pwc-archive/papers-with-abstracts huggingface dataset:
GNN-based Precoder Design and Fine-tuning for Cell-free Massive MIMO with Real-world CSI
Authors: Tianzheng Miao, Thomas Feys, Gilles Callebaut, Jarne Van Mulders, Emanuele Peschiera, Md Arifur Rahman, François Rottenberg
Date: 2025-05-13 00:00:00
Tasks: Graph Neural Network, Transfer Learning

Cell-free massive MIMO (CF-mMIMO) has emerged as a promising paradigm for delivering uniformly high-quality coverage in future wireless networks. To address the inherent challenges of precoding in such distributed systems, recent studies have explored the use of graph neural network (GNN)-based methods, using their powerful representation capabilities. However, these approaches have predominantly been trained and validated on synthetic datasets, leaving their generalizability to real-world propagation environments largely unverified. In this work, we initially pre-train the GNN using simulated channel state information (CSI) data, which incorporates standard propagation models and small-scale Rayleigh fading. Subsequently, we finetune the model on real-world CSI measurements collected from a physical testbed equipped with distributed access points (APs). To balance the retention of pre-trained features with adaptation to real-world conditions, we adopt a layer-freezing strategy during fine-tuning, wherein several GNN layers are frozen and only the later layers remain trainable. Numerical results demonstrate that the fine-tuned GNN significantly outperforms the pre-trained model, achieving an approximate 8.2 bits per channel use gain at 20 dB signal-to-noise ratio (SNR), corresponding to a 15.7 % improvement. These findings highlight the critical role of transfer learning and underscore the potential of GNN-based precoding techniques to effectively generalize from synthetic to real-world wireless environments.
3GPP-Compliant Radar Cross Section Characterization of Indoor Factory Targets
Authors: Ali Waqar Azim, Ahmad Bazzi, Roberto Bomfin, Marwa Chafii
Date: 2025-05-13 00:00:00
Tasks: Integrated sensing and communication

The following paper presents a systematic 3rd Generation Partnership Project (3GPP)-compliant characterization of radar cross section (RCS) for indoor factory (InF) objects, including small and mid-sized unmanned aerial vehicles (UAVs), robotic arms, and automated guided vehicles (AGVs). Through measurements in the 25-28 GHz range, we validate the 3GPP standardized log-normal distribution model for RCS for above-mentioned target objects. The 3GPP-complaint RCS parameters obtained for the small-sized UAV are in close agreement (<1 dB deviation) with 3GPP agreed values. The mid-sized UAVs exhibit higher reflectivity compared to the small-sized UAV due to enhanced specular components attributed to material and lithium-ion battery packs. The robotic arm exhibits dynamic RCS behavior due to mechanical articulation, whereas UAVs show clear size-dependent reflectivity patterns in AGVs. Our findings provide empirical validation for RCS characterization for integrated sensing and communication channel modeling in InF environments.
Robust Beamforming Design for STAR-RIS Aided RSMA Network with Hardware Impairments
Authors: Ziyue Wang, Xiaoyan Ma, Xingyu Peng, Zheao Li, JinYuan Liu, Yongliang Guan, Chau Yuen
Date: 2025-05-13 00:00:00

In this article, we investigate the robust beamforming design for a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided downlink rate-splitting multiple access (RSMA) communication system, where both transceivers and STAR-RIS suffer from the impact of hardware impairments (HWI). A base station (BS) is deployed to transmit messages concurrently to multiple users, utilizing a STAR-RIS to improve communication quality and expand user coverage. We aim to maximize the achievable sum rate of the users while ensuring the constraints of transmit power, STAR-RIS coefficients, and the actual rate of the common stream for all users. To solve this challenging high-coupling and non-convexity problem, we adopt a fractional programming (FP)-based alternating optimization (AO) approach, where each sub-problem is addressed via semidefinite relaxation (SDR) and successive convex approximation (SCA) methods. Numerical results demonstrate that the proposed scheme outperforms other multiple access schemes and conventional passive RIS in terms of the achievable sum rate. Additionally, considering the HWI of the transceiver and STAR-RIS makes our algorithm more robust than when such considerations are not included.
Joint Optimization of User Association and Resource Allocation for Load Balancing With Multi-Level Fairness
Authors: Jonggyu Jang, Hyeonsu Lyu, David J. Love, Hyun Jong Yang
Date: 2025-05-13 00:00:00
Tasks: Fairness

User association, the problem of assigning each user device to a suitable base station, is increasingly crucial as wireless networks become denser and serve more users with diverse service demands. The joint optimization of user association and resource allocation (UARA) is a fundamental issue for future wireless networks, as it plays a pivotal role in enhancing overall network performance, user fairness, and resource efficiency. Given the latency-sensitive nature of emerging network applications, network management favors algorithms that are simple and computationally efficient rather than complex centralized approaches. Thus, distributed pricing-based strategies have gained prominence in the UARA literature, demonstrating practicality and effectiveness across various objective functions, e.g., sum-rate, proportional fairness, max-min fairness, and alpha-fairness. While the alpha-fairness frameworks allow for flexible adjustments between efficiency and fairness via a single parameter $\alpha$, existing works predominantly assume a homogeneous fairness context, assigning an identical $\alpha$ value to all users. Real-world networks, however, frequently require differentiated user prioritization due to varying application requirements and latency. To bridge this gap, we propose a novel heterogeneous alpha-fairness (HAF) objective function, assigning distinct {\alpha} values to different users, thereby providing enhanced control over the balance between throughput, fairness, and latency across the network. We present a distributed, pricing-based optimization approach utilizing an auxiliary variable framework and provide analytical proof of its convergence to an $\epsilon$-optimal solution, where the optimality gap $\epsilon$ decreases with the number of iterations.
Extract the Best, Discard the Rest: CSI Feedback with Offline Large AI Models
Authors: Jialin Zhuang, Yafei Wang, Hongwei Hou, Yu Han, Wenjin Wang, Shi Jin, Jiangzhou Wang
Date: 2025-05-13 00:00:00

Large AI models (LAMs) have shown strong potential in wireless communication tasks, but their practical deployment remains hindered by latency and computational constraints. In this work, we focus on the challenge of integrating LAMs into channel state information (CSI) feedback for frequency-division duplex (FDD) massive multiple-intput multiple-output (MIMO) systems. To this end, we propose two offline frameworks, namely site-specific LAM-enhanced CSI feedback (SSLCF) and multi-scenario LAM-enhanced CSI feedback (MSLCF), that incorporate LAMs into the codebook-based CSI feedback paradigm without requiring real-time inference. Specifically, SSLCF generates a site-specific enhanced codebook through fine-tuning on locally collected CSI data, while MSLCF improves generalization by pre-generating a set of environment-aware codebooks. Both of these frameworks build upon the LAM with vision-based backbone, which is pre-trained on large-scale image datasets and fine-tuned with CSI data to generate customized codebooks. This resulting network named LVM4CF captures the structural similarity between CSI and image, allowing the LAM to refine codewords tailored to the specific environments. To optimize the codebook refinement capability of LVM4CF under both single- and dual-side deployment modes, we further propose corresponding training and inference algorithms. Simulation results show that our frameworks significantly outperform existing schemes in both reconstruction accuracy and system throughput, without introducing additional inference latency or computational overhead. These results also support the core design methodology of our proposed frameworks, extracting the best and discarding the rest, as a promising pathway for integrating LAMs into future wireless systems.
Short Wins Long: Short Codes with Language Model Semantic Correction Outperform Long Codes
Authors: Jiafu Hao, Chentao Yue, Hao Chang, Branka Vucetic, Yonghui Li
Date: 2025-05-13 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence

This paper presents a novel semantic-enhanced decoding scheme for transmitting natural language sentences with multiple short block codes over noisy wireless channels. After ASCII source coding, the natural language sentence message is divided into segments, where each is encoded with short block channel codes independently before transmission. At the receiver, each short block of codewords is decoded in parallel, followed by a semantic error correction (SEC) model to reconstruct corrupted segments semantically. We design and train the SEC model based on Bidirectional and Auto-Regressive Transformers (BART). Simulations demonstrate that the proposed scheme can significantly outperform encoding the sentence with one conventional long LDPC code, in terms of block error rate (BLER), semantic metrics, and decoding latency. Finally, we proposed a semantic hybrid automatic repeat request (HARQ) scheme to further enhance the error performance, which selectively requests retransmission depends on semantic uncertainty.
SemCSINet: A Semantic-Aware CSI Feedback Network in Massive MIMO Systems
Authors: Ruonan Ren, Jianhua Mo, Meixia Tao
Date: 2025-05-13 00:00:00

Massive multiple-input multiple-output (MIMO) technology is a key enabler of modern wireless communication systems, which demand accurate downlink channel state information (CSI) for optimal performance. Although deep learning (DL) has shown great potential in improving CSI feedback, most existing approaches fail to exploit the semantic relationship between CSI and other related channel metrics. In this paper, we propose SemCSINet, a semantic-aware Transformer-based framework that incorporates Channel Quality Indicator (CQI) into the CSI feedback process. By embedding CQI information and leveraging a joint coding-modulation (JCM) scheme, SemCSINet enables efficient, digital-friendly CSI feedback under noisy feedback channels. Experimental results on DeepMIMO datasets show that SemCSINet significantly outperforms conventional methods, particularly in scenarios with low signal-to-noise ratio (SNR) and low compression ratios (CRs), highlighting the effectiveness of semantic embedding in enhancing CSI reconstruction accuracy and system robustness.
Multi-Active RIS-Assisted THz Cell-Free Systems: Spectral and Energy Efficiency Tradeoff
Authors: Mario R. Camana, Zaid Abdullah, Carla E. Garcia, Chandan Kumar Sheemar, Eva Lagunas, Symeon Chatzinotas
Date: 2025-05-13 00:00:00

Reconfigurable intelligent surfaces (RISs) and cell-free massive multiple-input multiple-output (CF-mMIMO) are effective solutions for mitigating large path loss and inter-cell interference in terahertz (THz) systems. However, passive RISs are notably limited from double-fading attenuation, motivating the use of active RISs with power amplification to improve signal strength. In this paper, we investigate a multi-active RIS-aided wideband CF-mMIMO system for THz communications, considering low-resolution digital-to-analog converters (DACs) to optimize the spectral efficiency (SE)-energy efficiency (EE) tradeoff by adjusting precoding vectors and reflection coefficient response of the RISs, subject to power and minimum desirable per-user rate constraints. This leads to a highly complex and non-convex, multi-objective and fractional optimization problem. To solve it, we propose a tailored quadratic transformation to manage the fractional form. This allows decomposition into two subproblems, which are iteratively solved via a successive convex approximation algorithm to optimize the precoding vectors and active RIS reflection coefficients until convergence. Numerical results demonstrate that the proposed active RIS-aided CF-mMIMO system effectively addresses propagation loss and limited scattering in THz communication, achieving superior EE and SE compared to conventional passive RIS across diverse scenarios. Furthermore, the integration of low-resolution DACs shows significant improvement in EE while preserving satisfactory communication performance.
Sparsity-Aware Near-Field Beam Training via Multi-Beam Combination
Authors: Zijun Wang, Rama Kiran, Jinesh Nair, Chien-Hua Chen, Tzu-Han Chou, Shawn Tsai, Rui Zhang
Date: 2025-05-13 00:00:00
Tasks: regression

This paper proposes an adaptive near-field beam training method to enhance performance in multi-user and multipath environments. The approach identifies multiple strongest beams through beam sweeping and linearly combines their received signals - capturing both amplitude and phase - for improved channel estimation. Two codebooks are considered: the conventional DFT codebook and a near-field codebook that samples both angular and distance domains. As the near-field basis functions are generally non-orthogonal and often over-complete, we exploit sparsity in the solution using LASSO-based linear regression, which can also suppress noise. Simulation results show that the near-field codebook reduces feedback overhead by up to 95% compared to the DFT codebook. The proposed LASSO regression method also maintains robustness under varying noise levels, particularly in low SNR regions. Furthermore, an off-grid refinement scheme is introduced to enhance accuracy especially when the codebook sampling is coarse, improving reconstruction accuracy by 69.4%.
N$^2$LoS: Single-Tag mmWave Backscatter for Robust Non-Line-of-Sight Localization
Authors: Zhenguo Shi, Yihe Yan, Yanxiang Wang, Wen Hu, Chun Tung Chou
Date: 2025-05-13 00:00:00
Tasks: Super-Resolution, TAG

The accuracy of traditional localization methods significantly degrades when the direct path between the wireless transmitter and the target is blocked or non-penetrable. This paper proposes N2LoS, a novel approach for precise non-line-of-sight (NLoS) localization using a single mmWave radar and a backscatter tag. N2LoS leverages multipath reflections from both the tag and surrounding reflectors to accurately estimate the targets position. N2LoS introduces several key innovations. First, we design HFD (Hybrid Frequency-Hopping and Direct Sequence Spread Spectrum) to detect and differentiate reflectors from the target. Second, we enhance signal-to-noise ratio (SNR) by exploiting the correlation properties of the designed signals, improving detection robustness in complex environments. Third, we propose FS-MUSIC (Frequency-Spatial Multiple Signal Classification), a super resolution algorithm that extends the traditional MUSIC method by constructing a higher-rank signal matrix, enabling the resolution of additional multipath components. We evaluate N2LoS using a 24 GHz mmWave radar with 250 MHz bandwidth in three diverse environments: a laboratory, an office, and an around-the-corner corridor. Experimental results demonstrate that N2LoS achieves median localization errors of 10.69 cm (X) and 11.98 cm (Y) at a 5 m range in the laboratory setting, showcasing its effectiveness for real-world NLoS localization.
Performance Analysis of Cooperative Integrated Sensing and Communications for 6G Networks
Authors: Dongsheng Sui, Cunhua Pan, Hong Ren, Jiahua Wan, Liuchang Zhuo, Jing Jin, Qixing Wang, Jiangzhou Wang
Date: 2025-05-13 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this work, we aim to effectively characterize the performance of cooperative integrated sensing and communication (ISAC) networks and to reveal how performance metrics relate to network parameters. To this end, we introduce a generalized stochastic geometry framework to model the cooperative ISAC networks, which approximates the spatial randomness of the network deployment. Based on this framework, we derive analytical expressions for key performance metrics in both communication and sensing domains, with a particular focus on communication coverage probability and radar information rate. The analytical expressions derived explicitly highlight how performance metrics depend on network parameters, thereby offering valuable insights into the deployment and design of cooperative ISAC networks. In the end, we validate the theoretical performance analysis through Monte Carlo simulation results. Our results demonstrate that increasing the number of cooperative base stations (BSs) significantly improves both metrics, while increasing the BS deployment density has a limited impact on communication coverage probability but substantially enhances the radar information rate. Additionally, increasing the number of transmit antennas is effective when the total number of transmit antennas is relatively small. The incremental performance gain reduces with the increase of the number of transmit antennas, suggesting that indiscriminately increasing antennas is not an efficient strategy to improve the performance of the system in cooperative ISAC networks.
Statistical CSI-Based Distributed Precoding Design for OFDM-Cooperative Multi-Satellite Systems
Authors: Yafei Wang, Vu Nguyen Ha, Konstantinos Ntontin, Hong Yan, Wenjin Wang, Symeon Chatzinotas, Björn Ottersten
Date: 2025-05-12 00:00:00

This paper investigates the design of distributed precoding for multi-satellite massive MIMO transmissions. We first conduct a detailed analysis of the transceiver model, in which delay and Doppler precompensation is introduced to ensure coherent transmission. In this analysis, we examine the impact of precompensation errors on the transmission model, emphasize the near-independence of inter-satellite interference, and ultimately derive the received signal model. Based on such signal model, we formulate an approximate expected rate maximization problem that considers both statistical channel state information (sCSI) and compensation errors. Unlike conventional approaches that recast such problems as weighted minimum mean square error (WMMSE) minimization, we demonstrate that this transformation fails to maintain equivalence in the considered scenario. To address this, we introduce an equivalent covariance decomposition-based WMMSE (CDWMMSE) formulation derived based on channel covariance matrix decomposition. Taking advantage of the channel characteristics, we develop a low-complexity decomposition method and propose an optimization algorithm. To further reduce computational complexity, we introduce a model-driven scalable deep learning (DL) approach that leverages the equivariance of the mapping from sCSI to the unknown variables in the optimal closed-form solution, enhancing performance through novel dense Transformer network and scaling-invariant loss function design. Simulation results validate the effectiveness and robustness of the proposed method in some practical scenarios. We also demonstrate that the DL approach can adapt to dynamic settings with varying numbers of users and satellites.
Near-Field Beamfocusing, Localization, and Channel Estimation with Modular Linear Arrays
Authors: Alva Kosasih, Özlem Tuğfe Demir, Emil Björnson
Date: 2025-05-12 00:00:00

This paper investigates how near-field beamfocusing can be achieved using a modular linear array (MLA), composed of multiple widely spaced uniform linear arrays (ULAs). The MLA architecture extends the aperture length of a standard ULA without adding additional antennas, thereby enabling near-field beamfocusing without increasing processing complexity. Unlike conventional far-field beamforming, near-field beamfocusing enables simultaneous data transmission to multiple users at different distances in the same angular interval, offering significant multiplexing gains. We present a detailed mathematical analysis of the beamwidth and beamdepth achievable with the MLA and show that by appropriately selecting the number of antennas in each constituent ULA, ideal near-field beamfocusing can be realized. In addition, we propose a computationally efficient localization method that fuses estimates from each ULA, enabling efficient parametric channel estimation. Simulation results confirm the accuracy of the analytical expressions and that MLAs achieve near-field beamfocusing with a limited number of antennas, making them a promising solution for next-generation wireless systems.
When Near Becomes Far: From Rayleigh to Optimal Near-Field and Far-Field Boundaries
Authors: Sajad Daei, Gabor Fodor, Mikael Skoglund
Date: 2025-05-12 00:00:00

The transition toward 6G is pushing wireless communication into a regime where the classical plane-wave assumption no longer holds. Millimeter-wave and sub-THz frequencies shrink wavelengths to millimeters, while meter-scale arrays featuring hundreds of antenna elements dramatically enlarge the aperture. Together, these trends collapse the classical Rayleigh far-field boundary from kilometers to mere single-digit meters. Consequently, most practical 6G indoor, vehicular, and industrial deployments will inherently operate within the radiating near-field, where reliance on the plane-wave approximation leads to severe array-gain losses, degraded localization accuracy, and excessive pilot overhead. This paper re-examines the fundamental question: Where does the far-field truly begin? Rather than adopting purely geometric definitions, we introduce an application-oriented approach based on user-defined error budgets and a rigorous Fresnel-zone analysis that fully accounts for both amplitude and phase curvature. We propose three practical mismatch metrics: worst-case element mismatch, worst-case normalized mean square error, and spectral efficiency loss. For each metric, we derive a provably optimal transition distance--the minimal range beyond which mismatch permanently remains below a given tolerance--and provide closed-form solutions. Extensive numerical evaluations across diverse frequencies and antenna-array dimensions show that our proposed thresholds can exceed the Rayleigh distance by more than an order of magnitude. By transforming the near-field from a design nuisance into a precise, quantifiable tool, our results provide a clear roadmap for enabling reliable and resource-efficient near-field communications and sensing in emerging 6G systems.
Channel Estimation for Wideband XL-MIMO: A Constrained Deep Unrolling Approach
Authors: Peicong Zheng, Xuantao Lyu, Ye Wang, Yi Gong
Date: 2025-05-12 00:00:00

Extremely large-scale multiple-input multiple-output (XL-MIMO) enables the formation of narrow beams, effectively mitigating path loss in high-frequency communications. This capability makes the integration of wideband high-frequency communications and XL-MIMO a key enabler for future 6G networks. Realizing the full potential of such wideband XL-MIMO systems depends critically on acquiring accurate channel state information. However, channel estimation is significantly challenging due to inherent wideband XL-MIMO channel characteristics, including near-field propagation, beam split, and spatial non-stationarity. To effectively capture these channel characteristics, we formulate channel estimation as a maximum a posteriori problem, which facilitates the use of prior channel knowledge. We then propose an unrolled proximal gradient descent algorithm with learnable step sizes, which employs a dedicated neural network for proximal mapping. This design empowers the proposed algorithm to implicitly learn prior channel knowledge directly from data, thereby eliminating the need for explicit regularization functions. To improve the convergence, we introduce a monotonic descent constraint on the layer-wise estimation error and provide theoretical analyses to characterize the algorithm's convergence behavior. Simulation results show that the proposed unrolling-based algorithm outperforms the traditional and deep learning-based methods.