pwc-archive/papers-with-abstracts huggingface dataset:
Panoptic: True Joint mmWave Communication and Sensing with Compressive Sidelobe Forming
Authors: Heyu Guo, Ruiyi Shen, Florian Kosterhon, Yasaman Ghasempour
Date: 2025-04-08 00:00:00

The integration of communication and sensing functions within mmWave systems has gained attention due to the potential for enhanced passive sensing and improved communication reliability. State-of-the-art techniques separate these two functions in frequency, use of hardware, or time, i.e., sending known preambles for channel sensing or unknown symbols for communications. In this paper, we introduce Panoptic, a novel system architecture for integrated communication and sensing sharing the same hardware, frequency, and time resources. Panoptic jointly detects unknown symbols and channel components from data-modulated signals. The core idea is a new beam manipulation technique, which we call compressive sidelobe forming, that maintains a directional mainlobe toward the intended communication nodes while acquiring unique spatial information through pseudorandom sidelobe perturbations. We implemented Panoptic on 60 GHz mmWave radios and conducted extensive over-the-air experiments. Our results show that Panoptic achieves reflector angular localization error of less than 2\deg while at the same time supporting mmWave data communication with a negligible BER penalty when compared with conventional communication-only mmWave systems.
Low-Complexity SDP-ADMM for Physical-Layer Multicasting in Massive MIMO Systems
Authors: Mahmoud Zaher, Emil Björnson
Date: 2025-04-08 00:00:00

There is a demand for the same data content from several user equipments (UEs) in many wireless communication applications. Physical-layer multicasting combines the beamforming capability of massive MIMO (multiple-input multiple-output) and the broadcast nature of the wireless channel to efficiently deliver the same data to a group of UEs using a single transmission. This paper tackles the max-min fair (MMF) multicast beamforming optimization, which is an NP-hard problem. We develop an efficient semidefinite program-alternating direction method of multipliers (SDP-ADMM) algorithm to find the near-global optimal rank-1 solution to the MMF multicast problem in a massive MIMO system. Numerical results show that the proposed SDP-ADMM algorithm exhibits similar spectral efficiency performance to state-of-the-art algorithms running on standard SDP solvers at a vastly reduced computational complexity. We highlight that the proposed ADMM elimination procedure can be employed as an effective low-complexity rank reduction method for other problems utilizing semidefinite relaxation.
Unimodular Waveform Design that Minimizes PSL of Ambiguity Function over A Continuous Doppler Frequency Shift Region of Interest
Authors: Weiting Lin, YuWei Chang, Borching Su
Date: 2025-04-08 00:00:00

In active sensing systems, waveforms with ambiguity functions (AFs) of low peak sidelobe levels (PSLs) across a time delay and Doppler frequency shift plane (delay-Doppler plane) of interest are desirable for reducing false alarms. Additionally, unimodular waveforms are preferred due to hardware limitations. In this paper, a new method is proposed to design unimodular waveforms with PSL suppression over a continuous Doppler frequency shift region, based on the discrete-time ambiguity function (DTAF). Compared with existing methods that suppress PSL over grid points in the delay-Doppler plane by using the discrete ambiguity function (DAF), we regard the DTAF optimization problem as of more practical interest because the Doppler frequency shifts observed in echo signals reflected from targets are inherently continuous rather than discrete. The problem of interest is formulated as an optimization problem with infinite constraints along with unimodular constraints. To the best of the authors' knowledge, such a problem has not been studied yet. We propose to reformulate a non-convex semi-infinite programming (SIP) to a semidefinite programming (SDP) with a finite number of constraints and a rank-one constraint, which is then solved by the sequential rank-one constraint relaxation (SROCR) algorithm. Simulation results demonstrate that the proposed method outperforms existing methods in achieving a lower PSL of AF over a continuous Doppler frequency shift region of interest. Moreover, the designed waveform can effectively prevent false alarms when detecting a target with an arbitrary velocity.
Fair Resource Allocation in UAV-based Semantic Communication System with Fluid Antenna
Authors: Liang Siyun, Chen Zhu, Zhaohui Yang, Changsheng You, Dusit Niyato, Kai-Kit Wong, Zhaoyang Zhang
Date: 2025-04-08 00:00:00
Tasks: Semantic Communication, Semantic Compression

In this paper, the problem of maximization of the minimum equivalent rate in a unmanned-aerial-vehicle (UAV)-based multi-user semantic communication system is investigated. In the considered model, a multi-antenna UAV employs semantic extraction techniques to compress the data ready to be sent to the users, which are equipped with fluid antennas. Our aim is to jointly optimize the trajectory of the UAV, the transmit beamforming and the semantic compression rate at the UAV, as well as the selection of activated ports in fluid antenna system (FAS), to maximize the minimum equivalent transmission rate among all user. An alternating algorithm is designed to solve the problem. Simulation results validate the effectiveness of the proposed algorithm.
Matched Topological Subspace Detector
Authors: Chengen Liu, Victor M. Tenorio, Antonio G. Marques, Elvin Isufi
Date: 2025-04-08 00:00:00
Tasks: Missing Values

Topological spaces, represented by simplicial complexes, capture richer relationships than graphs by modeling interactions not only between nodes but also among higher-order entities, such as edges or triangles. This motivates the representation of information defined in irregular domains as topological signals. By leveraging the spectral dualities of Hodge and Dirac theory, practical topological signals often concentrate in specific spectral subspaces (e.g., gradient or curl). For instance, in a foreign currency exchange network, the exchange flow signals typically satisfy the arbitrage-free condition and hence are curl-free. However, the presence of anomalies can disrupt these conditions, causing the signals to deviate from such subspaces. In this work, we formulate a hypothesis testing framework to detect whether simplicial complex signals lie in specific subspaces in a principled and tractable manner. Concretely, we propose Neyman-Pearson matched topological subspace detectors for signals defined at a single simplicial level (such as edges) or jointly across all levels of a simplicial complex. The (energy-based projection) proposed detectors handle missing values, provide closed-form performance analysis, and effectively capture the unique topological properties of the data. We demonstrate the effectiveness of the proposed topological detectors on various real-world data, including foreign currency exchange networks.
Unimodular Waveform Design for Integrated Sensing and Communication MIMO System via Manifold Optimization
Authors: Jiangtao Wang, Xuyang Zhao, Muyu Mei, Yongchao Wang
Date: 2025-04-08 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has been widely recognized as one of the key technologies for 6G wireless networks. In this paper, we focus on the waveform design of ISAC system, which can realize radar sensing while also facilitate information transmission. The main content is as follows: first, we formulate the waveform design problem as a nonconvex and non-smooth model with a unimodulus constraint based on the measurement metric of the radar and communication system. Second, we transform the model into an unconstrained problem on the Riemannian manifold and construct the corresponding operators by analyzing the unimodulus constraint. Third, to achieve the solution efficiently, we propose a low-complexity non-smooth unimodulus manifold gradient descent (N-UMGD) algorithm with theoretical convergence guarantee. The simulation results show that the proposed algorithm can concentrate the energy of the sensing signal in the desired direction and realize information transmission with a low bit error rate.
PRACH Preamble Detection as a Multi-Class Classification Problem: A Machine Learning Approach Using SVM
Authors: F. Ferenc, M. Szczachor
Date: 2025-04-08 00:00:00
Tasks: Multi-class Classification

This study addresses the preamble detection problem in the Random Access procedure of LTE/5G networks by formulating it as a multi-class classification task and evaluating the effectiveness of machine learning techniques. A Support Vector Machine (SVM) model is implemented and compared against conventional detection methods. The proposed approach improves preamble index assignment, enhancing detection efficiency for User Equipment (UE) accessing the network. Performance analysis demonstrates that the SVM-based solution increases detection accuracy while reducing missed detections. These findings underscore the potential of machine learning in optimizing the Random Access procedure and improving network accessibility.
Signal and Backward Raman Pump Power Optimization in Multi-Band Systems Using Fast Power Profile Estimation
Authors: Yanchao Jiang, Jad Sarkis, Stefano Piciaccia, Fabrizio Forghieri, Pierluigi Poggiolini
Date: 2025-04-08 00:00:00

This paper presents an efficient numerical method for calculating spatial power profiles of both signal and pump with significant Interchannel Stimulated Raman Scattering (ISRS) and backward Raman amplification in multiband systems. This method was evaluated in the optimization of a C+L+S/C+L+S+E 1000km link, employing three backward Raman pumps, by means of a closed-form EGN model (CFM6). The results show a 100x computational speed increase, enabling deep optimization which made it possible to obtain very good overall system performance and flat GSNR.
Large-Scale Classification of Shortwave Communication Signals with Machine Learning
Authors: Stefan Scholl
Date: 2025-04-07 00:00:00

This paper presents a deep learning approach to the classification of 160 shortwave radio signals. It addresses the typical challenges of the shortwave spectrum, which are the large number of different signal types, the presence of various analog modulations and ionospheric propagation. As a classifier a deep convolutional neural network is used, that is trained to recognize 160 typical shortwave signal classes. The approach is blind and therefore does not require preknowledge or special preprocessing of the signal and no manual design of discriminative features for each signal class. The network is trained on a large number of synthetically generated signals and high quality recordings. Finally, the network is evaluated on real-world radio signals obtained from globally deployed receiver hardware and achieves up to 90% accuracy for an observation time of only 1 second.
Performance and Complexity Analysis of Terahertz-Band MIMO Detection
Authors: Hakim Jemaa, Simon Tarboush, Hadi Sarieddeen, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri
Date: 2025-04-07 00:00:00

Achieving terabit-per-second (Tbps) data rates in terahertz (THz)-band communications requires bridging the complexity gap in baseband transceiver design. This work addresses the signal processing challenges associated with data detection in THz multiple-input multiple-output (MIMO) systems. We begin by analyzing the trade-offs between performance and complexity across various detection schemes and THz channel models, demonstrating significant complexity reduction by leveraging spatial parallelizability over subspaces of correlated THz MIMO channels. We derive accurate detection error probability bounds by accounting for THz-specific channel models and mismatches introduced by subspace decomposition. Building on this, we propose a subspace detector that integrates layer sorting, QR decomposition, and channel-matrix puncturing to balance performance loss and parallelizability. Furthermore, we introduce a channel-matrix reuse strategy for wideband THz MIMO detection. Simulations over accurate, ill-conditioned THz channels show that efficient parallelizability achieves multi-dB performance gains, while wideband reuse strategies offer computational savings with minimal performance degradation.
Modeling Micro-Doppler Signature of Multi-Propeller Drones in Distributed ISAC
Authors: Heraldo Cesar Alves Costa, Saw J. Myint, Carsten Andrich, Sebastian W. Giehl, Maximilian Engelhardt, Christian Schneider, Reiner S. Thomä
Date: 2025-04-07 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) will be one key feature of future 6G networks, enabling simultaneous communication and radar sensing. The radar sensing geometry of ISAC will be multistatic since that corresponds to the common distributed structure of a mobile communication network. Within this framework, micro-Doppler analysis plays a vital role in classifying targets based on their micromotions, such as rotating propellers, vibration, or moving limbs. However, research on bistatic micro-Doppler effects, particularly in ISAC systems utilizing OFDM waveforms, remains limited. Existing methods, including electromagnetic simulations often lack scalability for generating the large datasets required to train machine learning algorithms. To address this gap, this work introduces an OFDM-based bistatic micro-Doppler model for multi-propeller drones. The proposed model adapts the classic thin-wire model to include bistatic sensing configuration with an OFDM-like signal. Then, it extends further by incorporating multiple propellers and integrating the reflectivity of the drone's static parts. Measurements were performed to collect ground truth data for verification of the proposed model. Validation results show that the model generates micro-Doppler signatures closely resembling those obtained from measurements, demonstrating its potential as a tool for data generation. In addition, it offers a comprehensive approach to analyzing bistatic micro-Doppler effects.
"Security for Everyone" in Finite Blocklength IRS-aided Systems With Perfect and Imperfect CSI
Authors: Monir Abughalwa, Diep N. Nguyen, Dinh Thai Hoang, Van-Dinh Nguyen, Ming Zeng, Quoc-Viet Pham, Eryk Dutkiewicz
Date: 2025-04-07 00:00:00

Provisioning secrecy for all users, given the heterogeneity in their channel conditions, locations, and the unknown location of the attacker/eavesdropper, is challenging and not always feasible. The problem is even more difficult under finite blocklength constraints that are popular in ultra-reliable low-latency communication (URLLC) and massive machine-type communications (mMTC). This work takes the first step to guarantee secrecy for all URLLC/mMTC users in the finite blocklength regime (FBR) where intelligent reflecting surfaces (IRS) are used to enhance legitimate users' reception and thwart the potential eavesdropper (Eve) from intercepting. To that end, we aim to maximize the minimum secrecy rate (SR) among all users by jointly optimizing the transmitter's beamforming and IRS's passive reflective elements (PREs) under the FBR latency constraints. The resulting optimization problem is non-convex and even more complicated under imperfect channel state information (CSI). To tackle it, we linearize the objective function, and decompose the problem into sequential subproblems. When perfect CSI is not available, we use the successive convex approximation (SCA) approach to transform imperfect CSI-related semi-infinite constraints into finite linear matrix inequalities (LMI). We prove that our proposed algorithm converges to a locally optimal solution with low computational complexity thanks to our closed-form linearization approach. This makes the solution scalable for large IRS deployments. Extensive simulations with practical settings show that our approach can ensure secure communication for all users while satisfying FBR constraints even with only imperfect CSI.
Secure Communications for All Users in Low-Resolution IRS-aided Systems Under Imperfect and Unknown CSI
Authors: Monir Abughalwa, Diep N. Nguyen, Dinh Thai Hoang, Thang X. Vu, Eryk Dutkiewicz, Symeon Chatzinotas
Date: 2025-04-07 00:00:00
Tasks: All

Provisioning secrecy for all users, given the heterogeneity and uncertainty of their channel conditions, locations, and the unknown location of the attacker/eavesdropper, is challenging and not always feasible. This work takes the first step to guarantee secrecy for all users where a low resolution intelligent reflecting surfaces (IRS) is used to enhance legitimate users' reception and thwart the potential eavesdropper (Eve) from intercepting. In real-life scenarios, due to hardware limitations of the IRS' passive reflective elements (PREs), the use of a full-resolution (continuous) phase shift (CPS) is impractical. In this paper, we thus consider a more practical case where the phase shift (PS) is modeled by a low-resolution (quantized) phase shift (QPS) while addressing the phase shift error (PSE) induced by the imperfect channel state information (CSI). To that end, we aim to maximize the minimum secrecy rate (SR) among all users by jointly optimizing the transmitter's beamforming vector and the IRS's passive reflective elements (PREs) under perfect/imperfect/unknown CSI. The resulting optimization problem is non-convex and even more complicated under imperfect/unknown CSI. The resulting optimization problem is non-convex and even more complicated under imperfect/unknown CSI. To tackle it, we linearize the objective function and decompose the problem into sequential subproblems. When the perfect CSI is not available, we use the successive convex approximation (SCA) approach to transform imperfect CSI related semi-infinite constraints into finite linear matrix inequalities (LMI). We prove that our proposed algorithm converges to a locally optimal solution with low computational complexity. Extensive simulations with practical settings show that our approach can ensure secure communication for all users while the IRS's PREs are quantized and are affected by the PSE.
Probabilistic Position-Aided Beam Selection for mmWave MIMO Systems
Authors: Joseph K. Chege, Arie Yeredor, Martin Haardt
Date: 2025-04-07 00:00:00
Tasks: Bayesian Inference, Position

Millimeter-wave (mmWave) MIMO systems rely on highly directional beamforming to overcome severe path loss and ensure robust communication links. However, selecting the optimal beam pair efficiently remains a challenge due to the large search space and the overhead of conventional methods. This paper proposes a probabilistic position-aided beam selection approach that exploits the statistical dependence between user equipment (UE) positions and optimal beam indices. We model the underlying joint probability mass function (PMF) of the positions and the beam indices as a low-rank tensor and estimate its parameters from training data using Bayesian inference. The estimated model is then used to predict the best (or a list of the top) beam pair indices for new UE positions. The proposed method is evaluated using data generated from a state-of-the-art ray tracing simulator and compared with neural network-based and fingerprinting approaches. The results show that our approach achieves a high data rate with fewer training samples and a significantly reduced beam search space. These advantages render it a promising solution for practical mmWave MIMO deployments, reducing the beam search overhead while maintaining a reliable connectivity.
Joint BS Deployment and Power Optimization for Minimum EMF Exposure with RL in Real-World Based Urban Scenario
Authors: Xueyun Long, Yueheng Li, Mario Pauli, Benjamin Nuss, Thomas Zwick
Date: 2025-04-07 00:00:00

Conventional base station (BS) deployments typically prioritize coverage, quality of service (QoS), or cost reduction, often overlooking electromagnetic field (EMF) exposure. Whereas EMF exposure triggers significant public concern due to its potential health implications, making it crucial to address when deploying BS in densely populated areas. To this end, this paper addresses minimizing average EMF exposure while maintaining coverage in a 3D urban scenario by jointly optimizing BS deployment and power. To address this, firstly, accurate EMF prediction is essential, as traditional empirical models lack the required accuracy, necessitating a deterministic channel model. A novel least-time shoot-and-bounce ray (SBR) ray-launching (RL) algorithm is therefore developed to overcome several limitations of current simulators and is validated with real-world measurements. Secondly, to further reduce computational complexity, unlike using a fixed grid size to discretize the target area, the adaptive grid refinement (AGR) algorithm is designed with a flexible grid to predict the overall EMF exposure. Finally, based on the EMF exposure predictions, the Nelder-Mead (NM) method is used in the joint optimization, and urban user equipment (UE) distributions are incorporated to better reflect real-world conditions. When evaluating the benefits of the whole process, the results are compared against using empirical channel models, revealing notable differences and underestimation of EMF exposure that highlight the importance of considering real-world scenario.
Low-Rate Semantic Communication with Codebook-based Conditional Generative Models
Authors: Kailang Ye, Mingze Gong, Shuoyao Wang, Daquan Feng
Date: 2025-04-07 00:00:00
Tasks: Saliency Detection, Semantic Communication

Generative semantic communication models are reshaping semantic communication frameworks by moving beyond pixel-wise optimization to align with human perception. However, many existing approaches prioritize image-level perceptual quality, often neglecting alignment with downstream tasks, which can lead to suboptimal semantic representation. This paper introduces an Ultra-Low Bitrate Semantic Communication (ULBSC) system that employs a conditional generative model and a learnable condition codebook.By integrating saliency conditions and image-level semantic information, the proposed method enables high-perceptual-quality and controllable task-oriented image transmission. Recognizing shared patterns among objects, we propose a codebook-assisted condition transmission method, integrated with joint source-channel coding (JSCC)-based text transmission to establish ULBSC. The codebook serves as a knowledge base, reducing communication costs to achieve ultra-low bitrate while enhancing robustness against noise and inaccuracies in saliency detection. Simulation results indicate that, under ultra-low bitrate conditions with an average compression ratio of 0.57Z%o, the proposed system delivers superior visual quality compared to traditional JSCC techniques and achieves higher saliency similarity between the generated and source images compared to state-of-the-art generative semantic communication methods.