pwc-archive/papers-with-abstracts huggingface dataset:
Secrecy Rate Maximization for 6G Reconfigurable Holographic Surfaces Assisted Systems
Authors: Chandan Kumar Sheemar, Wali Ullah Khan, Marco Di Renzo, Asad Mahmood, Jorge Querol, Symeon Chatzinotas
Date: 2025-03-03 00:00:00

Reconfigurable holographic surfaces (RHS) have emerged as a transformative material technology, enabling dynamic control of electromagnetic waves to generate versatile holographic beam patterns. This paper addresses the problem of secrecy rate maximization for an RHS-assisted systems by joint designing the digital beamforming, artificial noise (AN), and the analog holographic beamforming. However, such a problem results to be non-convex and challenging. Therefore, to solve it, a novel alternating optimization algorithm based on the majorization-maximization (MM) framework for RHS-assisted systems is proposed, which rely on surrogate functions to facilitate efficient and reliable optimization. In the proposed approach, digital beamforming design ensures directed signal power toward the legitimate user while minimizing leakage to the unintended receiver. The AN generation method projects noise into the null space of the legitimate user channel, aligning it with the unintended receiver channel to degrade its signal quality. Finally, the holographic beamforming weights are optimized to refine the wavefronts for enhanced secrecy rate performance Simulation results validate the effectiveness of the proposed framework, demonstrating significant improvements in secrecy rate compared to the benchmark method.
Uplink Transmission Design for Fluid Antenna-Enabled Multiuser MIMO Systems with Imperfect CSI
Authors: Linyue Hu, Luchu Li, Cunhua Pan, Hong Ren
Date: 2025-03-03 00:00:00
Tasks: Computational Efficiency

This paper investigates a two-timescale uplink transmission framework for a fluid antenna-enabled multiuser multi-input multi-output system (MIMO-FAS). Antenna positions are optimized based on statistical channel state information (CSI), while beamforming vectors at the base station (BS) adapt to instantaneous CSI. Under a Rician fading channel with imperfect CSI, we establish a linear minimum mean square error (LMMSE)-based channel estimation approach and derive a closed-form expression for the achievable uplink rate using a low-complexity maximal-ratio-combining (MRC) detector. The optimization problem is formulated as a minimum user rate maximization problem by optimizing the fluid antenna positions, subject to the feasible region and the minimum spacing distance constraints. To address this non-convex problem, a genetic algorithm (GA) method is proposed, encoding antenna configurations as population individuals. Additionally, an accelerated gradient ascent algorithm is proposed to enhance computational efficiency. Numerical results validate the mathematical derivations and demonstrate that the proposed two-timescale transmission strategy significantly outperforms traditional FPA systems, with both algorithms achieving enhanced gains.
Metering Error Estimation of Fast-Charging Stations Using Charging Data Analytics
Authors: Kang Ma, Xiulan Liu, Xi Chen, Xiaohu Liu, Wei Zhao, Lisha Peng, Songling Huang, Shisong Li
Date: 2025-03-03 00:00:00

Accurate electric energy metering (EEM) of fast charging stations (FCSs), serving as critical infrastructure in the electric vehicle (EV) industry and as significant carriers of vehicle-to-grid (V2G) technology, is the cornerstone for ensuring fair electric energy transactions. Traditional on-site verification methods, constrained by their high costs and low efficiency, struggle to keep pace with the rapid global expansion of FCSs. In response, this paper adopts a data-driven approach and proposes the measuring performance comparison (MPC) method. By utilizing the estimation value of state-of-charge (SOC) as a medium, MPC establishes comparison chains of EEM performance of multiple FCSs. Therefore, the estimation of EEM errors for FCSs with high efficiency is enabled. Moreover, this paper summarizes the interfering factors of estimation results and establishes corresponding error models and uncertainty models. Also, a method for discriminating whether there are EEM performance defects in FCSs is proposed. Finally, the feasibility of MPC method is validated, with results indicating that for FCSs with an accuracy grade of 2\%, the discriminative accuracy exceeds 95\%. The MPC provides a viable approach for the online monitoring of EEM performance for FCSs, laying a foundation for a fair and just electricity trading market.
Channel Semantic Characterization for Integrated Sensing and Communication Scenarios: From Measurements to Modeling
Authors: Zhengyu Zhang, Ruisi He, Bo Ai, Mi Yang, Xuejian Zhang, Ziyi Qi, Zhangdui Zhong
Date: 2025-03-03 00:00:00
Tasks: Integrated sensing and communication, ISAC

With the advancement of sixth-generation (6G) wireless communication systems, integrated sensing and communication (ISAC) is crucial for perceiving and interacting with the environment via electromagnetic propagation, termed channel semantics, to support tasks like decision-making. However, channel models focusing on physical characteristics face challenges in representing semantics embedded in the channel, thereby limiting the evaluation of ISAC systems. To tackle this, we present a novel framework for channel modeling from the conceptual event perspective. By leveraging a multi-level semantic structure and characterized knowledge libraries, the framework decomposes complex channel characteristics into extensible semantic characterization, thereby better capturing the relationship between environment and channel, and enabling more flexible adjustments of channel models for different events without requiring a complete reset. Specifically, we define channel semantics on three levels: status semantics, behavior semantics, and event semantics, corresponding to channel multipaths, channel time-varying trajectories, and channel topology, respectively. Taking realistic vehicular ISAC scenarios as an example, we perform semantic clustering, characterizing status semantics via multipath statistical distributions, modeling behavior semantics using Markov chains for time variation, and representing event semantics through a co-occurrence matrix. Results show the model accurately generates channels while capturing rich semantic information. Moreover, its generalization supports customized semantics.
Hybrid MIMO in the Upper Mid-Band: Architectures, Processing, and Energy Efficiency
Authors: Marouan Mizmizi, Ahmed Alkhateeb, Umberto Spagnolini
Date: 2025-03-03 00:00:00

As 6G networks evolve, the upper mid-band spectrum (7 GHz to 24 GHz), or frequency range 3 (FR3), is emerging as a promising balance between the coverage offered by sub-6 GHz bands and the high-capacity of millimeter wave (mmWave) frequencies. This paper explores the structure of FR3 hybrid MIMO systems and proposes two architectural classes: Frequency Integrated (FI) and Frequency Partitioned (FP). FI architectures enhance spectral efficiency by exploiting multiple sub-bands parallelism, while FP architectures dynamically allocate sub-band access according to specific application requirements. Additionally, two approaches, fully digital (FD) and hybrid analog-digital (HAD), are considered, comparing shared (SRF) versus dedicated RF (DRF) chain configurations. Herein signal processing solutions are investigated, particularly for an uplink multi-user scenario with power control optimization. Results demonstrate that SRF and DRF architectures achieve comparable spectral efficiency; however, SRF structures consume nearly half the power of DRF in the considered setup. While FD architectures provide higher spectral efficiency, they do so at the cost of increased power consumption compared to HAD. Additionally, FI architectures show slightly greater power consumption compared to FP; however, they provide a significant benefit in spectral efficiency (over 4 x), emphasizing an important trade-off in FR3 engineering.
Hierarchically Tunable 6DMA for Wireless Communication and Sensing: Modeling and Performance Optimization
Authors: Haocheng Hua, Yuyan Zhou, Weidong Mei, Jie Xu, Rui Zhang
Date: 2025-03-03 00:00:00
Tasks: Position

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.
RSSI Positioning with Fluid Antenna Systems
Authors: Wenzhi Liu, Zhisheng Rong, Xiayue Liu, Yufei Jiang, Xu Zhu
Date: 2025-03-02 00:00:00

We introduce a novel received signal strength intensity (RSSI)-based positioning method using fluid antenna systems (FAS), leveraging their inherent channel correlation properties to improve location accuracy. By enabling a single antenna to sample multiple spatial positions, FAS exhibits high correlation between its ports. We integrate this high inter-port correlation with a logarithmic path loss model to mitigate the impact of fast fading on RSSI signals, and derive a simplified multipoint positioning model based on the established relationship between channel correlation and RSSI signal correlation. A maximum likelihood estimator (MLE) is then developed, for which we provide a closed-form solution. Results demonstrate that our approach outperforms both traditional least squares (LS) methods and single-antenna systems, achieving accuracy comparable to conventional multi-antenna positioning. Furthermore, we analyze the impact of different antenna structures on positioning performance, offering practical guidance for FAS antenna design.
C2S-AE: CSI to Sensing enabled by an Auto-Encoder-based Framework
Authors: Jun Jiang, Shugong Xu, Wenjun Yu, Yuan Gao
Date: 2025-03-02 00:00:00
Tasks: Integrated sensing and communication, ISAC

Next-generation mobile networks are set to utilize integrated sensing and communication (ISAC) as a critical technology, providing significant support for sectors like the industrial Internet of Things (IIoT), extended reality (XR), and smart home applications. A key challenge in ISAC implementation is the extraction of sensing parameters from radio signals, a task that conventional methods struggle to achieve due to the complexity of acquiring sensing channel data. In this paper, we introduce a novel auto-encoder (AE)-based framework to acquire sensing information using channel state information (CSI). Specifically, our framework, termed C2S (CSI to sensing)-AE, learns the relationship between CSI and the delay power spectrum (DPS), from which the range information can be readily accessed. To validate our framework's performance, we conducted measurements of DPS and CSI in real-world scenarios and introduced the dataset 'SHU7'. Our extensive experiments demonstrate that the framework excels in C2S extrapolation, surpassing existing methods in terms of accuracy for both delay and signal strength of individual paths. This innovative approach holds the potential to greatly enhance sensing capabilities in future mobile networks, paving the way for more robust and versatile ISAC applications.
Confidence Based Asynchronous Integrated Communication and Localization Networks Using Pulsed UWB Signals
Authors: Fan Liu, Bofeng Zheng, Tingting Zhang, Qinyu Zhang
Date: 2025-03-02 00:00:00

In recent years, UWB has garnered widespread attention in academia and industry due to its low power consumption, wide bandwidth, and high time resolution characteristics. This paper introduces the design of an asynchronous IR-UWB integrated communication and localization (ICL) downlink network, which employs unified waveforms to enable simultaneous data transmission and localization. A differential sequential detection strategy has been proposed for data demodulation. To address errors caused by symbol misalignment, a novel symbol confidence metric model is introduced to ensure reliable pulse detection and time-of-arrival (TOA) estimation. Additionally, an asynchronous start-of-frame delimiter (SFD) detection model has been constructed to guide parameter optimization for practical applications. Furthermore, the clock drift estimation has been improved by leveraging the confidence metric within a modified weighted least squares (MWLS) framework. Simulation results demonstrate that the proposed system achieves reliable clock drift estimation, communication, and self-localization simultaneously. The operational range of the confidence metric required for these outcomes is also quantified, providing valuable insights for parameter design and system implementation. Finally, the agent localization accuracy can be achieved within 10 cm at over 90\% confidence, with commercial UWB devices according to practical measurements.
Towards Quantum SAGINs Harnessing Optical RISs: Applications, Advances, and the Road Ahead
Authors: Phuc V. Trinh, Shinya Sugiura, Chao Xu, Lajos Hanzo
Date: 2025-03-02 00:00:00

The space-air-ground integrated network (SAGIN) concept is vital for the development of seamless next-generation (NG) wireless coverage, integrating satellites, unmanned aerial vehicles, and manned aircraft along with the terrestrial infrastructure to provide resilient ubiquitous communications. By incorporating quantum communications using optical wireless signals, SAGIN is expected to support a synergistic global quantum Internet alongside classical networks. However, long-distance optical beam propagation requires line-of-sight (LoS) connections in the face of beam broadening and LoS blockages. To overcome blockages among SAGIN nodes, we propose deploying optical reconfigurable intelligent surfaces (ORISs) on building rooftops. They can also adaptively control optical beam diameters for reducing losses. This article first introduces the applications of ORISs in SAGINs, then examines their advances in quantum communications for typical SAGIN scenarios. Finally, the road ahead towards the practical realization of ORIS-aided NG quantum SAGINs is outlined.
An Approach of Directly Tracking Multiple Objects
Authors: Mingchao Liang, Florian Meyer
Date: 2025-03-02 00:00:00

In conventional approaches for multiobject tracking (MOT), raw sensor data undergoes several preprocessing stages to reduce data rate and computational complexity. This typically includes coherent processing that aims at maximizing the signal-to-noise ratio (SNR), followed by a detector that extracts "point" measurements, e.g., the range and bearing of objects, which serve as inputs for sequential Bayesian MOT. While using point measurements significantly simplifies the statistical model, the reduced data rate can lead to a loss of critical, object-related information and, thus, potentially to reduced tracking performance. In this paper, we propose a direct tracking approach that avoids a detector and most preprocessing stages. For direct tracking, we introduce a measurement model for the data-generating process of the sensor data, along with state-transition and birth models for the dynamics and the appearance and disappearance of objects. Based on the new statistical model, we develop a factor graph and particle-based belief propagation (BP) method for efficient sequential Bayesian estimation. Contrary to the track-before-detect (TBD) paradigm which also avoids a detector, direct tracking integrates coherent processing within the Bayesian MOT framework. Numerical experiments based on a passive acoustic dataset demonstrate that the proposed direct approach outperforms state-of-the-art conventional methods that rely on multiple preprocessing stages.
Net-Zero Integrated Sensing and Communication in Backscatter Systems
Authors: Yu Zhang, Tongyang Xu, Christos Masouros, Zhu Han
Date: 2025-03-01 00:00:00
Tasks: Integrated sensing and communication, ISAC, TAG

Future wireless networks targeted for improving spectral and energy efficiency, are expected to simultaneously provide sensing functionality and support low-power communications. This paper proposes a novel net-zero integrated sensing and communication (ISAC) model for backscatter systems, including an access point (AP), a net-zero device, and a user receiver. We fully utilize the backscatter mechanism for sensing and communication without additional power consumption and signal processing in the hardware device, which reduces the system complexity and makes it feasible for practical applications. To further optimize the system performance, we design a novel signal frame structure for the ISAC model that effectively mitigates communication interference at the transmitter, tag, and receiver. Additionally, we employ distributed antennas for sensing which can be placed flexibly to capture a wider range of signals from diverse angles and distances, thereby improving the accuracy of sensing. We derive theoretical expressions for the symbol error rate (SER) and tag's location detection probability, and provide a detailed analysis of how the system parameters, such as transmit power and tag's reflection coefficient, affect the system performance.
Predicting Cascading Failures in Power Systems using Machine Learning
Authors: Samita Rani Pani, Pallav Kumar Bera, Rajat Kanti Samal
Date: 2025-03-01 00:00:00
Tasks: Bayesian Optimization, Management, Multi-class Classification

Cascading failure studies help assess and enhance the robustness of power systems against severe power outages. Onset time is a critical parameter in the analysis and management of power system stability and reliability, representing the timeframe within which initial disturbances may lead to subsequent cascading failures. In this paper, different traditional machine learning algorithms are used to predict the onset time of cascading failures. The prediction task is articulated as a multi-class classification problem, employing machine learning algorithms. The results on the UIUC 150-Bus power system data available publicly show high classification accuracy with Random Forest. The hyperparameters of the Random Forest classifier are tuned using Bayesian Optimization. This study highlights the potential of machine learning models in predicting cascading failures, providing a foundation for the development of more resilient power systems.
Toward AIML Enabled WiFi Beamforming CSI Feedback Compression: An Overview of IEEE 802.11 Standardization
Authors: Ziming He
Date: 2025-03-01 00:00:00

Transmit beamforming is one of the key techniques used in the existing IEEE 802.11 WiFi standards and future generations such as 11be and 11bn, a.k.a., ultra high reliability (UHR). The paper gives an overview of the current standardization activities regarding the artificial intelligence and machine learning (AIML) enabled beamforming channel state information (CSI) feedback compression technique, defined by the 802.11 AIML topic interest group (TIG). Two key challenges the AIML TIG is going to tackle in the future beamforming standards and four defined key performance indicators (KPIs) for the AIML enabled schemes are discussed in the paper. The two challenges are the CSI feedback overhead and the compression complexity, and the four KPIs are feedback overhead, AIML model sharing overhead, packet error rate and complexity. Moreover, the paper presents a couple of AIML enabled compression schemes accepted by the TIG, such as the K-means and autoencoder based schemes, and uses simulated and analyzed data to explain how these schemes are designed according to the KPIs. Finally, future research directions are indicated for encouraging more researchers and engineers to contribute to this technique and the standardization of the next generation WiFi beamforming.
Stacked Intelligent Metasurfaces-Enhanced MIMO OFDM Wideband Communication Systems
Authors: Zheao Li, Jiancheng An, Chau Yuen
Date: 2025-03-01 00:00:00

Multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems rely on digital or hybrid digital and analog designs for beamforming against frequency-selective fading, which suffer from high hardware complexity and energy consumption. To address this, this work introduces a fully-analog stacked intelligent metasurfaces (SIM) architecture that directly performs wave-domain beamforming, enabling diagonalization of the end-to-end channel matrix and inherently eliminating inter-antenna interference (IAI) for MIMO OFDM transmission. By leveraging cascaded programmable metasurface layers, the proposed system establishes multiple parallel subchannels, significantly improving multi-carrier transmission efficiency while reducing hardware complexity. To optimize the SIM phase shift matrices, a block coordinate descent and penalty convex-concave procedure (BCD-PCCP) algorithm is developed to iteratively minimize the channel fitting error across subcarriers. Simulation results validate the proposed approach, determining the maximum effective bandwidth and demonstrating substantial performance improvements. Moreover, for a MIMO OFDM system operating at 28 GHz with 16 subcarriers, the proposed SIM configuration method achieves over 300\% enhancement in channel capacity compared to conventional SIM configuration that only accounts for the center frequency.
Advances in Anti-Deception Jamming Strategies for Radar Systems: A Survey
Authors: Helena Calatrava, Shuo Tang, Pau Closas
Date: 2025-03-01 00:00:00

Deception jamming has long been a significant threat to radar systems, interfering with search, acquisition, and tracking by introducing false information that diverts attention from the targets of interest. As deception strategies become more sophisticated, the vulnerability of radar systems to these attacks continues to escalate. This paper offers a comprehensive review of the evolution of anti-deception jamming techniques, starting with legacy solutions and progressing to the latest advancements. Current research is categorized into three key areas: prevention strategies, which hinder the ability of jammers to alter radar processing; detection strategies, which alert the system to deception and may classify the type of attack; and mitigation strategies, which aim to reduce or suppress the impact of jamming. Additionally, key avenues for further research are highlighted, with a particular emphasis on distributed, cognitive, and AI-enabled radar systems. We envision this paper as a gateway to the existing literature on anti-deception jamming, a critical area for safeguarding radar systems against evolving threats.