pwc-archive/papers-with-abstracts huggingface dataset:
RIS-Aided Integrated Sensing and Communication Waveform Design With Tunable PAPR
Authors: Jinlong Wu, Lixin Li, Wensheng Lin, Wei Liang, Decan Zhao, Zhu Han
Date: 2025-04-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

Low peak-to-average power ratio (PAPR) transmission is an important and favorable requirement prevalent in radar and communication systems, especially in transmission links integrated with high power amplifiers. Meanwhile, motivated by the advantages of reconfigurable intelligent surface (RIS) in mitigating multi-user interference (MUI) to enhance the communication rate, this paper investigates the design problem of joint waveform and passive beamforming with PAPR constraint for integrated sensing and communication (ISAC) systems, where RIS is deployed for downlink communication. We first construct a trade-off optimization problem for the MUI and beampattern similarity under PAPR constraint. Then, in order to solve this multivariate problem, an iterative optimization algorithm based on alternating direction method of multipliers (ADMM) and manifold optimization is proposed. Finally, the simulation results show that the designed waveforms can well satisfy the PAPR requirement of the ISAC systems and achieve a trade-off between radar and communication performance. Under high signal-to-noise ratio (SNR) conditions, compared to systems without RIS, RIS-aided ISAC systems have a performance improvement of about 50\% in communication rate and at least 1 dB in beampatterning error.
Improved AFSA-Based Beam Training Without CSI for RIS-Assisted ISAC Systems
Authors: Yunxiang Shi, Lixin Li, Wensheng Lin, Wei Liang, Zhu Han
Date: 2025-04-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this paper, we consider transmit beamforming and reflection patterns design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems, where the dual-function base station (DFBS) lacks channel state information (CSI). To address the high overhead of cascaded channel estimation, we propose an improved artificial fish swarm algorithm (AFSA) combined with a feedback-based joint active and passive beam training scheme. In this approach, we consider the interference caused by multipath user echo signals on target detection and propose a beamforming design method that balances both communication and sensing performance. Numerical simulations show that the proposed AFSA outperforms other optimization algorithms, particularly in its robustness against echo interference under different communication signal-to-noise ratio (SNR) constraints.
DS-Pnet: FM-Based Positioning via Downsampling
Authors: Shilian Zheng, Xinjiang Qiu, Luxin Zhang, Quan Lin, Zhijin Zhao, Xiaoniu Yang
Date: 2025-04-10 00:00:00

In this paper we present DS-Pnet, a novel framework for FM signal-based positioning that addresses the challenges of high computational complexity and limited deployment in resource-constrained environments. Two downsampling methods-IQ signal downsampling and time-frequency representation downsampling-are proposed to reduce data dimensionality while preserving critical positioning features. By integrating with the lightweight MobileViT-XS neural network, the framework achieves high positioning accuracy with significantly reduced computational demands. Experiments on real-world FM signal datasets demonstrate that DS-Pnet achieves superior performance in both indoor and outdoor scenarios, with space and time complexity reductions of approximately 87% and 99.5%, respectively, compared to an existing method, FM-Pnet. Despite the high compression, DS-Pnet maintains robust positioning accuracy, offering an optimal balance between efficiency and precision.
Deep Learning-Based Wideband Spectrum Sensing with Dual-Representation Inputs and Subband Shuffling Augmentation
Authors: Shilian Zheng, Zhihao Ye, Luxin Zhang, Keqiang Yue, Zhijin Zhao
Date: 2025-04-10 00:00:00
Tasks: Data Augmentation

The widespread adoption of mobile communication technology has led to a severe shortage of spectrum resources, driving the development of cognitive radio technologies aimed at improving spectrum utilization, with spectrum sensing being the key enabler. This paper presents a novel deep learning-based wideband spectrum sensing framework that leverages multi-taper power spectral inputs to achieve high-precision and sample-efficient sensing. To enhance sensing accuracy, we incorporate a feature fusion strategy that combines multiple power spectrum representations. To tackle the challenge of limited sample sizes, we propose two data augmentation techniques designed to expand the training set and improve the network's detection probability. Comprehensive simulation results demonstrate that our method outperforms existing approaches, particularly in low signal-to-noise ratio conditions, achieving higher detection probabilities and lower false alarm rates. The method also exhibits strong robustness across various scenarios, highlighting its significant potential for practical applications in wireless communication systems.
Secure Directional Modulation with Movable Antenna Array Aided by RIS
Authors: Maolin Li, Jingdie Xin, Feng Shu, Xuehui Wang, Yongpeng Wu, Cunhua Pan
Date: 2025-04-10 00:00:00
Tasks: Compressive Sensing, Position

In this paper, to fully exploit the performance gains from moveable antennas (MAs) and reconfigurable intelligent surface (RIS), a RIS-aided directional modulation \textcolor{blue}{(DM)} network with movable antenna at base station (BS) is established Based on the principle of DM, a BS equipped with MAs transmits legitimate information to a single-antenna user (Bob) while exploiting artificial noise (AN) to degrade signal reception at the eavesdropper (Eve). The combination of AN and transmission beamforming vectors is modeled as joint beamforming vector (JBV) to achieve optimal power allocation. The objective is to maximize the achievable secrecy rate (SR) by optimizing MAs antenna position, phase shift matrix (PSM) of RIS, and JBV. The limited movable range (MR) and discrete candidate positions of the MAs at the BS are considered, which renders the optimization problem non-convex. To address these challenges, an optimization method under perfect channel state information (CSI) is firstly designed, in which the MAs antenna positions are obtained using compressive sensing (CS) technology, and JBV and PSM are iteratively optimized. Then, the design method and SR performance under imperfect CSI is investigated. The proposed algorithms have fewer iterations and lower complexity. Simulation results demonstrate that MAs outperform fixed-position antennas in SR performance when there is an adequately large MR available.
WK-Pnet: FM-Based Positioning via Wavelet Packet Decomposition and Knowledge Distillation
Authors: Shilian Zheng, Quan Lin, Peihan Qi, Luxin Zhang, Xinjiang Qiu, Zhijin Zhao, Xiaoniu Yang
Date: 2025-04-10 00:00:00
Tasks: Knowledge Distillation, Position

Accurate and efficient positioning in complex environments is critical for applications where traditional satellite-based systems face limitations, such as indoors or urban canyons. This paper introduces WK-Pnet, an FM-based indoor positioning framework that combines wavelet packet decomposition (WPD) and knowledge distillation. WK-Pnet leverages WPD to extract rich time-frequency features from FM signals, which are then processed by a deep learning model for precise position estimation. To address computational demands, we employ knowledge distillation, transferring insights from a high-capacity model to a streamlined student model, achieving substantial reductions in complexity without sacrificing accuracy. Experimental results across diverse environments validate WK-Pnet's superior positioning accuracy and lower computational requirements, making it a viable solution for positioning in real-time resource-constraint applications.
Diffusion Augmented Complex Maximum Total Correntropy Algorithm for Power System Frequency Estimation
Authors: Haiquan Zhao, Yi Peng, Jinsong Chen, Jinhui Hu
Date: 2025-04-10 00:00:00

Currently, adaptive filtering algorithms have been widely applied in frequency estimation for power systems. However, research on diffusion tasks remains insufficient. Existing diffusion adaptive frequency estimation algorithms exhibit certain limitations in handling input noise and lack robustness against impulsive noise. Moreover, traditional adaptive filtering algorithms designed based on the strictly-linear (SL) model fail to effectively address frequency estimation challenges in unbalanced three-phase power systems. To address these issues, this letter proposes an improved diffusion augmented complex maximum total correntropy (DAMTCC) algorithm based on the widely linear (WL) model. The proposed algorithm not only significantly enhances the capability to handle input noise but also demonstrates superior robustness to impulsive noise. Furthermore, it successfully resolves the critical challenge of frequency estimation in unbalanced three-phase power systems, offering an efficient and reliable solution for diffusion power system frequency estimation. Finally, we analyze the stability of the algorithm and computer simulations verify the excellent performance of the algorithm.
Conformal Robust Beamforming via Generative Channel Models
Authors: Xin Su, Qiushuo Hou, Ruisi He, Osvaldo Simeone
Date: 2025-04-09 00:00:00
Tasks: Conformal Prediction, Decision Making, Uncertainty Quantification, valid

Traditional approaches to outage-constrained beamforming optimization rely on statistical assumptions about channel distributions and estimation errors. However, the resulting outage probability guarantees are only valid when these assumptions accurately reflect reality. This paper tackles the fundamental challenge of providing outage probability guarantees that remain robust regardless of specific channel or estimation error models. To achieve this, we propose a two-stage framework: (i) construction of a channel uncertainty set using a generative channel model combined with conformal prediction, and (ii) robust beamforming via the solution of a min-max optimization problem. The proposed method separates the modeling and optimization tasks, enabling principled uncertainty quantification and robust decision-making. Simulation results confirm the effectiveness and reliability of the framework in achieving model-agnostic outage guarantees.
xApp Conflict Mitigation with Scheduler
Authors: Idris Cinemre, Toktam Mahmoodi, Amirmohammad Farzaneh
Date: 2025-04-09 00:00:00
Tasks: Scheduling

Open RAN (O-RAN) fosters multi-vendor interoperability and data-driven control but simultaneously introduces the challenge of coordinating pre-trained xApps that may produce conflicting actions. Although O-RAN specifications mandate offline training and validation to prevent the deployment of untrained or inadequately tested models, operational conflicts can still arise under dynamic and context-dependent conditions.This work proposes a scheduler-based conflict mitigation framework to address these challenges without requiring training xApps together or further xApp re-training. By examining an indirect conflict involving power and resource block allocation xApps and employing an Advantage Actor-Critic (A2C) approach to train both xApps and the scheduler, we illustrate that a straightforward A2C-based scheduler improves performance relative to independently deployed xApps and conflicting cases. Notably, among all tested deployment scenarios (including individual xApp deployment, multiple conflicting xApps, and limited scheduler configurations), augmenting the system with baseline xApps and enabling the scheduler to select from a broader pool achieves the highest total transmission rate, thereby underscoring the importance of adaptive scheduling mechanisms. These findings highlight the context-dependent nature of conflicts in automated network management, as two xApps may conflict under certain conditions but coexist under others. Consequently, the ability to dynamically update and adapt the scheduler to accommodate diverse operational intents is vital for future network deployments. By offering dynamic scheduling without re-training xApps, this framework advances practical conflict resolution solutions while supporting real-world scalability.
Interference Mitigation and Spectral Efficiency Enhancement in a Multi-BD Symbiotic Radio
Authors: Fikiri Salum Uledi, Muhammad Bilal Janjua, Çagrı Özgenç Etemoglu, Hüseyin Arslan
Date: 2025-04-09 00:00:00

This study presents a framework designed to mitigate direct-link interference (DLI) and inter-backscatter device interference (IBDI) in multi-backscatter orthogonal frequency division multiplexing (OFDM)-based symbiotic radio (SR) systems. The framework employs OFDM signal designs with strategic allocation of null subcarriers and incorporates two backscatter modulation techniques: on-off frequency shift keying (OFSK) and multiple frequency shift keying (MFSK) for symbiotic backscatter communication (SBC). Additionally, we propose Fully-Orthogonal and Semi-Orthogonal multiple access schemes to facilitate SBC alongside primary communication. The Fully-Orthogonal scheme maintains orthogonality between direct link and SBC signals, thereby ensuring interference-free SBC, albeit at a reduced spectral efficiency. In contrast, the Semi-Orthogonal schemes eliminate IBDI but permit partial DLI, striking a balance between reliability and spectral efficiency. To address the partial DLI inherent in Semi-Orthogonal schemes, successive interference cancellation (SIC) is employed at the receiver, enhancing SBC reliability. To tackle channel estimation challenges in SBC within the SR system, we implement non-coherent detection techniques at the receiver. The performance of the proposed system is evaluated based on average bit error rate (BER) and sum-rate metrics, demonstrating the effectiveness of our schemes. We provide analytical results for the system's detection performance under both proposed modulation techniques and multiple access schemes, which are subsequently validated through extensive simulations. These simulations indicate a notable error-rate reduction of up to $10^{-3}$ at $20$ dB with the Fully-Orthogonal scheme with MFSK.
Integrated Sensing and Communications Over the Years: An Evolution Perspective
Authors: Di Zhang, Yuanhao Cui, Xiaowen Cao, Nanchi Su, Fan Liu, Xiaojun Jing, J. Andrew Zhang, Jie Xu, Christos Masouros, Dusit Niyato, Marco Di Renzo
Date: 2025-04-09 00:00:00
Tasks: Decision Making, ISAC

Integrated Sensing and Communications (ISAC) enables efficient spectrum utilization and reduces hardware costs for beyond 5G (B5G) and 6G networks, facilitating intelligent applications that require both high-performance communication and precise sensing capabilities. This survey provides a comprehensive review of the evolution of ISAC over the years. We examine the expansion of the spectrum across RF and optical ISAC, highlighting the role of advanced technologies, along with key challenges and synergies. We further discuss the advancements in network architecture from single-cell to multi-cell systems, emphasizing the integration of collaborative sensing and interference mitigation strategies. Moreover, we analyze the progress from single-modal to multi-modal sensing, with a focus on the integration of edge intelligence to enable real-time data processing, reduce latency, and enhance decision-making. Finally, we extensively review standardization efforts by 3GPP, IEEE, and ITU, examining the transition of ISAC-related technologies and their implications for the deployment of 6G networks.
A Survey of New Mid-Band/FR3 for 6G: Channel Measurement, Characterization and Modeling in Outdoor Environment
Authors: Haiyang Miao, Jianhua Zhang, Pan Tang, Jie Meng, Qi Zhen, Ximan Liu, Enrui Liu, Peijie Liu, Lei Tian, Guangyi Liu
Date: 2025-04-09 00:00:00
Tasks: Survey

The new mid-band (6-24 GHz) has attracted significant attention from both academia and industry, which is the spectrum with continuous bandwidth that combines the coverage benefits of low frequency with the capacity advantages of high frequency. Since outdoor environments represent the primary application scenario for mobile communications, this paper presents the first comprehensive review and summary of multi-scenario and multi-frequency channel characteristics based on extensive outdoor new mid-band channel measurement data, including UMa, UMi, and O2I. Specifically, a survey of the progress of the channel characteristics is presented, such as path loss, delay spread, angular spread, channel sparsity, capacity and near-field spatial non-stationary characteristics. Then, considering that satellite communication will be an important component of future communication systems, we examine the impact of clutter loss in air-ground communications. Our analysis of the frequency dependence of mid-band clutter loss suggests that its impact is not significant. Additionally, given that penetration loss is frequency-dependent, we summarize its variation within the FR3 band. Based on experimental results, comparisons with the standard model reveal that while the 3GPP TR 38.901 model remains a useful reference for penetration loss in wood and glass, it shows significant deviations for concrete and glass, indicating the need for further refinement. In summary, the findings of this survey provide both empirical data and theoretical support for the deployment of mid-band in future communication systems, as well as guidance for optimizing mid-band base station deployment in the outdoor environment. This survey offers the reference for improving standard models and advancing channel modeling.
Sensing-Oriented Adaptive Resource Allocation Designs for OFDM-ISAC Systems
Authors: Peishi Li, Ming Li, Rang Liu, Qian Liu, A. Lee Swindlehurst
Date: 2025-04-09 00:00:00
Tasks: Integrated sensing and communication, ISAC

Orthogonal frequency division multiplexing - integrated sensing and communication (OFDM-ISAC) has emerged as a key enabler for future wireless networks, leveraging the widely adopted OFDM waveform to seamlessly integrate wireless communication and radar sensing within a unified framework. In this paper, we propose adaptive resource allocation strategies for OFDM-ISAC systems to achieve optimal trade-offs between diverse sensing requirements and communication quality-of-service (QoS). We first develop a comprehensive resource allocation framework for OFDM-ISAC systems, deriving closed-form expressions for key sensing performance metrics, including delay resolution, Doppler resolution, delay-Doppler peak sidelobe level (PSL), and received signal-to-noise ratio (SNR). Building on this theoretical foundation, we introduce two novel resource allocation algorithms tailored to distinct sensing objectives. The resolution-oriented algorithm aims to maximize the weighted delay-Doppler resolution while satisfying constraints on PSL, sensing SNR, communication sum-rate, and transmit power. The sidelobe-oriented algorithm focuses on minimizing delay-Doppler PSL while satisfying resolution, SNR, and communication constraints. To efficiently solve the resulting non-convex optimization problems, we develop two adaptive resource allocation algorithms based on Dinkelbach's transform and majorization-minimization (MM). Extensive simulations validate the effectiveness of the proposed sensing-oriented adaptive resource allocation strategies in enhancing resolution and sidelobe suppression. Remarkably, these strategies achieve sensing performance nearly identical to that of a radar-only scheme, which dedicates all resources to sensing. These results highlight the superior performance of the proposed methods in optimizing the trade-off between sensing and communication objectives within OFDM-ISAC systems.
A Novel Angle-Delay-Doppler Estimation Scheme for AFDM-ISAC System in Mixed Near-field and Far-field Scenarios
Authors: Yirui Luo, Yong Liang Guan, Yao Ge, David González G., Chau Yuen
Date: 2025-04-09 00:00:00
Tasks: Integrated sensing and communication, ISAC

The recently proposed multi-chirp waveform, affine frequency division multiplexing (AFDM), is considered as a potential candidate for integrated sensing and communication (ISAC). However, acquiring accurate target sensing parameter information becomes challenging due to fractional delay and Doppler shift occurrence, as well as effects introduced by the coexistence of near-field (NF) and far-field (FF) targets associated with large-scale antenna systems. In this paper, we propose a novel angle-delay-Doppler estimation scheme for AFDM-ISAC system in mixed NF and FF scenarios. Specifically, we model the received ISAC signals as a third-order tensor that admits a low-rank CANDECOMP/PARAFAC (CP) format. By employing the Vandermonde nature of the factor matrix and the spatial smoothing technique, we develop a structured CP decomposition method that guarantees the condition for uniqueness. We further propose a low-complexity estimation scheme to acquire target sensing parameters with fractional values, including angle of arrival/departure (AoA/AoD), delay and Doppler shift accurately. We also derive the Cram\'er-Rao Lower Bound (CRLB) as a benchmark and analyze the complexity of our proposed scheme. Finally, simulation results are provided to demonstrate the effectiveness and superiority of our proposed scheme.
Sensing With Random Communication Signals
Authors: Shihang Lu, Fan Liu, Yifeng Xiong, Zhen Du, Yuanhao Cui, Shuangyang Li, Weijie Yuan, Jie Yang, Shi Jin
Date: 2025-04-09 00:00:00
Tasks: Integrated sensing and communication, ISAC

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission.