pwc-archive/papers-with-abstracts huggingface dataset:
Robust Phantom-Assisted Framework for Multi-Person Localization and Vital Signs Monitoring Using MIMO FMCW Radar
Authors: Yonathan Eder, Emma Zagoury, Shlomi Savariego, Moshe Namer, Oded Cohen, Yonina C. Eldar
Date: 2025-01-12 00:00:00

With the rising prevalence of cardiovascular and respiratory disorders and an aging global population, healthcare systems face increasing pressure to adopt efficient, non-contact vital sign monitoring (NCVSM) solutions. This study introduces a robust framework for multi-person localization and vital signs monitoring, using multiple-input-multiple-output frequency-modulated continuous wave radar, addressing challenges in real-world, cluttered environments. Two key contributions are presented. First, a custom hardware phantom was developed to simulate multi-person NCVSM scenarios, utilizing recorded thoracic impedance signals to replicate realistic cardiopulmonary dynamics. The phantom's design facilitates repeatable and rapid validation of radar systems and algorithms under diverse conditions to accelerate deployment in human monitoring. Second, aided by the phantom, we designed a robust algorithm for multi-person localization utilizing joint sparsity and cardiopulmonary properties, alongside harmonics-resilient dictionary-based vital signs estimation, to mitigate interfering respiration harmonics. Additionally, an adaptive signal refinement procedure is introduced to enhance the accuracy of continuous NCVSM by leveraging the continuity of the estimates. Performance was validated and compared to existing techniques through 12 phantom trials and 12 human trials, including both single- and multi-person scenarios, demonstrating superior localization and NCVSM performance. For example, in multi-person human trials, our method achieved average respiration rate estimation accuracies of 94.14%, 98.12%, and 98.69% within error thresholds of 2, 3, and 4 breaths per minute, respectively, and heart rate accuracies of 87.10%, 94.12%, and 95.54% within the same thresholds. These results highlight the potential of this framework for reliable multi-person NCVSM in healthcare and IoT applications.
Multi-Carrier Faster-Than-Nyquist Signaling for OTFS Systems
Authors: Xueyang Wang, Shiqi Gong, Wenqian Shen, Chengwen Xing, J. Andrew Zhang
Date: 2025-01-12 00:00:00

Orthogonal time frequency space (OTFS) modulation technique is promising for high-mobility applications to achieve reliable communications. However, the capacity of OTFS systems is generally limited by the Nyquist criterion, requiring orthogonal pulses in both time and frequency domains. In this paper, we propose a novel multi-carrier faster-than-Nyquist (MC-FTN) signaling scheme for OTFS systems. By adopting non-orthogonal pulses in both time and frequency domains, our scheme significantly improves the capacity of OTFS systems. Specifically, we firstly develop the signal models for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) OTFS systems. Then, we optimize the delay-Doppler (DD) domain precoding matrix at the transmitter to suppress both the inter-symbol interference (ISI) and inter-carrier interference (ICI) introduced by the MC-FTN signaling. For SISO systems, we develop an eigenvalue decomposition (EVD) precoding scheme with optimal power allocation (PA) for achieving the maximum capacity. For MIMO systems, we develop a successive interference cancellation (SIC)-based precoding scheme via decomposing the capacity maximization problem into multiple sub-capacity maximization problems with largely reduced dimensions of optimization variables. Numerical results demonstrate that our proposed MC-FTN-OTFS signaling scheme achieves significantly higher capacity than traditional Nyquist-criterion-based OTFS systems. Moreover, the SIC-based precoding scheme can effectively reduce the complexity of MIMO capacity maximization, while attaining performance close to the optimal EVD-based precoding scheme.
Sidelobe Level Reduction in the ACF of NLFM Signals Using the Smoothing Spline Method
Authors: Roohollah Ghavamirad, Ramezan Ali Sadeghzadeh, Mohammad Ali Sebt
Date: 2025-01-11 00:00:00

The high level of sidelobes in the autocorrelation function of the nonlinear frequency modulation signal is a challenge. One of the conventional methods to reduce the sidelobe levels is to use the principle of stationary phase. In this method, the frequency function is calculated using a selection window. The signal frequency function cannot be obtained in closed form and numerical methods must be used to find it. This is usually done using the polynomial curve fitting. In this paper, the frequency function of the signal has been obtained using the smoothing spline method. The simulation results show an improvement of 10 dB to 20 dB in the peak sidelobe level of the autocorrelation function of the nonlinear frequency modulation signal compared to the previous methods.
Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks
Authors: Muhammad Ahmed Mohsin, Hassan Rizwan, Muhammad Jazib, Muhammad Iqbal, Muhammad Bilal, Tabinda Ashraf, Muhammad Farhan Khan, Jen-Yi Pan
Date: 2025-01-11 00:00:00
Tasks: Deep Reinforcement Learning, Diversity

This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated multipoint non-orthogonal multiple access (CoMP-NOMA). Our system model incorporates a UAV-assisted RIS in coordination with a terrestrial RIS which aims for signal enhancement. We aim to maximize the sum rate for all users in the network using a custom hybrid proximal policy optimization (H-PPO) algorithm by optimizing the UAV trajectory, base station (BS) power allocation factors, active RIS amplification factor, and phase shift matrix. We integrate edge users into NOMA pairs to achieve diversity gain, further enhancing the overall experience for edge users. Exhaustive comparisons are made with passive RIS-assisted networks to demonstrate the superior efficacy of active RIS in terms of energy efficiency, outage probability, and network sum rate.
Target Detection in ISAC Systems with Active RISs: A Multi-Perspective Observation Approach
Authors: Shoushuo Zhang, Rang Liu, Ming Li, Wei Wang, Qian Liu
Date: 2025-01-11 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has emerged as a transformative technology for 6G networks, enabling the seamless integration of communication and sensing functionalities. Reconfigurable intelligent surfaces (RIS), with their capability to adaptively reconfigure the radio environment, have shown significant potential in enhancing communication quality and enabling advanced cooperative sensing. This paper investigates a multi-RIS-assisted ISAC system and introduces a novel multi-perspective observation framework that leverages the diversity of multiple observation paths, each exhibiting distinct spatial, delay, and Doppler characteristics for both target and clutter. The proposed framework integrates symbol-level precoding (SLP) and space-time adaptive processing (STAP) to fully exploit the benefits of multi-perspective observations, enabling superior target-clutter separation and significantly improving detection accuracy. The objective is to jointly design the transmit waveform, reflection coefficients of multiple active RISs, and spatial-temporal receive filters to maximize the radar output signal-to-clutter-plus-noise ratio (SCNR) for target detection, while ensuring the quality-of-service (QoS) requirements of communication users. To address the resulting non-convex optimization problem, an effective iterative algorithm is developed, combining fractional programming (FP), majorization-minimization (MM), and the alternating direction method of multipliers (ADMM). Extensive simulation results validate the effectiveness of the proposed multi-perspective observation strategy, demonstrating its advantages in improving target detection performance in challenging environments.
Precoding Design for Limited-Feedback MISO Systems via Character-Polynomial Codes
Authors: Siva Aditya Gooty, Samin Riasat, Hessam Mahdavifar, Robert W. Heath Jr
Date: 2025-01-10 00:00:00
Tasks: Quantization

We consider the problem of Multiple-Input Single- Output (MISO) communication with limited feedback, where the transmitter relies on a limited number of bits associated with the channel state information (CSI), available at the receiver (CSIR) but not at the transmitter (no CSIT), sent via the feedback link. We demonstrate how character-polynomial (CP) codes, a class of analog subspace codes (also, referred to as Grassmann codes) can be used for the corresponding quantization problem in the Grassmann space. The proposed CP codebook-based precoding design allows for a smooth trade-off between the number of feedback bits and the beamforming gain, by simply adjusting the rate of the underlying CP code. We present a theoretical upper bound on the mean squared quantization error of the CP codebook, and utilize it to upper bound the resulting distortion as the normalized gap between the CP codebook beamforming gain and the baseline equal gain transmission (EGT) with perfect CSIT. We further show that the distortion vanishes asymptotically. The results are also confirmed via simulations for different types of fading models in the MISO system and various parameters.
Multifractal Analysis of Physiological Signals: A Novel Approach to Optimizing Pacing Strategy in a Pilot Study
Authors: Véronique Billat, Wejdene Nasr Ben Hadj Amor, Guillaume Saës, Stéphane Jaffard, Florent Palacin
Date: 2025-01-10 00:00:00

Marathons are one of the ultimate challenges of human endeavor. In this paper, we apply recently introduced multifractal techniques which yield a new classification parameter in the processing of physiological data captured on marathon runners. The comparison of their values gives a new insight on the way that runners of different level conduct their run, and ultimately, can be used in order to give advice on how to improve their performance.
On the Sum Rate and User Fairness of STAR-RIS Aided Communications
Authors: Haochen Li, Xidong Mu, Yuanwei Liu, Yue Chen, Pan Zhiwen
Date: 2025-01-10 00:00:00
Tasks: Fairness

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided communication system is investigated. A robust joint beamforming design problem under the imperfect channel state information (CSI) is formulated to maximize the weighted sum of the Jain's fairness index and the normalized system sum rate. To solve this non-convex problem, an alternating optimization (AO) algorithm is proposed, which leverages the S-Procedure, successive convex approximation (SCA), and semidefinite relaxation (SDR). Simulation results demonstrate that with proposed algorithm: 1) various trade-offs between sum rate and user fairness can be achieved; 2) a larger trade-off region can be achieved by adopting STAR-RIS compared to conventional RIS; and 3) the performance degradation caused by imperfect CSI is less than 7% with our proposed robust beamforming approach.
Soft-Demapping for Short Reach Optical Communication: A Comparison of Deep Neural Networks and Volterra Series
Authors: Maximilian Schaedler, Georg Böcherer, Stephan Pachnicke
Date: 2025-01-10 00:00:00

In optical fiber communication, optical and electrical components introduce nonlinearities, which require effective compensation to attain highest data rates. In particular, in short reach communication, components are the dominant source of nonlinearities. Volterra series are a popular countermeasure for receiver-side equalization of nonlinear component impairments and their memory effects. However, Volterra equalizer architectures are generally very complex. This article investigates soft deep neural network (DNN) architectures as an alternative for nonlinear equalization and soft-decision demapping. On coherent 92 GBd dual polarization 64QAM back-to-back measurements performance and complexity is experimentally evaluated. The proposed bit-wise soft DNN equalizer (SDNNE) is compared to a 5th order Volterra equalizer at a 15 % overhead forward error correction (FEC) limit. At equal performance, the computational complexity is reduced by 65 %. At equal complexity, the performance is improved by 0.35 dB gain in optical signal-to-noise-ratio (OSNR).
A Practical Demonstration of DRL-Based Dynamic Resource Allocation xApp Using OpenAirInterface
Authors: Onur Sever, Onur Salan, Ibrahim Hokelek, Ali Gorcin
Date: 2025-01-10 00:00:00
Tasks: Deep Reinforcement Learning

Network slicing is a key enabler for providing a differentiated service support to heterogeneous use cases and applications in 5G and beyond networks through creating multiple logical slices. Resource allocation for satisfying diverse requirements of slices is a highly challenging task under time-varying traffic and wireless channel conditions. This paper presents a deep reinforcement learning (DRL) approach for allocating radio resources to slices, where the objective is to meet the latency requirement of the low-latency slice without jeopardizing the performance of the other slice. The proposed DRL approach is implemented within an open source mobile network emulator, namely OpenAirInterface, to create an O-RAN compliant end-to-end 5G network capable of dynamic resource allocation capabilities. The intelligent resource allocation mechanism operates on the RAN Intelligent Controller (RIC) as an xApp, enabling monitoring and dynamic resource control of the gNB through the E2 interface. The results demonstrate that the latency requirement of the low-latency slice is met under extremely loaded traffic scenarios, where the trained DRL model deployed on the near-RT RIC platform is used to dynamically allocate the radio resources to the slices.
User Selection in Near-Field Gigantic MIMO Systems with Modular Arrays
Authors: José P. González-Coma, Santiago Fernández, F. Javier López-Martínez
Date: 2025-01-10 00:00:00

Modular Arrays (MAs) are a promising architecture to enable multi-user communications in next-generation multiple-input multiple-output (MIMO) systems based on extra-large (XL) or gigantic MIMO (gMIMO) deployments, trading off improved spatial resolution with characteristic interference patterns associated with grating lobes. In this work, we analyze whether MAs can outperform conventional collocated deployments, in terms of achievable sum-spectral efficiency (SE) and served users in a multi-user downlink set-up. First, we provide a rigorous analytical characterization of the inter-user interference for modular gMIMO systems operating in the near field. Then, we leverage these results to optimize the user selection and precoding mechanisms, designing two algorithms that largely outperform existing alternatives in the literature, with different algorithmic complexities. Results show that the proposed algorithms yield over 70% improvements in achievable sum-spectral efficiencies compared to the state of the art. We also illustrate how MAs allows us to serve a larger number of users thanks to their improved spatial resolution, compared to the collocated counterpart.
RIS Optimization Algorithms for Urban Wireless Scenarios in Sionna RT
Authors: Ahmet Esad Güneşer, Berkay Şekeroğlu, Sefa Kayraklık, Erhan Karakoca, İbrahim Hökelek, Sultan Aldirmaz-Colak, Ali Görçin
Date: 2025-01-10 00:00:00

This paper evaluates the performance of reconfigurable intelligent surface (RIS) optimization algorithms, which utilize channel estimation methods, in ray tracing (RT) simulations within urban digital twin environments. Beyond Sionna's native capabilities, we implement and benchmark additional RIS optimization algorithms based on channel estimation, enabling an evaluation of RIS strategies under various deployment conditions. Coverage maps for RIS-assisted communication systems are generated through the integration of Sionna's RT simulations. Moreover, real-world experimentation underscores the necessity of validating algorithms in near-realistic simulation environments, as minor variations in measurement setups can significantly affect performance.
Array Gain for Pinching-Antenna Systems (PASS)
Authors: Chongjun Ouyang, Zhaolin Wang, Yuanwei Liu, Zhiguo Ding
Date: 2025-01-10 00:00:00

Pinching antennas is a novel flexible-antenna technology, which can be realized by employing small dielectric particles on a waveguide. The aim of this letter is to characterize the array gain achieved by pinching-antenna systems (PASS). A closed-form upper bound on the array gain is derived by fixing the inter-antenna spacing. Asymptotic analyses of this bound are conducted by considering an infinitely large number of antennas, demonstrating the existence of an optimal number of antennas that maximizes the array gain. To approach this bound, an antenna position refinement method is introduced. The relationship between the array gain and inter-antenna spacing is further explored by incorporating the effect of mutual coupling. It is proven that there also exists an optimal inter-antenna spacing that maximizes the array gain. Numerical results demonstrate that by optimizing the number of antennas and inter-antenna spacing, PASS can achieve a significantly larger array gain than conventional-antenna systems.
Savanna dynamics with grazing, browsing, and migration effects
Authors: Chiun-Chuan Chen, Ting-Yang Hsiao, Shun-Chieh Wang
Date: 2025-01-11 00:00:00

This article explores the dynamics of savanna ecosystems with grazing, browsing, and migration effects. Covering over one-eighth of the Earth's land area and supporting about one-fifth of the global population, the savanna is an ecological system whose importance has only recently garnered significant attention from biologists. The interactions between organisms in this ecosystem are highly complex, and fundamental mathematical issues remain unresolved. We rigorously analyze traveling waves in savanna systems and focus on whether trees, grass, grazers, and browsers coexist. We demonstrate the existence of various traveling waves, including waves transitioning from extinction to co-existence and waves from a grass-vegetation state (where only grass and grazers exist) to co-existence. Due to the biodiversity of species in grassland ecosystems, it is not appropriate to consider overly simplified models of competition between grasses and trees. From both a biological and mathematical perspective, factors such as animal grazing, browsing, and migration (which facilitates seed dispersal) play a crucial role in promoting ecological stability and coexistence. Additionally, we estimate the nonzero minimum value of the total plant biomass within the savanna dynamic system to better understand the persistence and stability of sustainable development within the ecosystem.
Predicting the depth of the most recent common ancestor of a random sample of $k$ species: the impact of phylogenetic tree shape
Authors: Michael Fuchs, Mike Steel
Date: 2025-01-16 00:00:00

We consider the following question: how close to the ancestral root of a phylogenetic tree is the most recent common ancestor of $k$ species randomly sampled from the tips of the tree? For trees having shapes predicted by the Yule-Harding model, it is known that the most recent common ancestor is likely to be close to (or equal to) the root of the full tree, even as $n$ becomes large (for $k$ fixed). However, this result does not extend to models of tree shape that more closely describe phylogenies encountered in evolutionary biology. We investigate the impact of tree shape (via the Aldous $\beta-$splitting model) to predict the number of edges that separate the most recent common ancestor of a random sample of $k$ tip species and the root of the parent tree they are sampled from. Both exact and asymptotic results are presented. We also briefly consider a variation of the process in which a random number of tip species are sampled.
Continuation methods as a tool for parameter inference in electrophysiology modeling
Authors: Matt J Owen, Gary R Mirams
Date: 2025-01-13 00:00:00

Parameterizing mathematical models of biological systems often requires fitting to stable periodic data. In cardiac electrophysiology this typically requires converging to a stable action potential through long simulations. We explore this problem through the theory of dynamical systems, bifurcation analysis and continuation methods; under which a converged action potential is a stable limit cycle. Various attempts have been made to improve the efficiency of identifying these limit cycles, with limited success. We demonstrate that continuation methods can more efficiently infer the converged action potential as proposed model parameter sets change during optimization or inference routines. In an example electrophysiology model this reduces parameter inference computation time by 70%. We also discuss theoretical considerations and limitations of continuation method use in place of time-consuming model simulations. The application of continuation methods allows more robust optimization by making extra runs from multiple starting locations computationally tractable, and facilitates the application of inference methods such as Markov Chain Monte Carlo to gain more information on the plausible parameter space.
The Convergence of Dynamic Routing between Capsules
Authors: Daoyuan Ye, Juntao Li, YiTing Shen
Date: 2025-01-08 00:00:00

Capsule networks(CapsNet) are recently proposed neural network models with new processing layers, specifically for entity representation and discovery of images. It is well known that CapsNet have some advantages over traditional neural networks, especially in generalization capability. At the same time, some studies report negative experimental results. The causes of this contradiction have not been thoroughly analyzed. The preliminary experimental results show that the behavior of routing algorithms does not always produce good results as expected, and in most cases, different routing algorithms do not change the classification results, but simply polarize the link strength, especially when they continue to repeat without stopping. To realize the true potential of the CapsNet, deep mathematical analysis of the routing algorithms is crucial. In this paper, we will give the objective function that is minimized by the dynamic routing algorithm, which is a concave function. The dynamic routing algorithm can be regarded as nonlinear gradient method to solving an optimization algorithm under linear constraints, and its convergence can be strictly proved mathematically. Furthermore, the mathematically rigorous proof of the convergence is given for this class of iterative routing procedures. We analyze the relation between the objective function and the constraints solved by the dynamic routing algorithm in detail, and perform the corresponding routing experiment to analyze the effect of our convergence proof.