arXiv subjects

Wenwu Xie

Publications and source records attributed to Wenwu Xie.

6 recordsLinked to original sources

arXiv2026

Shift-MoE-Based DJSCC for CSI Feedback in Multi-User Pinching-Antenna Systems

In frequency-division duplexing systems, the performance gains of pinching-antenna systems (PASS) critically depend on accurate channel state information (CSI) at the base station. However, PASS CSI exhibits structured correlations over the waveguide-antenna grid and pronounced heterogeneity across users, making conventional fixed feedback mappings difficult to generalize. To address this challenge, this letter proposes an end-to-end CSI feedback scheme over a noisy uplink feedback link based on deep joint source-channel coding, termed Shift-based Mixture-of-Experts (Shift-MoE). Specifically, Shift-MoE leverages channel-grouped one-step shift operations to capture grid dependencies without global attention, and employs a gated multilayer perceptron mixture-of-experts module to adapt to heterogeneous CSI statistics across users. Numerical results demonstrate that the proposed Shift-MoE consistently outperforms representative learning-based CSI feedback baselines in normalized mean squared error and remains effective under different system parameter settings.

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arXiv2026

Recent Advances in Near-Field Beam Training and Channel Estimation for XL-MIMO Systems

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key technology for next-generation wireless communication systems. By deploying significantly more antennas than conventional massive MIMO systems, XL-MIMO promises substantial improvements in spectral efficiency. However, due to the drastically increased array size, the conventional planar wave channel model is no longer accurate, necessitating a transition to a near-field spherical wave model. This shift challenges traditional beam training and channel estimation methods, which were designed for planar wave propagation. In this article, we present a comprehensive review of state-of-the-art beam training and channel estimation techniques for XL-MIMO systems. We analyze the fundamental principles, key methodologies, and recent advancements in this area, highlighting their respective strengths and limitations in addressing the challenges posed by the near-field propagation environment. Furthermore, we explore open research challenges that remain unresolved to provide valuable insights for researchers and engineers working toward the development of next-generation XL-MIMO communication systems.

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arXiv2026

Joint Power Allocation and Antenna Placement for Pinching-Antenna Systems under User Location Uncertainty

Pinching antenna systems have attracted much attention recently owing to its capability to maintain reliable line-of-sight (LoS) communication in high-frequency bands. By guiding signals through a waveguide and emitting them via a movable pinching antenna, these systems enable dynamic control of signal propagation and spatial adaptability. However, their performance heavily depends on effective resource allocation-encompassing power, bandwidth, and antenna positioning-which becomes challenging under imperfect channel state information (CSI) and user localization uncertainty. Existing studies largely assume perfect CSI or ideal user positioning, while our prior work considered uniform localization errors, an oversimplified assumption. In this paper, we develop a robust resource allocation framework for multiuser downlink pinching antenna systems under Gaussian-distributed localization uncertainty, which more accurately models real-world positioning errors. An energy efficiency (EE) maximization problem is formulated subject to probabilistic outage constraints, and an analytical power allocation strategy is derived under given antenna positions. On this basis, the heuristic particle swarm optimization (PSO) algorithm is employed to identify the antenna position that achieves the global EE configuration. Simulation results illustrate that the proposed scheme greatly enhances both EE and system reliability compared with fixed-antenna benchmark, validating its effectiveness for practical high-frequency wireless deployments.

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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.

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arXiv2024

Double Self-Sustainable Reconfigurable Intelligent Surfaces Aided Wireless Communications

A double self-sustainable reconfigurable intelligent surfaces (RISs) assisted multi-user multiple input multiple output (MIMO) system is investigated. Two RISs are equipped with energy harvesting circuit to achieve self-sustainable transmission. The aim is to minimize the transmission power at the base station (BS), while guaranteeing the quality of service (QoS) requirements of the users and meeting the power consumption requirements of the RISs. A block coordinate descent (BCD) algorithm based on the penalty-based method and successive convex approximation (SCA) is employed to alternatively optimize the active beamforming at the BS and the phase shifts, as well as amplitude coefficients of two RISs. Simulation results show that the required power consumption at the BS for the proposed double self-sustainable RISs system is significantly reduced compared to conventional RIS systems.

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arXiv2020

Deep Learning-based Modulation Detection for NOMA Systems

Since the signal with strong power should be demodulated first for successive interference cancellation (SIC) demodulation in non-orthogonal multiple access (NOMA) systems, the base station (BS) should inform the near user terminal (UT), which has allocated higher power, of modulation mode of the far user terminal. To avoid unnecessary signaling overhead in this process, a blind detection algorithm of NOMA signal modulation mode is designed in this paper. Taking the joint constellation density diagrams of NOMA signal as the detection features, deep residual network is built for classification, so as to detect the modulation mode of NOMA signal. In view of the fact that the joint constellation diagrams are easily polluted by high intensity noise and lose their real distribution pattern, the wavelet denoising method is adopted to improve the quality of constellations. The simulation results represent that the proposed algorithm can achieve satisfactory detection accuracy in NOMA systems. In addition, the factors affecting the recognition performance are also verified and analyzed.

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