Lipeng Zhu

Intelligent reflecting surfaces (IRSs) enhance wireless network performance by intelligently reconfiguring the propagation environment to improve capacity, coverage, and energy efficiency, making them an appealing technology for future sixth-generation (6G) systems. However, conventional IRS design paradigms typically rely on pilot-training-based reflection updates to maintain communication quality in a dynamic environment, which are hindered by significant practical challenges, such as the high implementation overhead caused by frequent channel estimation and reflection reconfiguration, excessive system cost due to base station (BS)-IRS coordination, and lack of robustness against non-ideal channel conditions. To tackle these challenges, this article provides a comprehensive overview of the promising quasi-static IRS reflection design framework for wireless network coverage enhancement, by utilizing easily accessible received signal power measurements at user terminals. Practical design issues and open problems in optimizing this framework are discussed, including long-term network coverage performance characterization, power measurement collection under existing network protocols, and power-measurement-based IRS reflection optimization and robust design strategies. Ray tracing-based simulation results validate the effectiveness of the quasi-static IRS reflection design in practical narrowband and wideband systems. Lastly, promising approaches for IRS deployment and reflection optimization in more general settings are discussed to guide future research.

With the exponential growth of connected devices and the advent of 6G, embodied agentic artificial intelligence (AI) emerges as a transformative paradigm for next-generation wireless networks. In this study, we leverage it to aid unmanned aerial vehicle (UAV)-assisted multi-access edge computing (MEC) to provide efficient and reliable computation offloading for resource-constrained ground users. To cope with dynamic task arrivals and limited resource availability, we propose a two-timescale optimization framework that simultaneously minimizes the average task completion delay while maintaining load balance among UAVs and ensuring a low task failure rate. For the long-term system configuration, we develop a hybrid K-means and Particle Swarm Optimization (HKPSO) algorithm to jointly optimize UAV deployment and user association. For the short-term resource management, we design a two-layer Deep Reinforcement Learning (DRL) architecture where each UAV acts as an embodied agent. A Masked Double Deep Q-Network (MDDQN) handles discrete transmission scheduling while a Softmax Twin Delayed Deep Deterministic Policy Gradient (STD3) algorithm manages continuous computation allocation. Extensive experiments demonstrate that our proposed framework outperforms the baseline algorithms, validating its effectiveness and practicality in dynamic multi-UAV MEC systems.

Movable antenna (MA) is an emerging technology that can significantly improve communication performance via the continuous adjustment of the antenna positions. To unleash the potential of MAs in wideband communication systems, acquiring accurate channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers, is a crucial issue. In this paper, we study the channel estimation problem for wideband MA systems. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at selected position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem with multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Moreover, we also devise an alternating refinement approach to further improve the accuracy of the estimated FRVs, DRV, and PRT. This is achieved by minimizing the discrepancy between the received pilots and those constructed by the estimated CSI, which can be efficiently carried out by using the gradient descent algorithm. Finally, simulation results demonstrate that both the SOMP-based channel estimation method and alternating refinement method can reconstruct the complete wideband CSI with high accuracy, where the alternating refinement method performs better despite a higher complexity.

The sixth-generation (6G) mobile communication systems are expected to provide seamless connectivity for unmanned aerial vehicles (UAVs) to support them in fulfilling various tasks. However, the line-of-sight (LoS)-dominated channels of cellular-connected UAVs expose them to severe co-channel interference from nearby base stations (BSs), which significantly degrades communication reliability. To address this challenge, this paper investigates a movable antenna (MA)-enhanced cellular-connected UAV communication system, where the additional spatial degrees of freedom (DoFs) offered by MAs are exploited for the interference-aware UAV trajectory planning. Specifically, we formulate an optimization problem to minimize the UAV mission completion time by jointly optimizing the UAV beamforming matrix, antenna position vector (APV), UAV trajectory, and UAV–BS association, subject to constraints on signal-to-interference-plus-noise ratio (SINR) requirements, UAV mobility, and MA mobility. To overcome the inherent challenges of the continuous-time formulation, we discretize both the flight region and trajectory of the UAV, thereby reformulating the problem into a tractable discrete optimization problem. A selective uniform cost search (SUCS) algorithm is then developed for UAV trajectory planning, where the feasibility of candidate grid points is evaluated by jointly optimizing beamforming, APV, and UAV–BS association to maximize the expected SINR. Simulation results show that, compared with benchmark schemes, the proposed MA-enhanced design significantly improves the expected SINR of cellular-connected UAVs along the optimized trajectory, thereby reducing UAV mission completion time while ensuring reliable communication links.

Movable antenna (MA) systems enhance wireless communication performance by dynamically adjusting antennas’ positions. However, most existing MA studies treat antennas’ mutual coupling as detrimental and neglect it by enforcing interantenna spacing larger than half-wavelength, which may result in model inaccuracy. This letter investigates the effect and potential benefits of mutual coupling in MA arrays for multi-user downlink communications. Specifically, we allow inter-antenna spacing to be less than half-wavelength and establish a mutual coupling-aware system model. The sum-rate maximization problem is formulated by jointly optimizing antennas’ positions and beamforming vectors with the position-dependent antenna mutual coupling matrix considered. To solve this highly non-convex problem, we propose a nested Greedy Search and Gradient Descent with Fractional Programming (GS-GD-FP) algorithm. It employs GS-GD in the outer loop to optimize antennas’ positions, while the Fractional Programming (FP) method is utilized in the inner loop to optimize beamforming vectors and attain the maximum achievable sum-rate for given antennas’ positions. Numerical results demonstrate that the proposed coupling-aware MA array achieves significant sum-rate gains over conventional MA arrays optimized without considering mutual coupling in various scenarios.

This paper proposes an efficient method for modeling and reconstructing the channel gain map (CGM) based on virtual scatterers. Specifically, we develop a virtual scatterer model to characterize the channel gain distribution in three-dimensional (3D) space, by capturing the multi-path propagation environment structure and exploiting the angular-domain spatial correlation of scatterer response. In this model, the CGM is represented as a function over a set of tunable parameters for virtual scatterers, including their number, positions, and scatterer response coefficients (SRCs), which can be estimated from a limited number of channel gain measurements at a given set of locations within the region of interest. This new representation offers a flexible and scalable modeling framework for efficient and accurate CGM reconstruction. Furthermore, we propose a progressive estimation algorithm to acquire the scatterers’ parameters. In this algorithm, we gradually increase the number of virtual scatterers to balance the computational complexity and reconstruction accuracy, and derive the closed-form solutions of SRCs with any given number and positions of virtual scatterers. In addition, by exploiting the spatial correlation of scatterer response, we propose a Gaussian process regression (GPR)-based inference method to predict the SRCs that cannot be directly estimated. Finally, ray-tracing-based simulation results under realistic physical environments validate the effectiveness of the proposed method, demonstrating that it achieves higher reconstruction accuracy compared to conventional CGM estimation approaches, especially for the scenario with limited channel measurements.

In conventional antenna arrays, mutual coupling between antenna elements is often regarded as detrimental. However, under specific conditions, it can be harnessed to enhance the far-field directivity (i.e., beamforming gain). Theoretically, the directivity of an N-antenna superdirective array over the endfire direction can reach N², significantly exceeding the directivity of a traditional uncoupled array which is N over all directions. This paper investigates the potential of mutual coupling effects in movable antenna (MA) arrays for directivity enhancement. A low-complexity algorithm called Greedy Search and Gradient Descent (GS-GD) is proposed to optimize the antenna positions for maximizing the array directivity over any given direction, where the antenna positions are first selected sequentially from discrete grid points and then continuously refined through gradient descent (GD) optimization. Numerical results demonstrate that the optimized MA array design by exploiting the antenna coupling achieves significant directivity gains compared to the conventional uniform linear array (ULA) without antenna coupling over all directions. Additionally, the proposed GS-GD algorithm is shown to approach the global optimum closely in most directions.

Movable antenna (MA) has emerged as a promising technology for future wireless systems. Compared with traditional fixed-position antennas, MA improves system performance by antenna movement to optimize channel conditions. For multiuser wideband MA systems, this paper proposes deep learning-based framework integrating channel estimation (CE), antenna position optimization, and beamforming, with a clear workflow and enhanced efficiency. Specifically, to obtain accurate channel state information (CSI), we design a two-stage CE mechanism: first reconstructing the channel matrix from limited measurements via compressive sensing, then introducing a Swin-Transformer-based denoising network to refine CE accuracy for subsequent optimization. Building on this, we address the joint optimization challenge by proposing a Transformer-based network that intelligently maps CSI sequences of candidate positions to optimal MA positions while combining a model-driven weighted minimum mean square error (WMMSE) beamforming approach to achieve better performance. Simulation results demonstrate that the proposed methods achieve superior performance compared with existing counterparts under various conditions. The codes about this work are available at https://github.com/ZiweiWan/Code-4-DL-MA-CE-BF.

This paper presents an end-to-end deep learning framework in a movable antenna (MA)-enabled multiuser communication system. In contrast to the conventional works assuming perfect channel state information (CSI) for MA placement or adopting a decoupled CSI acquisition and MA placement design paradigm, we address the practical CSI acquisition issue through the design of pilot signals and quantized CSI feedback, and further incorporate the joint optimization of channel estimation, MA placement, and precoding design. The proposed mechanism enables the system to learn an optimized transmission strategy from imperfect channel data, overcoming the limitations of conventional methods that conduct channel estimation and antenna position optimization separately. To balance the performance and overhead, we further extend the proposed framework to optimize the antenna placement based on the statistical CSI. Simulation results demonstrate that the proposed approach consistently outperforms traditional benchmarks in terms of achievable sum-rate of users, especially under limited feedback and sparse channel environments. Notably, it achieves a performance comparable to the widely-adopted gradient-based methods with perfect CSI, while maintaining significantly lower CSI feedback overhead. These results highlight the effectiveness and adaptability of learning-based MA system design for future wireless systems.

Six-dimensional movable antenna (6DMA) introduces spatial degrees of freedom (DoFs) through joint three-dimensional (3D) position and rotation adjustment, offering great potential for enhancing physical-layer security (PLS). However, when eavesdroppers (Eves) are angularly proximate to legitimate users or occupy dominant propagation paths, existing movable antenna schemes that optimize only position or rotation separately cannot fully exploit spatial DoFs, resulting in limited security performance. To overcome this limitation, this letter investigates a 6DMA-assisted secure communication system where a base station equipped with 6DMA surfaces transmits confidential information to multiple legitimate users in the presence of multiple Eves. We aim to maximize the sum secrecy rate by jointly optimizing the transmit beamforming and artificial noise (AN) vector, along with the 3D positions and 3D rotations of all 6DMA surfaces. Accordingly, we propose an efficient alternating optimization (AO) algorithm to obtain a high-quality suboptimal solution, where the positions and rotations of 6DMA surfaces are optimized via a proximal successive convex approximation (PSCA) technique. Simulation results verify the potential of the 6DMA-assisted wireless system for secure communication, and demonstrate that the incorporation of AN into the 6DMA-assisted design can further improve the secrecy performance.