arXiv subjects

Yuanwei Liu

Publications and source records attributed to Yuanwei Liu.

At least 199 records · Page 11Linked to original sources

arXiv2025

Optimal Beamforming for Multi-User Continuous Aperture Array (CAPA) Systems

The optimal beamforming design for multi-user continuous aperture array (CAPA) systems is proposed. In contrast to conventional spatially discrete array (SPDA), the beamformer for CAPA is a continuous function rather than a discrete vector or matrix, rendering beamforming optimization a non-convex integral-based functional programming. To address this challenging issue, the closed-form optimal structure of the CAPA beamformer is first derived for maximizing generic system utility functions, by addressing the inversion of continuous functions and using the Lagrangian duality and the calculus of variations. The derived optimal structure is a linear combination of the continuous channel responses for CAPA, with the linear weights determined by the channel correlations. As a further advance, a monotonic optimization method is proposed for obtaining globally optimal CAPA beamforming based on the derived optimal structure. More particularly, a closed-form fixed-point iteration is proposed to obtain the globally optimal solution to the power minimization problem for CAPA beamforming. Furthermore, based on the optimal structure, the low-complexity maximum ratio transmission (MRT), zero-forcing (ZF), and minimum mean-squared error (MMSE) designs for CAPA beamforming are derived. It is theoretically proved that: 1) the MRT and ZF designs are asymptotically optimal in low and high signal-to-noise ratio (SNR) regimes, respectively, and 2) the MMSE design is optimal for signal-to-leakage-plus-noise ratio (SLNR) maximization. Our numerical results validate the effectiveness of the proposed designs and reveal that: i) CAPA achieves significant communication performance gain over SPDA, and ii) the MMSE design achieves nearly optimal performance in most cases, while the MRT and ZF designs achieve nearly optimal performance in specific cases

cs.IT↗

arXiv2025

Performance Analysis of Near-Field Sensing in Wideband MIMO Systems

The performance of near-field sensing (NISE) in a legacy wideband multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system is analyzed. The maximum likelihood estimates (MLE) for the target's distance and angle relative to the antenna array are derived. To evaluate the estimation error, closed-form analytical expressions of Cramer-Rao bounds (CRBs) are derived for both uniform linear arrays (ULAs) and uniform circular arrays (UCAs). The asymptotic CRBs are then analyzed to reveal the scaling laws of CRBs with respect to key system parameters, including array size, bandwidth, and target distance. Our results reveal that 1) the mean-squared error achieved by MLEs approaches CRBs in the high signal-to-noise ratio regime; 2) a larger array aperture does not necessarily improve NISE performance, especially with ultra-large bandwidth; 3) large bandwidth sets an estimation error ceiling for NISE as target distance increases; 4) array aperture and bandwidth, rather than the number of antennas and subcarriers, are the key factors affecting wideband NISE performance; and 5) UCAs offer superior, angle-independent wideband NISE performance compared to ULAs with the same aperture.

cs.IT↗

arXiv2025

Continuous-Aperture Array (CAPA)-Based Wireless Communications: Capacity Characterization

The capacity limits of continuous-aperture array (CAPA)-based wireless communications are characterized. To this end, an analytically tractable transmission framework is established for both uplink and downlink CAPA systems. Based on this framework, closed-form expressions for the single-user channel capacity are derived. The results are further extended to a multiuser case by characterizing the capacity limits of a two-user channel and proposing the associated capacity-achieving decoding and encoding schemes. In the uplink case, the capacity-achieving detectors and sum-rate capacity are derived, and the capacity region is characterized. In the downlink case, the uplink-downlink duality is established by deriving the uplink-to-downlink and downlink-to-uplink transformations under the same power constraint, based on which the optimal source current distributions and the achieved sum-rate capacity and capacity region are characterized. For comparison, the uplink and downlink sum-rates achieved by the linear zero-forcing scheme are also analyzed. To gain further insights, several case studies are presented by specializing the derived results into various array structures, including the planar CAPA, linear CAPA, and planar spatially discrete array (SPDA). Numerical results are provided to reveal that the channel capacity achieved by CAPAs converges towards a finite upper bound as the aperture size increases; and CAPAs offer superior capacity over the conventional SPDAs.

cs.IT↗

arXiv2025

On the Performance of Uplink Pinching Antenna Systems (PASS)

Pinching antenna (PA) is a flexible antenna composed of a waveguide and multiple dielectric particles, which is capable of reconfiguring wireless channels intelligently in line-of-sight links. By leveraging the unique features of PAs, we exploit the uplink (UL) transmission in pinching antenna systems (PASS). To comprehensively evaluate the performance gains of PASS in UL transmissions, three scenarios, multiple PAs for a single user (MPSU), a single PA for a single user (SPSU), and a single PA for multiple users (SPMU) are considered. The positions of PAs are optimized to obtain the maximal channel gains in the considered scenarios. For the MPSU and SPSU scenarios, by applying the optimized position of PAs, closed-form expressions for analytical, asymptotic and approximated ergodic rate are derived. As the further advance, closed-form expressions of approximated ergodic rate is derived when a single PA is fixed in the SPMU scenario. Our results demonstrate the following key insights: i) The proposed PASS significantly outperforms conventional Multiple-input Single-output networks by exploiting the flexibility of PAs; ii) The PA distribution follows an asymmetric non-uniform distribution in the MPSU scenario; iii) Optimizing PA positions significantly enhances the ergodic sum rate performance.

cs.IT↗

arXiv2025

Large Language Model Agents for Radio Map Generation and Wireless Network Planning

Using commercial software for radio map generation and wireless network planning often require complex manual operations, posing significant challenges in terms of scalability, adaptability, and user-friendliness, due to heavy manual operations. To address these issues, we propose an automated solution that employs large language model (LLM) agents. These agents are designed to autonomously generate radio maps and facilitate wireless network planning for specified areas, thereby minimizing the necessity for extensive manual intervention. To validate the effectiveness of our proposed solution, we develop a software platform that integrates LLM agents. Experimental results demonstrate that a large amount manual operations can be saved via the proposed LLM agent, and the automated solutions can achieve an enhanced coverage and signal-to-interference-noise ratio (SINR), especially in urban environments.

cs.IT↗

arXiv2025

Beam Training for Pinching-Antenna Systems (PASS)

This article investigates the beam training design problems for pinching-antenna systems (PASS), where single-waveguide-single-user (SWSU), single-waveguide-multi-user (SWMU) and multi-waveguide-multi-user (MWMU) scenarios are considered. For SWSU-PASS, we design a scalable codebook, based on which we propose a three-stage beam training (3SBT) scheme. Specifically, 1) firstly, the 3SBT scheme utilizes one activated pinching antenna to obtain a coarse one-dimensional location at the first stage; 2) secondly, it achieves further phase matching with an increased number of activated antennas at the second stage; 3) finally, it realizes precise beam alignment through an exhaustive search at the third stage. For SWMU-PASS, based on the scalable codebook design, we propose an improved 3SBT scheme to support non-orthogonal multiple access (NOMA) transmission. For MWMU-PASS, we first present a generalized expression of the received signal based on the partially-connected hybrid beamforming structure. Furthermore, we introduce an increased-dimensional scalable codebook design, based on which an increased-dimensional 3SBT scheme is proposed. Numerical results reveal that: i) the proposed beam training scheme can significantly reduce the training overhead compared to the two-dimensional exhaustive search, while maintaining reasonable rate performance; ii) compared to fixed-location pinching antennas and conventional array antennas, the proposed dynamic pinching antennas yield better flexibility and improved performance.

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arXiv2025

GPASS: Deep Learning for Beamforming in Pinching-Antenna Systems (PASS)

A novel GPASS architecture is proposed for jointly learning pinching beamforming and transmit beamforming in pinching antenna systems (PASS). The GPASS is with a staged architecture, where the positions of pinching antennas are first learned by a sub-GNN. Then, the transmit beamforming is learned by another sub-GNN based on the antenna positions. The sub-GNNs are incorporated with the permutation property of the beamforming policy, which helps improve the learning performance. The optimal solution structure of transmit beamforming is also leveraged to simplify the mappings to be learned. Numerical results demonstrate that the proposed architecture can achieve a higher SE than a heuristic baseline method with low inference complexity.

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arXiv2025

Diffusion Model for Multiple Antenna Communications

The potential of applying diffusion models (DMs) for multiple antenna communications is discussed. A unified framework of applying DM for multiple antenna tasks is first proposed. Then, the tasks are innovatively divided into two categories, i.e., decision-making tasks and generation tasks, depending on whether an optimization of system parameters is involved. For each category, it is conceived 1) how the framework can be used for each task and 2) why the DM is superior to traditional artificial intelligence (TAI) and conventional optimization tasks. It is highlighted that the DMs are well-suited for scenarios with strong interference and noise, excelling in modeling complex data distribution and exploring better actions. A case study of learning beamforming with a DM is then provided, to demonstrate the superiority of the DMs with simulation results. Finally, the applications of DM for emerging multiple antenna technologies and promising research directions are discussed.

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arXiv2025

Design and Prototyping of Filtering Active STAR-RIS with Adjustable Power Splitting

Reconfigurable Intelligent Surfaces (RISs) have emerged as a transformative technology for next-generation wireless communication systems, offering unprecedented control over electromagnetic wave propagation. In particular, Simultaneously Transmitting and Reflecting RISs (STAR-RISs) have garnered significant attention due to their full-space coverage. This paper presents an active STAR-RIS, which enables independent control of both transmission and reflection phases and features out-of-band harmonic suppression. Unlike the traditional passive RIS, the proposed design integrates active amplification to overcome the inherent passive losses, significantly enhancing signal strength and system performance. Additionally, the system supports dynamic power allocation between transmission and reflection modes, providing greater flexibility to meet diverse communication demands in complex propagation environments. The versatility of the design is further validated by extending the Radar Cross Section (RCS)-based path loss model to the STAR-RIS. This design improves efficiency, flexibility, and adaptability, offering a promising solution for future wireless communication systems, particularly in scenarios requiring simultaneous control of transmission and reflection signals.

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arXiv2025

Performance Analysis of Holographic MIMO Based Integrated Sensing and Communications

A holographic multiple-input multiple-output (MIMO)-based integrated sensing and communications (ISAC) framework is proposed for both downlink and uplink scenarios. The spatial correlation is incorporated into the communication channel modeling, while a spherical wave-based model is used to characterize the sensing link. By considering both instantaneous and statistical channel state information, closed-form expressions are derived for sensing rates (SRs), communication rates (CRs), and outage probabilities under various ISAC designs. This enables an investigation into the theoretical performance limits of the proposed holographic MIMO-based ISAC (HISAC) framework. Further insights are gained by examining the high signal-to-noise ratio (SNR) slopes and diversity orders. Specifically: i) for the downlink case, a sensing-centric (S-C) design and a communications-centric (C-C) design are investigated using different beamforming strategies, and a Pareto optimal design is proposed to characterize the attainable SR-CR region; ii) for the uplink case, the S-C design and the C-C design differ in the interference cancellation order between the communication and sensing signals, with the rate region obtained through a time-sharing strategy. Numerical results are provided to demonstrate that HISAC systems outperform both conventional MIMO-based ISAC systems and holographic MIMO-based frequency-division sensing and communications systems, underscoring the superior performance of the HISAC framework.

cs.IT↗

arXiv2025

On the Sum Rate and User Fairness of STAR-RIS Aided Communications

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided communication system is investigated. A robust joint beamforming design problem under the imperfect channel state information (CSI) is formulated to maximize the weighted sum of the Jain's fairness index and the normalized system sum rate. To solve this non-convex problem, an alternating optimization (AO) algorithm is proposed, which leverages the S-Procedure, successive convex approximation (SCA), and semidefinite relaxation (SDR). Simulation results demonstrate that with proposed algorithm: 1) various trade-offs between sum rate and user fairness can be achieved; 2) a larger trade-off region can be achieved by adopting STAR-RIS compared to conventional RIS; and 3) the performance degradation caused by imperfect CSI is less than 7% with our proposed robust beamforming approach.

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arXiv2025

Wideband Beamforming for RIS Assisted Near-Field Communications

A near-field wideband beamforming scheme is investigated for reconfigurable intelligent surface (RIS) assisted multiple-input multiple-output (MIMO) systems, in which a deep learning-based end-to-end (E2E) optimization framework is proposed to maximize the system spectral efficiency. To deal with the near-field double beam split effect, the base station is equipped with frequency-dependent hybrid precoding architecture by introducing sub-connected true time delay (TTD) units, while two specific RIS architectures, namely true time delay-based RIS (TTD-RIS) and virtual subarray-based RIS (SA-RIS), are exploited to realize the frequency-dependent passive beamforming at the RIS. Furthermore, the efficient E2E beamforming models without explicit channel state information are proposed, which jointly exploits the uplink channel training module and the downlink wideband beamforming module. In the proposed network architecture of the E2E models, the classical communication signal processing methods, i.e., polarized filtering and sparsity transform, are leveraged to develop a signal-guided beamforming network. Numerical results show that the proposed E2E models have superior beamforming performance and robustness to conventional beamforming benchmarks. Furthermore, the tradeoff between the beamforming gain and the hardware complexity is investigated for different frequency-dependent RIS architectures, in which the TTD-RIS can achieve better spectral efficiency than the SA-RIS while requiring additional energy consumption and hardware cost.

cs.IT↗

arXiv2025

Aerial Active STAR-RIS-Aided IoT NOMA Networks

A novel framework of the unmanned aerial vehicle (UAV)-mounted active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) communications with the non-orthogonal multiple access (NOMA) is proposed for Internet-of-Things (IoT) networks. In particular, an active STAR-RIS is deployed onboard to enhance the communication link between the base station (BS) and the IoT devices, and NOMA is utilized for supporting the multi-device connectivity. Based on the proposed framework, a system sum rate maximization problem is formulated for the joint optimization of the active STAR-RIS beamforming, the UAV trajectory design, and the power allocation. To solve the non-convex problem with highly-coupled variables, an alternating optimization (AO) algorithm is proposed to decouple the original problem into three subproblems. Specifically, for the active STAR-RIS beamforming, the amplification coefficient, the power-splitting ratio, and the phase shift are incorporated into a combined variable to simplify the optimization process. Afterwards, the penalty-based method is invoked for handling the non-convex rank-one constraint. For the UAV trajectory design and the power allocation subproblems, the successive convex optimization method is applied for iteratively approximating the local-optimal solution. Numerical results demonstrate that: 1) the proposed algorithm achieves superior performance compared to the benchmarks in terms of the sum rate; and 2) the UAV-mounted active STAR-RIS can effectively enhance the channel gain from the BS to the IoT devices by the high-quality channel construction and the power compensation.

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arXiv2024

Exploiting Movable-Element STARS for Wireless Communications

A novel movable-element enabled simultaneously transmitting and reflecting surface (ME-STARS) communication system is proposed, where ME-STARS elements positions can be adjusted to enhance the degress-of-freedom for transmission and reflection. For each ME-STARS operating protocols, namely energy-splitting (ES), mode switching (MS), and time switching (TS), a weighted sum rate (WSR) maximization problem is formulated to jointly optimize the active beamforming at the base station (BS) as well as the elements positions and passive beamforming at the ME-STARS. An alternative optimization (AO)-based iterative algorithm is developed to decompose the original non-convex problem into three subproblems. Specifically, the gradient descent algorithm is employed for solving the ME-STARS element position optimization subproblem, and the weighted minimum mean square error and the successive convex approximation methods are invoked for solving the active and passive beamforming subproblems, respectively. It is further demonstrated that the proposed AO algorithm for ES can be extended to solve the problems for MS and TS. Numerical results unveil that: 1) the ME-STARS can significantly improve the WSR compared to the STARS with fixed position elements and the conventional reconfigurable intelligent surface with movable elements, thanks to the extra spatial-domain diversity and the higher flexibility in beamforming; and 2) the performance gain of ME-STARS is significant in the scenarios with larger number of users or more scatterers.

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arXiv2024

Unveiling the Potential of NOMA: A Journey to Next Generation Multiple Access

Revolutionary sixth-generation wireless communications technologies and applications, notably digital twin networks (DTN), connected autonomous vehicles (CAVs), space-air-ground integrated networks (SAGINs), zero-touch networks, industry 5.0, and healthcare 5.0, are driving next-generation wireless networks (NGWNs). These technologies generate massive data, requiring swift transmission and trillions of device connections, fueling the need for sophisticated next-generation multiple access (NGMA) schemes. NGMA enables massive connectivity in the 6G era, optimizing NGWN operations beyond current multiple access (MA) schemes. This survey showcases non-orthogonal multiple access (NOMA) as NGMA's frontrunner, exploring What has NOMA delivered?, What is NOMA providing?, and What lies ahead?. We present NOMA variants, fundamental operations, and applicability in multi-antenna systems, machine learning, reconfigurable intelligent surfaces (RIS), cognitive radio networks (CRN), integrated sensing and communications (ISAC), terahertz networks, and unmanned aerial vehicles (UAVs). Additionally, we explore NOMA's interplay with state-of-the-art wireless technologies, highlighting its advantages and technical challenges. Finally, we unveil NOMA research trends in the 6G era and provide design recommendations and future perspectives for NOMA as the leading NGMA solution for NGWNs.

cs.IT↗

arXiv2024

Local Features Meet Stochastic Anonymization: Revolutionizing Privacy-Preserving Face Recognition for Black-Box Models

The task of privacy-preserving face recognition (PPFR) currently faces two major unsolved challenges: (1) existing methods are typically effective only on specific face recognition models and struggle to generalize to black-box face recognition models; (2) current methods employ data-driven reversible representation encoding for privacy protection, making them susceptible to adversarial learning and reconstruction of the original image. We observe that face recognition models primarily rely on local features ({e.g., face contour, skin texture, and so on) for identification. Thus, by disrupting global features while enhancing local features, we achieve effective recognition even in black-box environments. Additionally, to prevent adversarial models from learning and reversing the anonymization process, we adopt an adversarial learning-based approach with irreversible stochastic injection to ensure the stochastic nature of the anonymization. Experimental results demonstrate that our method achieves an average recognition accuracy of 94.21\% on black-box models, outperforming existing methods in both privacy protection and anti-reconstruction capabilities.

cs.CV↗

arXiv2024

Joint Beamforming for STAR-RIS in Near-Field Communications

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided near-field multiple-input multiple-output (MIMO) communication framework is proposed. A weighted sum rate maximization problem for the joint optimization of the active beamforming at the base station (BS) and the transmission/reflection-coefficients (TRCs) at the STAR-RIS is formulated. The resulting non-convex problem is solved by the developed block coordinate descent (BCD)-based algorithm. Numerical results illustrate that the near-field beamforming for the STAR-RIS aided MIMO communications significantly improve the achieved weighted sum rate.

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arXiv2024

STAR-RIS Aided Integrated Sensing, Computing, and Communication for Internet of Robotic Things

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing, computing, and communication (ISCC) Internet of Robotic Things (IoRT) framework is proposed. Specifically, the full-duplex (FD) base station (BS) simultaneously receives the offloading signals from decision robots (DRs) and carries out target robot (TR) sensing. A computation rate maximization problem is formulated to optimize the sensing and receive beamformers at the BS and the STAR-RIS coefficients under the BS power constraint, the sensing signal-to-noise ratio constraint, and STAR-RIS coefficients constraints. The alternating optimization (AO) method is adopted to solve the proposed optimization problem. With fixed STAR-RIS coefficients, the sub-problem with respect to sensing and receiving beamformer at the BS is tackled with the weighted minimum mean-square error method. Given beamformers at the BS, the sub-problem with respect to STAR-RIS coefficients is tacked with the penalty method and successive convex approximation method. The overall algorithm is guaranteed to converge to at least a stationary point of the computation rate maximization problem. Our simulation results validate that the proposed STAR-RIS aided ISCC IoRT system can enhance the sum computation rate compared with the benchmark schemes.

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