pwc-archive/papers-with-abstracts huggingface dataset:
Performance Analysis of BEM-based Channel Estimation for OTFS with Hardware Impairments
Authors: Haowei Wu, Huanyu Chen, Qihao Peng, Qu Luo, Jinglan Ou
Date: 2025-02-06 00:00:00

This letter studies the low-complexity channel estimation for orthogonal time frequency space (OTFS) in the presence of hardware impairments. Firstly, to tackle the computational complexity of channel estimation, the basis expansion model (BEM) is utilized. Then, the mean square error (MSE) of the estimated channel is theoretically derived, revealing the effects of hardware impairments on channel estimation. Based on the estimated channel, the minimum mean square error (MMSE) detector is adopted to analyze the impacts of imperfect hardware on the bit error rate (BER). Finally, the numerical results validate the correctness of our theoretical analysis of the MSE for channel estimation and lower bound of the BER, and also demonstrate that even minor hardware impairments can significantly degrade the performance of the OTFS system.
Pre-Optimized Irregular Arrays versus Moveable Antennas in Multi-User MIMO Systems
Authors: Amna Irshad, Alva Kosasih, Vitaly Petrov, Emil Björnson
Date: 2025-02-06 00:00:00
Tasks: Diversity

Massive multiple-input multiple-output (MIMO) systems exploit the spatial diversity achieved with an array of many antennas to perform spatial multiplexing of many users. Similar performance can be achieved using fewer antennas if movable antenna (MA) elements are used instead. MA-enabled arrays can dynamically change the antenna locations, mechanically or electrically, to achieve maximum spatial diversity for the current propagation conditions. However, optimizing the antenna locations for each channel realization is computationally excessive, requires channel knowledge for all conceivable locations, and requires rapid antenna movements, thus making real-time implementation cumbersome. To overcome these challenges, we propose a pre-optimized irregular array (PIA) concept, where the antenna locations at the base station are optimized a priori for a given coverage area. The objective is to maximize the average sum rate and we take a particle swarm optimization approach to solve it. Simulation results show that PIA achieves performance comparable to MA-enabled arrays while outperforming traditional uniform arrays. Hence, PIA offers a fixed yet efficient array deployment approach without the complexities associated with MA-enabled arrays.
Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels
Authors: Shuai Ma, Zhiye Sun, Bin Shen, Youlong Wu, Hang Li, Guangming Shi, Shiyin Li, Naofal Al-Dhahir
Date: 2025-02-06 00:00:00
Tasks: Data Compression, Image Reconstruction

In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multi-user interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multi-user interference. More importantly, SFDMA can protect the privacy of users' semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta-Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme.
ICGNN: Graph Neural Network Enabled Scalable Beamforming for MISO Interference Channels
Authors: Changpeng He, Yang Lu, Bo Ai, Octavia A. Dobre, Zhiguo Ding, Dusit Niyato
Date: 2025-02-06 00:00:00
Tasks: Graph Neural Network, Transfer Learning

This paper investigates the graph neural network (GNN)-enabled beamforming design for interference channels. We propose a model termed interference channel GNN (ICGNN) to solve a quality-of-service constrained energy efficiency maximization problem. The ICGNN is two-stage, where the direction and power parts of beamforming vectors are learned separately but trained jointly via unsupervised learning. By formulating the dimensionality of features independent of the transceiver pairs, the ICGNN is scalable with the number of transceiver pairs. Besides, to improve the performance of the ICGNN, the hybrid maximum ratio transmission and zero-forcing scheme reduces the output ports, the feature enhancement module unifies the two types of links into one type, the subgraph representation enhances the message passing efficiency, and the multi-head attention and residual connection facilitate the feature extracting. Furthermore, we present the over-the-air distributed implementation of the ICGNN. Ablation studies validate the effectiveness of key components in the ICGNN. Numerical results also demonstrate the capability of ICGNN in achieving near-optimal performance with an average inference time less than 0.1 ms. The scalability of ICGNN for unseen problem sizes is evaluated and enhanced by transfer learning with limited fine-tuning cost. The results of the centralized and distributed implementations of ICGNN are illustrated.
SWIPTNet: A Unified Deep Learning Framework for SWIPT based on GNN and Transfer Learning
Authors: Hong Han, Yang Lu, Zihan Song, Ruichen Zhang, Wei Chen, Bo Ai, Dusit Niyato, Dong In Kim
Date: 2025-02-06 00:00:00
Tasks: Graph Neural Network, Transfer Learning

This paper investigates the deep learning based approaches for simultaneous wireless information and power transfer (SWIPT). The quality-of-service (QoS) constrained sum-rate maximization problems are, respectively, formulated for power-splitting (PS) receivers and time-switching (TS) receivers and solved by a unified graph neural network (GNN) based model termed SWIPT net (SWIPTNet). To improve the performance of SWIPTNet, we first propose a single-type output method to reduce the learning complexity and facilitate the satisfaction of QoS constraints, and then, utilize the Laplace transform to enhance input features with the structural information. Besides, we adopt the multi-head attention and layer connection to enhance feature extracting. Furthermore, we present the implementation of transfer learning to the SWIPTNet between PS and TS receivers. Ablation studies show the effectiveness of key components in the SWIPTNet. Numerical results also demonstrate the capability of SWIPTNet in achieving near-optimal performance with millisecond-level inference speed which is much faster than the traditional optimization algorithms. We also show the effectiveness of transfer learning via fast convergence and expressive capability improvement.
Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach
Authors: Changpeng He, Yang Lu, Wei Chen, Bo Ai, Kai-Kit Wong, Dusit Niyato
Date: 2025-02-06 00:00:00
Tasks: Graph Neural Network

An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the transmit design. This paper proposes an ``unsupervised learning to optimize" paradigm to optimize the FAS. Particularly, we formulate the sum-rate and energy efficiency (EE) maximization problems for a multiple-user multiple-input single-output (MU-MISO) FAS and solved by a two-stage graph neural network (GNN) where the first stage and the second stage are for the inference of antenna positions and beamforming vectors, respectively. The outputs of the two stages are jointly input into a unsupervised loss function to train the two-stage GNN. The numerical results demonstrates that the advantages of the FAS for performance improvement and the two-stage GNN for real-time and scalable optimization. Besides, the two stages can function separately.
Compact Nested Hexagonal Metamaterial Sensor for High-Sensitivity Permittivity Characterization Across S and X-Band Frequencies
Authors: Md Mujahid Hossain, Saif Hannan
Date: 2025-02-06 00:00:00
Tasks: Sensitivity

This article presents a Compact Nested Hexagonal Metamaterial Sensor designed for microwave sensing to characterize material permittivity in S and X-band applications. The proposed sensor attained compact dimensions of merely 30 mm x 30 mm x 0.79 mm. This innovative design technique employs a distinctive and compact architecture with elevated electromagnetic (EM) field strength, enhancing the precision of the sensing mechanism in the microwave frequency spectrum. The design geometry and dimensions attained resonance frequencies of 3.98 GHz and 11.57 GHz, with notch depths of -13.16129 dB and -10.23024 dB, respectively. The design evolution, metamaterial properties, equivalent circuit model, and electric (E) is delineated to elucidate the stopband features at the resonant frequency. The suggested sensor attains a very high sensitivity of 9.55% in transmission mode (S21) for a permittivity range of 1 to 6. The reflection and transmission properties of the proposed CRR-based sensor are validated by simulations using the mathematical equations of the design. Furthermore, the sensor's performance is corroborated by utilizing several dielectric materials (Roger R04350B, Roger RT5880 and FR-4). The computed outcomes demonstrate alignment with the simulated results. The compact, low-profile sensor design and its excellent sensitivity for characterizing material permittivity render the suggested sensor appropriate for permittivity sensing applications.
UAV Cognitive Semantic Communications Enabled by Knowledge Graph for Robust Object Detection
Authors: Xi Song, Fuhui Zhou, Rui Ding, Zhibo Qu, Yihao Li, Qihui Wu, Naofal Al-Dhahir
Date: 2025-02-06 00:00:00
Tasks: Object, object-detection, Object Detection, Robust Object Detection, Semantic Communication, Semantic Compression

Unmanned aerial vehicles (UAVs) are widely used for object detection. However, the existing UAV-based object detection systems are subject to severe challenges, namely, their limited computation, energy and communication resources, which limits the achievable detection performance. To overcome these challenges, a UAV cognitive semantic communication system is proposed by exploiting a knowledge graph. Moreover, we design a multi-scale codec for semantic compression to reduce data transmission volume while guaranteeing detection performance. Considering the complexity and dynamicity of UAV communication scenarios, a signal-to-noise ratio (SNR) adaptive module with robust channel adaptation capability is introduced. Furthermore, an object detection scheme is proposed by exploiting the knowledge graph to overcome channel noise interference and compression distortion. Simulation results conducted on the practical aerial image dataset demonstrate that our proposed semantic communication system outperforms benchmark systems in terms of detection accuracy, communication robustness, and computation efficiency, especially in dealing with low bandwidth compression ratios and low SNR regimes.
On the Simulation and Correlation Properties of TWDP Fading Process
Authors: Almir Maric, Pamela Njemcevic
Date: 2025-02-05 00:00:00

This paper introduces a novel statistical simulator designed to model propagation in two-way diffuse power (TWDP) fading channels. The simulator employs two zero-mean stochastic sinusoids to simulate specular components, while a sum of sinusoids is used to model the diffuse one. Using the developed simulator, the autocorrelation and cross-correlation functions of the quadrature components, as well as the autocorrelation of the complex and squared envelope, are derived for the first time in literature for channels experiencing TWDP fading. The statistical properties of the proposed simulator are thoroughly validated through extensive simulations, which closely align with the theoretical results.
On the Conditional Phase Distribution of the TWDP Multipath Fading Process
Authors: Almir Maric, Pamela Njemcevic
Date: 2025-02-05 00:00:00

In this paper, the conditional phase distribution of the two-wave with diffuse power (TWDP) process is derived as a closed-form and as an infinite-series expression. For the obtained infinite series expression, a truncation analysis is performed and the truncated expression is used to examine the influence of different channel conditions on the behavior of the TWDP phase. All the results are verified through Monte Carlo simulations.
ScNeuGM: Scalable Neural Graph Modeling for Coloring-Based Contention and Interference Management in Wi-Fi 7
Authors: Zhouyou Gu, Jihong Park, Jinho Choi
Date: 2025-02-05 00:00:00
Tasks: Collision Avoidance, Deep Hashing, Management

Carrier-sense multiple access with collision avoidance in Wi-Fi often leads to contention and interference, thereby increasing packet losses. These challenges have traditionally been modeled as a graph, with stations (STAs) represented as vertices and contention or interference as edges. Graph coloring assigns orthogonal transmission slots to STAs, managing contention and interference, e.g., using the restricted target wake time (RTWT) mechanism introduced in Wi-Fi 7 standards. However, legacy graph models lack flexibility in optimizing these assignments, often failing to minimize slot usage while maintaining reliable transmissions. To address this issue, we propose ScNeuGM, a neural graph modeling (NGM) framework that flexibly trains a neural network (NN) to construct optimal graph models whose coloring corresponds to optimal slot assignments. ScNeuGM is highly scalable to large Wi-Fi networks with massive STA pairs: 1) it utilizes an evolution strategy (ES) to directly optimize the NN parameters based on one network-wise reward signal, avoiding exhaustive edge-wise feedback estimations in all STA pairs; 2) ScNeuGM also leverages a deep hashing function (DHF) to group contending or interfering STA pairs and restricts NGM NN training and inference to pairs within these groups, significantly reducing complexity. Simulations show that the ES-trained NN in ScNeuGM returns near-optimal graphs 4-10 times more often than algorithms requiring edge-wise feedback and reduces 25\% slots than legacy graph constructions. Furthermore, the DHF in ScNeuGM reduces the training and the inference time of NGM by 4 and 8 times, respectively, and the online slot assignment time by 3 times in large networks, and up to 30\% fewer packet losses in dynamic scenarios due to the timely assignments.
Single Antenna Terahertz Sensing using Preconfigured Metasurfaces
Authors: Furkan Ilgac, Aydin Sezgin
Date: 2025-02-05 00:00:00

The development of mobile terahertz (THz) sensing and localization with minimal infrastructure has garnered significant attention due to its substantial practical implications. Single-antenna radar systems are a favored choice for mobile platforms, as they offer notable advantages in terms of cost, weight, and simplicity. However, these systems face a critical limitation: the inability to extract angular information using a single antenna, which consequently prevents the achievement of complete localization. This paper proposes an angular estimation method for a single-antenna radar augmented with a pair of preconfigured metasurfaces. The metasurface pair is used for creating an interference pattern in the scene, which depends on the target angles and operating frequency. Moreover, the beam squint effects caused by the wide frequency range in the THz band provides suitable conditions for using sparse reconstruction techniques to obtain angular estimates. We utilize these properties to perform angular estimation with a single antenna. The simulation results show that with this method it is possible to perform fast and accurate multi-target estimation for a broad operating range.
Chirp-Permuted AFDM for Quantum-Resilient Physical-Layer Secure Communications
Authors: Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu
Date: 2025-02-05 00:00:00

We present a novel physical-layer secure communications scheme based on a chirp-permuted variant of the affine frequency division multiplexing (AFDM) waveform recently proposed for sixth-generation (6G) systems, which ensures that eavesdroppers unaware of the correct chirp-permutation face significant challenges in signal detection, even with perfect channel state information (CSI) and co-location with the legitimate user. The security of the proposed scheme is studied in terms of the complexity required to find the correct permutation via classical and quantum search algorithms, analytically shown to be infeasible in both cases due to the factorially-scaling search space, and the probability of breach under the random-guess approach, also shown to be negligible.
A Data Pilot-Aided Temporal Convolutional Network for Channel Estimation in IEEE 802.11p Vehicle-to-Vehicle Communications
Authors: Simbarashe Aldrin Ngorima, Albert Helberg, Marelie H. Davel
Date: 2025-02-05 00:00:00

In modern communication systems, having an accurate channel estimator is crucial. However, when there is mobility, it becomes difficult to estimate the channel and the pilot signals, which are used for channel estimation, become insufficient. In this paper, we introduce the use of Temporal Convolutional Networks (TCNs) with data pilot-aided (DPA) channel estimation and temporal averaging (TA) to estimate vehicle-to-vehicle same direction with Wall (VTV-SDWW) channels. The TCN-DPA-TA estimator showed an improvement in Bit Error Rate (BER) performance of up to 1 order of magnitude. Furthermore, the BER performance of the TCN-DPA without TA also improved by up to 0.7 magnitude compared to the best classical estimator.
Hybrid Near-Field and Far-Field Localization with Multiple Holographic MIMO Surfaces
Authors: Mengyuan Cao
Date: 2025-02-05 00:00:00

Localization methods based on holographic multiple input multiple output (HMIMO) have gained much attention for its potential to achieve high accuracy. By deploying multiple HMIMOs, we can improve the link quality and system coverage. As the scale of HMIMO increases to improve beam control capability, the near-field (NF) region of each HMIMO expands. However, existing multiple HMIMO-enabled methods mainly focus on the far-field (FF) of each HMIMO, which leads to low localization accuracy when applied in the NF. In this paper, a hybrid NF and FF localization method aided by multiple RISs, a low cost implementation of HMIMO, is proposed. In such a scenario, it is difficult to achieve user localization and RIS optimization since the equivalent NF of all RISs expands, which results in high complexity, and we need to handle the interference caused by multiple RISs. To tackle this challenge, we propose a two-phase RIS-enabled localization method that first estimate the relative locations of the user to each RIS and fuse the results to obtain the global estimation. In this way, the algorithm complexity is reduced. We formulate the RIS optimization problem to keep the RIS sidelobe as low as possible to minimize the interference. The effectiveness of the proposed method is verified through simulations.
Antenna Position Optimization for Movable Antenna-Empowered Near-Field Sensing
Authors: Yushen Wang, Weidong Mei, Xin Wei, Boyu Ning, Zhi Chen
Date: 2025-02-05 00:00:00
Tasks: Position

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.
Dual-Polarized Intelligent Omni-Surfaces for Independent Reflective-Refractive Transmission
Authors: Zizhou Zheng
Date: 2025-02-05 00:00:00

Intelligent omni-surface (IOS), which are capable of providing service coverage to mobile users (MUs) in a reflective and a refractive manner, has recently attracted widespread attention. However, the performance of traditionally IOS-aid systems is limited by the intimate coupling between the refraction and reflection behavior of IOS elements. In this letter, to overcome this challenge, we introduce the concept of dual-polarized IOS-assisted communication. More precisely, by employing the polarization domain in the design of IOS, full independent refraction and reflection modes can be delivered. We consider a downlink dual-polarized IOS-aided system, while also accounting for the leakage between different polarizations. To maximize the sum rate, we formulate a joint IOS phase shift and BS beamforming problem and proposed an iterative algorithm to solve the non-convex program. Simulation results validate that dual-polarized IOS significantly enhances the performance than the traditional one.
Perfect matching of reactive loads through complex frequencies: from circuital analysis to experiments
Authors: Angelica V. Marini, Davide Ramaccia, Alessandro Toscano, Filiberto Bilotti
Date: 2025-02-05 00:00:00

The experimental evidence of purely reactive loads impedance matching is here provided by exploiting the special scattering response under complex excitations. The study starts with a theoretical analysis of the reflection properties of an arbitrary reactive load and identifies the proper excitation able to transform the purely reactive load into a virtual resistive load during the time the signal is applied. To minimize reflections between the load and the transmission line, the excitation must have a complex frequency, leading to a propagating signal with a tailored temporal envelope. The aim of this work is to design and, for the first time,experimentally demonstrate this anomalous scattering behavior in microwave circuits, showing that the time-modulated signals can be exploited as a new degree of freedom for achieving impedance matching without introducing neither a matching network nor resistive elements, that are typically used for ensuring power dissipation and, thus, zero reflection. The proposed matching strategy does not alter the reactive load that is still lossless, enabling an anomalous termination condition where the energy is not dissipated nor reflected, but indefinitely accumulated in the reactive load. The stored energy leaks out the load as soon as the applied signal changes or stops.