheme can significantly outperform conventional antenna arrays without antenna movement or rotation, thus offering a cost-effective solution for wideband transmission over THz bands.

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arXiv2026

Indirect and Direct Multiuser Hybrid Beamforming for Far-Field and Near-Field Communications: A Deep Learning Approach

Hybrid beamforming for extremely large-scale multiple-input multiple-output (XL-MIMO) systems is challenging in the near field because the channel depends jointly on angle and distance, and the multiuser interference (MUI) is strong. Existing deep learning methods typically follow either a decoupled design that optimizes analog beamforming without explicitly accounting for MUI, or an end-to-end (E2E) joint analog-digital optimization that can be unstable under nonconvex constant-modulus (CM), pronounced analog-digital coupling, and gradient pattern of sum-rate loss. To address both issues, we develop a complex-valued E2E framework based on a variant minimum mean square error (variant-MMSE) criterion, where the digital precoder is eliminated in closed form via Karush-Kuhn-Tucker (KKT) conditions so that analog learning is trained with a stable objective. The network employs a grouped complex-convolution sensing front-end for uplink (UL) measurements, a shared complex multi-layer perceptron (MLP) for per-user feature extraction, and a merged constant-modulus head to output the analog precoder. In the indirect mode, the network designs hybrid beamformers from estimated channel state information (CSI). In the direct mode where explicit CSI is unavailable, the network learns the sensing operator and the analog mapping from short pilots, after which additional pilots estimate the equivalent channel and enable a KKT closed-form digital precoder. Simulations show that the indirect mode approaches the performance of iterative variant-MMSE optimization with a complexity reduction proportional to the antenna number. In the direct mode, the proposed method improves spectral efficiency over sparse-recovery pipelines and recent deep learning baselines under the same pilot budget.

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arXiv2026

Integrated Channel Estimation and Sensing for Near-Field ELAA Systems via Low-Rank Tensor Decomposition

In this paper, we study the problem of uplink channel estimation for near-filed orthogonal frequency division multiplexing (OFDM) systems, where a base station (BS), equipped with an extremely large-scale antenna array (ELAA), serves multiple users over the same time-frequency resource block. A non-orthogonal pilot transmission scheme is considered to accommodate a larger number of users that can be supported by ELAA systems without incurring an excessive amount of training overhead. To facilitate efficient multi-user channel estimation, we express the received signal as a third-order low-rank tensor, which admits a canonical polyadic decomposition (CPD) model for line-of-sight (LoS) scenarios and a block term decomposition (BTD) model for non-line-of-sight (NLoS) scenarios. An alternating least squares (ALS) algorithm and a non-linear least squares (NLS) algorithm are employed to perform CPD and BTD, respectively. Channel parameters are then efficiently extracted from the recovered factor matrices. By exploiting the geometry of the propagation paths in the estimated channel, users' positions can be precisely determined in LoS scenarios. Moreover, our uniqueness analysis shows that the proposed tensor-based joint multi-user channel estimation framework is effective even when the number of pilot symbols is much smaller than the number of users, revealing its potential in training overhead reduction. Simulation results demonstrate that the proposed method achieves markedly higher channel estimation accuracy than compressed sensing (CS)-based approaches.

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arXiv2026

Movable Antenna-Enhanced Near-Field Flexible Beamforming: Performance Analysis and Optimization

As an emerging wireless communication technology, movable antennas (MAs) offer the ability to adjust the spatial correlation of steering vectors, enabling more flexible beamforming compared to fixed-position antennas (FPAs). In this paper, we investigate the use of MAs for two typical near-field beamforming scenarios: beam nulling and multi-beam forming. In the first scenario, we aim to jointly optimize the positions of multiple MAs and the beamforming vector to maximize the beam gain toward a desired direction while nulling interference toward multiple undesired directions. In the second scenario, the objective is to maximize the minimum beam gain among all the above directions. However, both problems are non-convex and challenging to solve optimally. To gain insights, we first analyze several special cases and show that, with proper positioning of the MAs, directing the beam toward a specific direction can lead to nulls or full gains in other directions in the two scenarios, respectively. For the general cases, we propose a discrete sampling method and an alternating optimization algorithm to obtain high-quality suboptimal solutions to the two formulated problems. Furthermore, considering the practical limitations in antenna positioning accuracy, we analyze the impact of position errors on the performance of the optimized beamforming and MA positions, by introducing a Taylor series approximation for the near-field beam gain at each target. Numerical results validate our theoretical findings and demonstrate the effectiveness of our proposed algorithms.

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arXiv2026

Precoding Matrix Indicator in the 5G NR Protocol: A Tutorial on 3GPP Beamforming Codebooks

This paper bridges this critical gap by providing a systematic examination of the beamforming codebook technology, i.e., precoding matrix indicator (PMI), in the 5G NR from theoretical, standardization, and implementation perspectives. We begin by introducing the background of beamforming in multiple-input multiple-output (MIMO) systems and the signaling procedures for codebook-based beamforming in practical 5G systems. Then, we establish the fundamentals of regular codebooks and port-selection codebooks in 3GPP standards. Next, we provide rigorous technical analysis of 3GPP codebook evolution spanning Releases 15-18, with particular focus on: 1) We elucidate the core principles underlying codebook design, 2) provide clear physical interpretations for each symbolic variable in the codebook formulas, summarized in tabular form, and 3) offer intuitive visual illustrations to explain how codebook parameters convey information. These essential pedagogical elements are almost entirely absent in the often-obscure standardization documents. Through mathematical modeling, performance benchmarking, feedback comparisons, and scenario-dependent applicability analysis, we provide researchers and engineers with a unified understanding of beamforming codebooks in real-world systems. Furthermore, we identify future directions and other beamforming scenarios for ongoing research and development efforts. This work serves as both an informative tutorial and a guidance for future research, facilitating more effective collaboration between academia and industry in advancing wireless communication technologies.

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arXiv2025

Movable Antenna Enhanced Multi-Region Beam Coverage: A Multi-Notch-Filter-Inspired Design

Movable antenna (MA) has emerged as a promising technology to enhance wireless communication performance by exploiting the new degree of freedom (DoF) via antenna position optimization. In this letter, we investigate the MA-enhanced wide beam coverage over multiple subregions in the spatial domain. Specifically, we aim to maximize the minimum beam gain over the desired subregions by jointly optimizing the transmit beamforming and antenna position vector (APV). Although this problem is non-convex, we propose an efficient algorithm to solve it by leveraging the similarity between the considered multi-region coverage and classical multi-notch filter (MNF) design. In particular, we construct a spatial MNF-based transmit beamforming vector by assuming a continuous amplitude and phase-shift profile within the antenna movement region. Based on this continuous profile, we propose a sequential update algorithm to select an optimal subset of MA positions for multi-region coverage, jointly with a Gibbs sampling (GS) procedure to avoid undesired local optimum. Numerical results show that our proposed algorithm can significantly outperform conventional fixed position antennas (FPAs) and achieve a comparable performance to the alternating optimization (AO) algorithm with dramatically lower complexity.

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arXiv2025

Rotatable Antenna Array-Enhanced Null Steering: Performance Analysis and Optimization

Conventional fixed-orientation antenna (FOA) arrays offer limited degrees of freedom (DoF) for flexible beamforming such as null steering. To address this limitation, we propose a new rotatable antenna array (RAA) architecture in this paper, which enables three-dimensional (3D) rotational control of an antenna array to provide enhanced spatial flexibility for null steering. To characterize its performance, we aim to jointly optimize the 3D rotational angles of the RAA, to maximize the beam gain over a given desired direction, while nulling those over multiple interference directions under zero-forcing (ZF) beamforming. However, this problem is non-convex and challenging to tackle due to the highly nonlinear expression of the beam gain in terms of the rotational angles. To gain insights, we first examine several special cases including both isotropic and directional antenna radiation patterns, deriving the conditions under which full beam gain can be achieved over the desired direction while meeting the nulling constraints for interference directions. These conditions clearly indicate that compared with FOA arrays, RAAs can significantly relax the angular separation requirement for achieving effective null steering. For other general cases, we propose a sequential update algorithm, that iteratively refines the 3D rotational angles by discretizing the 3D angular search space. To avoid undesired local optimum, a Gibbs sampling (GS) procedure is also employed between two consecutive rounds of sequential update for solution exploration. Simulation results verify our analytical results and show superior null-steering performance of RAAs to FOA arrays.

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arXiv2025

Movable Antenna Empowered Near-Field Sensing via Antenna Position Optimization

Movable antenna (MA) technology exhibits great promise for enhancing the sensing capabilities of future sixth-generation (6G) networks due to its capability to alter antenna array geometry. With the growing prevalence of near-field propagation at ultra-high frequencies, this paper focuses on the application of one-dimensional (1D) and two-dimensional (2D) MA arrays for near-field sensing to jointly estimate the angle and distance information about a target. First, for the 1D MA array scenario, to gain insights into MA-enhanced near-field sensing, we investigate two simplified cases with only angle-of-arrival (AoA) or distance estimation, respectively, assuming that the other information is already known. The worst-case Cramer-Rao bounds (CRBs) on the mean square errors (MSEs) of the AoA estimation and the distance estimation are derived in these two cases. Then, we jointly optimize the positions of the MAs within the 1D array to minimize these CRBs and derive their closed-form solutions, which yield an identical array geometry to MA-enhanced far-field sensing. For the more challenging joint AoA and distance estimation, since the associated worst-case CRB is a highly complex and non-convex function with respect to the MA positions, a discrete sampling-based approach is proposed to sequentially update the MA positions and obtain an efficient suboptimal solution. Furthermore, we investigate the worst-case CRB minimization problems for a 2D MA array under various conditions and extend our proposed algorithms to solve them efficiently. Numerical results demonstrate that the proposed MA-enhanced near-field sensing scheme dramatically outperforms conventional fixed-position antennas (FPAs). Moreover, the joint angle and distance estimation results in a different array geometry from that in the individual estimation of angle/distance or far-field sensing.

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arXiv2025

Performance Optimization for Movable Antenna Enhanced MISO-OFDM Systems

Movable antenna (MA) technology offers a flexible approach to enhancing wireless channel conditions by adjusting antenna positions within a designated region. While most existing works focus on narrowband MA systems, this paper investigates MA position optimization for an MA-enhanced multiple-input single-output (MISO) orthogonal frequency-division multiplexing (OFDM) system. This problem appears to be particularly challenging due to the frequency-flat nature of MA positioning, which should accommodate the channel conditions across different subcarriers. To overcome this challenge, we discretize the movement region into a multitude of sampling points, thereby converting the continuous position optimization problem into a discrete point selection problem. Although this problem is combinatorial, we develop an efficient partial enumeration algorithm to find the optimal solution using a branch-and-bound framework, where a graph-theoretic method is incorporated to effectively prune suboptimal solutions. In the low signal-to-noise ratio (SNR) regime, a simplified graph-based algorithm is also proposed to obtain the optimal MA positions without the need for enumeration. Simulation results reveal that the proposed algorithm outperforms conventional fixed-position antennas (FPAs), while narrowband-based antenna position optimization can achieve near-optimal performance.

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arXiv2025

Radiation Pattern Reconfigurable FAS-Empowered Interference-Resilient UAV Communication

The widespread use of uncrewed aerial vehicles (UAVs) has propelled the development of advanced techniques on countering unauthorized UAV flights. However, the resistance of legal UAVs to illegal interference remains under-addressed. This paper proposes radiation pattern reconfigurable fluid antenna systems (RPR-FAS)-empowered interference-resilient UAV communication scheme. This scheme integrates the reconfigurable pixel antenna technology, which provides each antenna with an adjustable radiation pattern. Therefore, RPR-FAS can enhance the angular resolution of a UAV with a limited number of antennas, thereby improving spectral efficiency (SE) and interference resilience. Specifically, we first design dedicated radiation pattern adapted from 3GPP-TR-38.901, where the beam direction and half power beamwidth are tailored for UAV communications. Furthermore, we propose a low-storage-overhead orthogonal matching pursuit multiple measurement vectors algorithm, which accurately estimates the angle-of-arrival (AoA) of the communication link, even in the single antenna case. Particularly, by utilizing the Fourier transform to the radiation pattern gain matrix, we design a dimension-reduction technique to achieve 1--2 order-of-magnitude reduction in storage requirements. Meanwhile, we propose a maximum likelihood interference AoA estimation method based on the law of large numbers, so that the SE can be further improved. Finally, alternating optimization is employed to obtain the optimal uplink radiation pattern and combiner, while an exhaustive search is applied to determine the optimal downlink pattern, complemented by the water-filling algorithm for beamforming. Comprehensive simulations demonstrate that the proposed schemes outperform traditional methods in terms of angular sensing precision and spectral efficiency.

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arXiv2025

Energy-Efficient Movable Antennas: Mechanical Power Modeling and Performance Optimization

Movable antennas (MAs) offer additional spatial degrees of freedom (DoFs) to enhance communication performance through local antenna movement. However, to achieve accurate and fast antenna movement, MA drivers entail non-negligible mechanical power consumption, rendering energy efficiency (EE) optimization more critical compared to conventional fixed-position antenna (FPA) systems. To address this issue, we develop a fundamental power consumption model for stepper motor-driven multi-MA systems based on electric motor theory. Based on this model, we formulate an EE maximization problem from a multi-MA base station (BS) to multiple single-FPA users. We aim to jointly optimize the MAs' positions, moving speeds, and the BS's transmit precoding matrix subject to collision-avoidance constraints during the multi-MA movements. However, this problem is difficult to solve. To tackle this challenge, we first reveal that the collision-avoidance constraints can always be relaxed without loss of optimality by properly renumbering the MA indices. For the resulting relaxed problem, we first consider a simplified single-user setup and uncover a hidden monotonicity of the EE performance with respect to the MAs' moving speeds. To solve the remaining optimization problem, we develop a two-layer optimization framework. In the inner layer, the Dinkelbach algorithm is employed to derive the optimal beamforming solution for any given MA positions. In the outer layer, a sequential update algorithm is proposed to iteratively refine the MA positions based on the optimal values obtained from the inner layer. Next, we proceed to the general multi-user case and propose an alternating optimization (AO) algorithm. Numerical results demonstrate that despite the additional mechanical power consumption, the proposed algorithms can outperform both conventional FPA systems and other existing EE maximization benchmarks

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arXiv2025

A General Optimization Framework for Movable Antenna Systems via Discrete Sampling

Movable antenna (MA) systems have attracted growing interest in wireless communications due to their ability to reshape wireless channels via local antenna movement within a confined region. However, optimizing antenna positions to enhance communication performance turns out to be challenging due to the highly nonlinear relationship between wireless channels and antenna positions. Existing approaches, such as gradient-based and heuristic algorithms, often suffer from high computational complexity or undesired local optima. To address the above challenge, this letter proposes a general and low-complexity optimization framework for MA position optimization. Specifically, we discretize the antenna movement region into a set of sampling points, thereby transforming the continuous optimization problem into a discrete point selection problem. Next, we sequentially update the optimal sampling point for each MA over multiple rounds. To avoid convergence to poor local optima, a Gibbs sampling (GS) phase is introduced between rounds to explore adjacent and randomly generated candidate solutions. As a case study, we investigate joint precoding and antenna position optimization for an MA-enhanced broadcast system by applying the proposed framework. Numerical results demonstrate that the proposed algorithm achieves near-optimal performance and significantly outperforms existing benchmarks.

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arXiv2025

On the Secrecy Performance of Pinching-Antenna Systems

Pinching-antenna systems have recently gained significant attention as a novel reconfigurable-antenna technology due to its exceptional capability of mitigating signal-propagation path loss. In this letter, we investigate the secrecy performance of a pinching-antenna system in the presence of an eavesdropper. In particular, we derive an approximate expression of the system's secrecy outage probability (SOP) with respect to the random locations of the legitimate user and eavesdropper and analyze its asymptotic behavior. Moreover, we derive a constant performance lower bound on the SOP of the considered system, i.e., $\frac{2\pi-1}{24}$, which is significantly lower than that of conventional fixed-position antenna systems, i.e., $0.5$. Finally, simulation results are provided to validate the correctness of our analytical results.

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