arXiv subjects

Weidong Mei

Publications and source records attributed to Weidong Mei.

At least 55 records · Page 3Linked to original sources

arXiv2025

STAR-RIS-Enabled Multi-Path Beam Routing with Passive Beam Splitting

Reconfigurable intelligent surfaces (RISs) can be densely deployed in the environment to create multi-reflection line-of-sight (LoS) links for signal coverage enhancement. However, conventional reflection-only RISs can only achieve half-space reflection, which limits the LoS path diversity. In contrast, simultaneously transmitting and reflecting RISs (STAR-RISs) can achieve full-space reflection and transmission, thereby creating more LoS paths. Hence, in this paper, we study a new multi-STAR-RIS-aided communication system, where a multi-antenna base station (BS) transmits to multiple single-antenna users by exploiting the signal beam routing over a set of cascaded LoS paths each formed by multiple STAR-RISs. To reveal essential insights, we first consider a simplified single-user case, aiming to maximize its received signal power by jointly optimizing the active beamforming at the BS, the BS's power allocation over different paths, the number of selected beam-routing paths, the selected STAR-RISs for each path, as well as their amplitude and phase shifts for transmission/reflection. However, this problem is difficult to be optimally solved as different paths may be intricately coupled at their shared STAR-RISs. To tackle this difficulty, we first derive the optimal solution to this problem in closed-form for a given set of paths. The clique-based approach in graph theory is then applied to solve the remaining multi-path selection problem efficiently. Next, we extend the proposed clique-based method to the multi-user case to maximize the minimum received signal power among all users, subject to additional constraints on the disjointness of the selected paths for different users. Simulation results show that our proposed STAR-RIS-enabled beam routing outperforms the conventional beam routing with reflection-only RISs in both single- and multi-user cases.

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arXiv2025

Robust Movable-Antenna Position Optimization with Imperfect CSI for MISO Systems

Movable antenna (MA) technology has emerged as a promising solution for reconfiguring wireless channel conditions through local antenna movement within confined regions. Unlike previous works assuming perfect channel state information (CSI), this letter addresses the robust MA position optimization problem under imperfect CSI conditions for a multiple-input single-output (MISO) MA system. Specifically, we consider two types of CSI errors: norm-bounded and randomly distributed errors, aiming to maximize the worst-case and non-outage received signal power, respectively. For norm-bounded CSI errors, we derive the worst-case received signal power in closed-form. For randomly distributed CSI errors, due to the intractability of the probabilistic constraints, we apply the Bernstein-type inequality to obtain a closed-form lower bound for the non-outage received signal power. Based on these results, we show the optimality of the maximum-ratio transmission for imperfect CSI in both scenarios and employ a graph-based algorithm to obtain the optimal MA positions. Numerical results show that our proposed scheme can even outperform other benchmark schemes implemented under perfect CSI conditions.

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arXiv2025

Mechanical Power Modeling and Energy Efficiency Maximization for Movable Antenna Systems

Movable antennas (MAs) have recently garnered significant attention in wireless communications due to their capability to reshape wireless channels via local antenna movement within a confined region. However, to achieve accurate antenna movement, MA drivers introduce non-negligible mechanical power consumption, rendering energy efficiency (EE) optimization more critical compared to conventional fixed-position antenna (FPA) systems. To address this problem, we develop in this paper a fundamental power consumption model for stepper motor-driven MA systems by resorting to basic electric motor theory. Based on this model, we formulate an EE maximization problem by jointly optimizing an MA's position, moving speed, and transmit power. However, this problem is difficult to solve optimally due to the intricate relationship between the mechanical power consumption and the design variables. To tackle this issue, we first uncover a hidden monotonicity of the EE performance with respect to the MA's moving speed. Then, we apply the Dinkelbach algorithm to obtain the optimal transmit power in a semi-closed form for any given MA position, followed by an enumeration to determine the optimal MA position. Numerical results demonstrate that despite the additional mechanical power consumption, the MA system can outperform the conventional FPA system in terms of EE.

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arXiv2025

A Tutorial on Six-Dimensional Movable Antenna for 6G Networks: Synergizing Positionable and Rotatable Antennas

Six-dimensional movable antenna (6DMA) is a new and revolutionary technique that fully exploits the wireless channel spatial variations at the transmitter/receiver by flexibly adjusting the three-dimensional (3D) positions and/or 3D rotations of antennas/antenna surfaces (sub-arrays), thereby improving the performance of wireless networks cost-effectively without the need to deploy additional antennas. It is thus expected that the integration of new 6DMAs into future sixth-generation (6G) wireless networks will fundamentally enhance antenna agility and adaptability, and introduce new degrees of freedom (DoFs) for system design. Despite its great potential, 6DMA faces new challenges to be efficiently implemented in wireless networks, including corresponding architectures, antenna position and rotation optimization, channel estimation, and system design from both communication and sensing perspectives. In this paper, we provide a tutorial on 6DMA-enhanced wireless networks to address the above issues by unveiling associated new channel models, hardware implementations and practical position/rotation constraints, as well as various appealing applications in wireless networks. Moreover, we discuss two special cases of 6DMA, namely, rotatable 6DMA with fixed antenna position and positionable 6DMA with fixed antenna rotation, and highlight their respective design challenges and applications. We further present prototypes developed for 6DMA-enhanced communication along with experimental results obtained with these prototypes. Finally, we outline promising directions for further investigation.

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arXiv2025

Integrated Sensing, Computing, Communication, and Control for Time-Sequence-Based Semantic Communications

In the upcoming industrial internet of things (IIoT) era, a surge of task-oriented applications will rely on real-time wireless control systems (WCSs). For these systems, ultra-reliable and low-latency wireless communication will be crucial to ensure the timely transmission of control information. To achieve this purpose, we propose a novel time-sequence-based semantic communication paradigm, where an integrated sensing, computing, communication, and control (ISC3) architecture is developed to make sensible semantic inference (SI) for the control information over time sequences, enabling adaptive control of the robot. However, due to the causal correlations in the time sequence, the control information does not present the Markov property. To address this challenge, we compute the mutual information of the control information sensed at the transmitter (Tx) over different time and identify their temporal semantic correlation via a semantic feature extractor (SFE) module. By this means, highly correlated information transmission can be avoided, thus greatly reducing the communication overhead. Meanwhile, a semantic feature reconstructor (SFR) module is employed at the receiver (Rx) to reconstruct the control information based on the previously received one if the information transmission is not activated at the Tx. Furthermore, a control gain policy is also employed at the Rx to adaptively adjust the control gain for the controlled target based on several practical aspects such as the quality of the information transmission from the Tx to the Rx. We design the neural network structures of the above modules/policies and train their parameters by a novel hybrid reward multi-agent deep reinforcement learning framework. On-site experiments are conducted to evaluate the performance of our proposed method in practice, which shows significant gains over other baseline schemes.

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arXiv2025

Near-Field THz Bending Beamforming: A Convex Optimization Perspective

Terahertz (THz) communication systems suffer severe blockage issues, which may significantly degrade the communication coverage and quality. Bending beams, capable of adjusting their propagation direction to bypass obstacles, have recently emerged as a promising solution to resolve this issue by engineering the propagation trajectory of the beam. However, traditional bending beam generation methods rely heavily on the specific geometric properties of the propagation trajectory and can only achieve sub-optimal performance. In this paper, we propose a new and general bending beamforming method by adopting the convex optimization techniques. In particular, we formulate the bending beamforming design as a max-min optimization problem, aiming to optimize the analog or digital transmit beamforming vector to maximize the minimum received signal power among all positions along the bending beam trajectory. However, the resulting problem is non-convex and difficult to be solved optimally. To tackle this difficulty, we apply the successive convex approximation (SCA) technique to obtain a high-quality suboptimal solution. Numerical results show that our proposed bending beamforming method outperforms the traditional method and shows robustness to the obstacle in the environment.

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arXiv2025

Antenna Position Optimization for Movable Antenna-Empowered Near-Field Sensing

Movable antennas (MAs) show great promise for enhancing the sensing capabilities of future sixth-generation (6G) networks. With the growing prevalence of near-field propagation at ultra-high frequencies, this paper focuses on the application of MAs for near-field sensing to jointly estimate the angle and distance information of a target. First, to gain essential insights into MA-enhanced near-field sensing, we investigate two simplified cases with only the spatial angle-of-arrival (AoA) or distance estimation, respectively, assuming that the other information is already known. We derive the worst-case Cramer-Rao bounds (CRBs) on the mean square errors (MSEs) of the AoA estimation and the distance estimation via the multiple signal classification (MUSIC) algorithm in these two cases. Then, we jointly optimize the positions of the MAs within a linear array to minimize these CRBs and derive their closed-form solutions, which yield an identical array geometry to MA-aided far-field sensing. Furthermore, we proceed to the more challenging case with the joint AoA and distance estimation and derive the worst-case CRB under the two-dimensional (2D) MUSIC algorithm. The corresponding CRB minimization problem is efficiently solved by adopting a discrete sampling-based approach. Numerical results demonstrate that the proposed MA-enhanced near-field sensing significantly outperforms conventional sensing with 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 or distance.

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arXiv2025

Flexible Intelligent Metasurface-Aided Wireless Communications: Architecture and Performance

Typical reconfigurable intelligent surface (RIS) implementations include metasurfaces with almost passive unit elements capable of reflecting their incident waves in controllable ways, enhancing wireless communications in a cost-effective manner. In this paper, we advance the concept of intelligent metasurfaces by introducing a flexible array geometry, termed flexible intelligent metasurface (FIM), which supports both element movement (EM) and passive beamforming (PBF). In particular, based on the single-input single-output (SISO) system setup, we first compare three modes of FIM, namely, EM-only, PBF-only, and EM-PBF, in terms of received signal power under different FIM and channel setups. The PBF-only mode, which only adjusts the reflect phase, is shown to be less effective than the EM-only mode in enhancing received signal strength. In a multi-element, multi-path scenario, the EM-only mode improves the received signal power by 125% compared to the PBF-only mode. The EM-PBF mode, which optimizes both element positions and phases, further enhances performance. Additionally, we investigate the channel estimation problem for FIM systems by designing a protocol that gathers EM and PBF measurements, enabling the formulation of a compressive sensing problem for joint cascaded and direct channel estimation. We then propose a sparse recovery algorithm called clustering mean-field variational sparse Bayesian learning, which enhances estimation performance while maintaining low complexity.

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arXiv2025

Modeling and Optimization for Flexible Cylindrical Arrays-Enabled Wireless Communications

Flexible-geometry arrays have garnered much attention in wireless communications, which dynamically adjust wireless channels to improve the system performance. In this paper, we propose a novel flexible-geometry array for a $360^\circ$ coverage, named flxible cylindrical array (FCLA), comprised of multiple flexible circular arrays (FCAs). The elements in each FCA can revolve around the circle track to change their horizontal positions, and the FCAs can move along the vertical axis to change the elements' heights. Considering that horizontal revolving can change the antenna orientation, we adopt both the omni-directional and the directional antenna patterns. Based on the regularized zero-forcing (RZF) precoding scheme, we formulate a particular compressive sensing (CS) problem incorporating joint precoding and antenna position optimization, and propose two effective methods, namely FCLA-J and FCLA-A, to solve it. Specifically, the first method involves jointly optimizing the element's revolving angle, height, and precoding coefficient within a single CS framework. The second method decouples the CS problem into two subproblems by utilizing an alternative sparse optimization approach for the revolving angle and height, thereby reducing time complexity. Simulation results reveal that, when utilizing directional radiation patterns, FCLA-J and FCLA-A achieve substantial performance improvements of 43.32\% and 25.42\%, respectively, compared to uniform cylindrical arrays (UCLAs) with RZF precoding.

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arXiv2025

THz Beam Squint Mitigation via 3D Rotatable 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 effects. Instead of relying on high-cost true time delayers, we propose in this paper an efficient three-dimensional (3D) rotatable antenna technology to mitigate the beam-squint effects, motivated by the fact that beam squint disappears along the boresight direction. In particular, we focus on a wideband wide-beam coverage problem in this paper, aiming to maximize the minimum beamforming gain within a given angle and frequency range by jointly optimizing the analog beamforming vector and the 3D rotation angles of the antenna array. However, this problem is non-convex and difficult to be optimally solved due to the coupling of the spatial and frequency domains and that of the antenna weights and rotation. To tackle this issue, we first reformulate the problem into an equivalent form by merging the spatial and frequency domains into a single composite domain. Next, we combine alternating optimization (AO) and successive convex approximation (SCA) algorithms to optimize the analog beamforming and rotation angles within this composite domain. Simulation results demonstrate that the proposed scheme can significantly outperform conventional schemes without antenna rotation, thus offering a cost-effective solution for wideband transmission over THz bands.

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arXiv2025

Hierarchically Tunable 6DMA for Wireless Communication and Sensing: Modeling and Performance Optimization

This paper proposes a new hierarchically tunable six-dimensional movable antenna (HT-6DMA) architecture for base station (BS) in future wireless networks. The HT-6DMA BS consists of multiple antenna arrays that can flexibly move on a spherical surface, with their three-dimensional (3D) positions and 3D rotations/orientations efficiently characterized in the global spherical coordinate system (SCS) and their individual local SCSs, respectively. As a result, the 6DMA system is hierarchically tunable in the sense that each array's global position and local rotation can be separately adjusted in a sequential manner with the other being fixed, thus greatly reducing their design complexity and improving the achievable performance. In particular, we consider an HT-6DMA BS serving multiple single-antenna users in the uplink communication or sensing potential unmanned aerial vehicles (UAVs)/drones in a given airway area. Specifically, for the communication scenario, we aim to maximize the average sum rate of communication users in the long term by optimizing the positions and rotations of all 6DMA arrays at the BS. While for the airway sensing scenario, we maximize the minimum received sensing signal power along the airway by optimizing the 6DMA arrays' positions and rotations along with the BS's transmit covariance matrix. Despite that the formulated problems are both non-convex, we propose efficient solutions to them by exploiting the hierarchical tunability of positions/rotations of 6DMA arrays in our proposed model. Numerical results show that the proposed HT-6DMA design significantly outperforms not only the traditional BS with fixed-position antennas (FPAs), but also the existing 6DMA scheme. Furthermore, it is unveiled that the performance gains of HT-6DMA mostly come from the arrays' global position adjustments on the spherical surface, rather than their local rotation adjustments.

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arXiv2025

A Tutorial on Movable Antennas for Wireless Networks

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed.

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arXiv2025

Power-Measurement-Based Channel Autocorrelation Estimation for IRS-Assisted Wideband Communications

Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user's reference signal received power (RSRP) measurements with time-varying random IRS training reflections. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS reflection designs based on user power measurements.

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arXiv2025

Movable Antenna Enhanced DF and AF Relaying Systems: Performance Analysis and Optimization

Movable antenna (MA) has been deemed as a promising technology to flexibly reconfigure wireless channels by adjusting the antenna positions in a given local region. In this paper, we investigate the application of the MA technology in both decode-and-forward (DF) and amplify-and-forward (AF) relaying systems, where a relay is equipped with multiple MAs to assist in the data transmission between two single-antenna nodes. For the DF relaying system, our objective is to maximize the achievable rate at the destination by jointly optimizing the positions of the MAs in two stages for receiving signals from the source and transmitting signals to the destination, respectively. To drive essential insights, we first derive a closed-form upper bound on the maximum achievable rate of the DF relaying system. Then, a low-complexity algorithm based on projected gradient ascent (PGA) and alternating optimization (AO) is proposed to solve the antenna position optimization problem. For the AF relaying system, our objective is to maximize the achievable rate by jointly optimizing the two-stage MA positions as well as the AF beamforming matrix at the relay, which results in a more challenging optimization problem due to the intricate coupling variables. To tackle this challenge, we first reveal the hidden separability among the antenna position optimization in the two stages and the beamforming optimization. Based on such separability, we derive a closed-form upper bound on the maximum achievable rate of the AF relaying system and propose a low-complexity algorithm to obtain a high-quality suboptimal solution to the considered problem. Simulation results validate the efficacy of our theoretical analysis and demonstrate the superiority of the MA-enhanced relaying systems to the conventional relaying systems with fixed-position antennas (FPAs) and other benchmark schemes.

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arXiv2024

UAV-Enabled Passive 6D Movable Antennas: Joint Deployment and Beamforming Optimization

Intelligent reflecting surface (IRS) is composed of numerous passive reflecting elements and can be mounted on unmanned aerial vehicles (UAVs) to achieve six-dimensional (6D) movement by adjusting the UAV's three-dimensional (3D) location and 3D orientation simultaneously. Hence, in this paper, we investigate a new UAV-enabled passive 6D movable antenna (6DMA) architecture by mounting an IRS on a UAV and address the associated joint deployment and beamforming optimization problem. In particular, we consider a passive 6DMA-aided multicast system with a multi-antenna base station (BS) and multiple remote users, aiming to jointly optimize the IRS's location and 3D orientation, as well as its passive beamforming to maximize the minimum received signal-to-noise ratio (SNR) among all users under the practical angle-dependent signal reflection model. However, this optimization problem is challenging to be optimally solved due to the intricate relationship between the users' SNRs and the IRS's location and orientation. To tackle this challenge, we first focus on a simplified case with a single user, showing that one-dimensional (1D) orientation suffices to achieve the optimal performance. Next, we show that for any given IRS's location, the optimal 1D orientation can be derived in closed form, based on which several useful insights are drawn. To solve the max-min SNR problem in the general multi-user case, we propose an alternating optimization (AO) algorithm by alternately optimizing the IRS's beamforming and location/orientation via successive convex approximation (SCA) and hybrid coarse- and fine-grained search, respectively. To avoid undesirable local sub-optimal solutions, a Gibbs sampling (GS) method is proposed to generate new IRS locations and orientations for exploration in each AO iteration. Numerical results validate our theoretical analyses.

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arXiv2024

Throughput Maximization for Movable Antenna Systems with Movement Delay Consideration

In this paper, we model the minimum achievable throughput within a transmission block of restricted duration and aim to maximize it in movable antenna (MA)-enabled multiuser downlink communications. Particularly, we account for the antenna moving delay caused by mechanical movement, which has not been fully considered in previous studies, and reveal the trade-off between the delay and signal-to-interference-plus-noise ratio at users. To this end, we first consider a single-user setup to analyze the necessity of antenna movement. By quantizing the virtual angles of arrival, we derive the requisite region size for antenna moving, design the initial MA position, and elucidate the relationship between quantization resolution and moving region size. Furthermore, an efficient algorithm is developed to optimize MA position via successive convex approximation, which is subsequently extended to the general multiuser setup. Numerical results demonstrate that the proposed algorithms outperform fixed-position antenna schemes and existing ones without consideration of movement delay. Additionally, our algorithms exhibit excellent adaptability and stability across various transmission block durations and moving region sizes, and are robust to different antenna moving speeds. This allows the hardware cost of MA-aided systems to be reduced by employing low rotational speed motors.

cs.IT↗

arXiv2024

Disco Intelligent Omni-Surfaces: 360-degree Fully-Passive Jamming Attacks

Intelligent omni-surfaces (IOSs) with 360-degree electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a "DISCO" IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based FPJ, the interrelated refractive and reflective (R&R) coefficients of the adversarial IOS are randomly generated, acting like a "DISCO" that distributes wireless energy radiated by the base station. By introducing active channel aging (ACA) during channel coherence time, the DIOS-based FPJ can perform omnidirectional fully-passive jamming without neither jamming power nor channel knowledge of legitimate users (LUs). To characterize the impact of the DIOS-based PFJ, we derive the statistical characteristics of DIOS-jammed channels based on two widely-used IOS models, i.e., the constant-amplitude model and the variable-amplitude model. Consequently, the asymptotic analysis of the ergodic achievable sum rates under the DIOS-based omnidirectional fully-passive jamming is given based on the derived stochastic characteristics for both the two IOS models. Based on the derived analysis, the omnidirectional jamming impact of the proposed DIOS-based FPJ implemented by a constant-amplitude IOS does not depend on either the quantization number or the stochastic distribution of the DIOS coefficients, while the conclusion does not hold on when a variable-amplitude IOS is used. Numerical results based on one-bit quantization of the IOS phase shifts are provided to verify the effectiveness of the derived theoretical analysis. The proposed DIOS-based FPJ can not only launch omnidirectional fully-passive jamming, but also improve the jamming impact by about 55% at 10 dBm transmit power per LU.

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arXiv2024

RSRP Measurement Based Channel Autocorrelation Estimation for IRS-Aided Wideband Communication

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing powermeasurement-based IRS reflection designs in wideband channels.

cs.IT↗