pwc-archive/papers-with-abstracts huggingface dataset:
Beamforming Design for Secure RIS-Enabled ISAC: Passive RIS vs. Active RIS
Authors: Vaibhav Kumar, Marwa Chafii
Date: 2025-01-31 00:00:00
Tasks: Integrated sensing and communication, ISAC

The forthcoming sixth-generation (6G) communications standard is anticipated to provide integrated sensing and communication (ISAC) as a fundamental service. These ISAC systems present unique security challenges because of the exposure of information-bearing signals to sensing targets, enabling them to potentially eavesdrop on sensitive communication information with the assistance of sophisticated receivers. Recently, reconfigurable intelligent surfaces (RISs) have shown promising results in enhancing the physical layer security of various wireless communication systems, including ISAC. However, the performance of conventional passive RIS (pRIS)-enabled systems are often limited due to multiplicative fading, which can be alleviated using active RIS (aIRS). In this paper, we consider the problem of beampattern gain maximization in a secure pRIS/aRIS-enabled ISAC system, subject to signal-to-interference-plus-noise ratio constraints at communication receivers, and information leakage constraints at an eavesdropping target. For the challenging non-convex problem of joint beamforming design at the base station and the pRIS/aRIS, we propose a novel successive convex approximation (SCA)-based method. Unlike the conventional alternating optimization (AO)-based methods, in the proposed SCA-based approach, all of the optimization variables are updated simultaneously in each iteration. The proposed method shows significant performance superiority for pRIS-aided ISAC system compared to a benchmark scheme using penalty-based AO method. Moreover, our simulation results also confirm that aRIS-aided system has a notably higher beampattern gain at the target compared to that offered by the pRIS-aided system for the same power budget. We also present a detailed complexity analysis and proof of convergence for the proposed SCA-based method.
An Integrated Sensing and Communications System Based on Affine Frequency Division Multiplexing
Authors: Yuanhan Ni, Peng Yuan, Qin Huang, Fan Liu, Zulin Wang
Date: 2025-01-31 00:00:00
Tasks: ISAC

This paper proposes an integrated sensing and communications (ISAC) system based on affine frequency division multiplexing (AFDM) waveform. To this end, a metric set is designed according to not only the maximum tolerable delay/Doppler, but also the weighted spectral efficiency as well as the outage/error probability of sensing and communications. This enables the analytical investigation of the performance trade-offs of AFDM-ISAC system using the derived analytical relation among metrics and AFDM waveform parameters. Moreover, by revealing that delay and the integral/fractional parts of normalized Doppler can be decoupled in the affine Fourier transform-Doppler domain, an efficient estimation method is proposed for our AFDM-ISAC system, whose unambiguous Doppler can break through the limitation of subcarrier spacing. Theoretical analyses and numerical results verify that our proposed AFDM-ISAC system may significantly enlarge unambiguous delay/Doppler while possessing good spectral efficiency and peak-to-sidelobe level ratio in high-mobility scenarios.
Uplink Rate Splitting Multiple Access with Imperfect Channel State Information and Interference Cancellation
Authors: Farjam Karim, Nurul Huda Mahmood, Arthur S. de Sena, Deepak Kumar, Bruno Clerckx, Matti Latva-aho
Date: 2025-01-31 00:00:00

This article investigates the performance of uplink rate splitting multiple access (RSMA) in a two-user scenario, addressing an under-explored domain compared to its downlink counterpart. With the increasing demand for uplink communication in applications like the Internet-of-Things, it is essential to account for practical imperfections, such as inaccuracies in channel state information at the receiver (CSIR) and limitations in successive interference cancellation (SIC), to provide realistic assessments of system performance. Specifically, we derive closed-form expressions for the outage probability, throughput, and asymptotic outage behavior of uplink users, considering imperfect CSIR and SIC. We validate the accuracy of these derived expressions using Monte Carlo simulations. Our findings reveal that at low transmit power levels, imperfect CSIR significantly affects system performance more severely than SIC imperfections. However, as the transmit power increases, the impact of imperfect CSIR diminishes, while the influence of SIC imperfections becomes more pronounced. Moreover, we highlight the impact of the rate allocation factor on user performance. Finally, our comparison with non-orthogonal multiple access (NOMA) highlights the outage performance trade-offs between RSMA and NOMA. RSMA proves to be more effective in managing imperfect CSIR and enhances performance through strategic message splitting, resulting in more robust communication.
RIS Meets O-RAN: A Practical Demonstration of Multi-user RIS Optimization through RIC
Authors: Ali Fuat Sahin, Onur Salan, Ibrahim Hokelek, Ali Gorcin
Date: 2025-01-31 00:00:00
Tasks: Fairness

Open Radio Access Network (O-RAN) along with artificial intelligence, machine learning, cloud and edge networking, and virtualization are important enablers for designing flexible and software-driven programmable wireless networks. In addition, Reconfigurable Intelligent Surfaces (RIS) represent an innovative technology to direct incoming radio signals toward desired locations by software-controlled passive reflecting antenna elements. Despite their distinctive potential, there has been limited exploration of integrating RIS with the O-RAN framework, an area that holds promise for enhancing next-generation wireless systems. This paper addresses this gap by designing and developing the RIS optimization xApps within an O-RAN-based real-time 5G environment. We perform extensive measurement experiments using an end-to-end 5G testbed including the RIS prototype in a multi-user scenario. The results demonstrate that the RIS can be utilized either to boost the performance of the selected user or to provide the fairness among the users or to balance the tradeoff between the performance and fairness.
Integrated Sensing and Communication System Based on Radio Frequency Resonance Beam
Authors: Yixuan Guo, Shuaifan Xia, Mingliang Xiong, Qingwen Liu, Wen Fang, Qingwei Jiang, Gang Yan, Jiangchuan Mu
Date: 2025-01-31 00:00:00
Tasks: Integrated sensing and communication, ISAC

To address the complex beam control in traditional multiple-input multiple-output (MIMO) systems, researchers have proposed adaptive beam alignment using retro-directive antenna (RDA) arrays. This approach creates echo resonance between the base station (BS) and user equipment (UE), significantly reducing computational load. However, conventional resonant beam systems (RBS) suffer from echo interference due to the shared uplink and downlink frequency. Therefore, this paper proposes an innovative resonance beam-based integrated sensing and communication (RB-ISAC) system designed for efficient passive sensing and bidirectional communication. In this system, the UE operates passively, with both the BS and UE utilizing a phase conjugation and frequency conversion structure to decouple uplink and downlink carrier frequencies, ensuring continuous electromagnetic wave oscillation between the two ends. Effective compensation for signal propagation loss enables resonance after multiple oscillations. At this point, the beam's field forms a low-diffraction-loss, highly focused pattern, automatically aligning the transmitter and receiver. This enables high-precision passive positioning alongside robust uplink and downlink communication. Simulation results demonstrate the proposed system achieves resonance within multiple iterations, supporting uplink and downlink communication up to 5 m, and enabling passive direction of arrival (DOA) estimation with an error under 2$^\circ$ .
SDM Optical Systems with MMSE Equalizers: Information Rates and Performance Monitoring
Authors: Lucas Alves Zischler, Darli A. A. Mello
Date: 2025-01-30 00:00:00

The information rate of coupled space-division multiplexing (SDM) transmission systems is impaired by the stochastic effects of mode-dependent gain (MDG) and mode-dependent loss (MDL), turning it into a random variable and reducing its average value. In systems operating with minimum mean squared error (MMSE) equalizers and no channel-state information (CSI), co-channel interference further reduces the instantaneous and average information rates. Analytical solutions for the average information rate in MDG- and MDL-impaired systems under strong coupling have been presented in early studies assuming ideal maximum-likelihood (ML) equalization. However, to the best of our knowledge, a solution encompassing co-channel interference under MMSE equalization has not been presented yet. In this work, we derive statistical models for the MMSE equalizer coefficients and develop analytical solutions for the post-filtering information rate. We also use these statistical models and analytical solutions to carry out MDG and signal-to-noise ratio (SNR) monitoring in coupled SDM systems. The derived analytical solutions and monitoring techniques are validated by Monte-Carlo simulations, exhibiting a suitable accuracy within practical operational values.
Beamforming with Oversampled Time-Modulated Arrays
Authors: Marcin Wachowiak, André Bourdoux, Sofie Pollin
Date: 2025-01-30 00:00:00

The time-modulated array (TMA) is a simple array architecture in which each antenna is connected via a multi-throw switch. The switch acts as a modulator switching state faster than the symbol rate. The phase shifting and beamforming is achieved by a cyclic shift of the periodical modulating signal across antennas. In this paper, the TMA mode of operation is proposed to improve the resolution of a conventional phase shifter. The TMAs are analyzed under constrained switching frequency being a small multiple of the symbol rate. The presented generic signal model gives insight into the magnitude, phase and spacing of the harmonic components generated by the quantized modulating sequence. It is shown that the effective phase-shifting resolution can be improved multiplicatively by the oversampling factor ($O$) at the cost of introducing harmonics. Finally, the array tapering with an oversampled modulating signal is proposed. The oversampling provides $O+1$ uniformly distributed tapering amplitudes.
Frequency Diverse Array OFDM System for Joint Communication and Sensing
Authors: Marcin Wachowiak, André Bourdoux, Sofie Pollin
Date: 2025-01-30 00:00:00

The frequency-diverse array (FDA) offers a time-varying beamforming capability without the use of phase shifters. The autoscanning property is achieved by applying a frequency offset between the antennas. This paper analyzes the performance of an FDA joint communication and sensing system with the orthogonal frequency-division multiplexing (OFDM) modulation. The performance of the system is evaluated against the scanning frequency, number of antennas and number of subcarriers. The utilized metrics; integrated sidelobe level (ISL) and error vector magnitude (EVM) allow for straightforward comparison with a standard single-input single-output (SISO) OFDM system.
Tuning Event Camera Biases Heuristic for Object Detection Applications in Staring Scenarios
Authors: David El-Chai Ben-Ezra, Daniel Brisk
Date: 2025-01-30 00:00:00
Tasks: object-detection, Object Detection

One of the main challenges in unlocking the potential of neuromorphic cameras, also called 'event cameras', is the development of novel methods that solve the multi-parameter problem of adjusting their bias parameters to accommodate a desired task. Actually, it is very difficult to find in the literature a systematic heuristic that solves the problem for any desired application. In this paper we present a tuning parametes heuristic for the biases of event cameras, for tasks that require small objects detection in staring scenarios. The main purpose of the heuristic is to squeeze the camera's potential, optimize its performance, and expand its detection capabilities as much as possible. In the presentation, we translate the experimental properties of event camera and systemic constrains into mathematical terms, and show, under certain assumptions, how the multi-variable problem collapses into a two-parameter problem that can be solved experimentally. A main conclusion that will be demonstrated is that for certain desired signals, such as the one provided by an incandescent lamp powered by the periodic electrical grid, the optimal values of the camera are very far from the default values recommended by the manufacturer.
Nonsuppressible viremia during HIV-1 therapy meets molecular virology
Authors: Xiangshuai Liu, Jacob Sznajder, Sergey Troyanovsky, Thomas Hope
Date: 2025-01-31 00:00:00
Tasks: Virology

HIV-1 replication can be suppressed with antiretroviral therapy (ART), but individuals who stop taking ART soon become viremic again. Some people experience extended times of detectable viremia despite optimal adherence to ART. In the issue of the JCI, White, Wu, and coauthors elucidate a source of nonsuppressible viremia (NSV) in treatment-adherent patients clonally expanded T cells harboring HIV-1 proviruses with small deletions or mutations in the 5'-leader, the UTR that includes the major splice donor site of viral RNA. These mutations altered viral RNA-splicing efficiency and RNA dimerization and packaging, yet still allowed production of detectable levels of noninfectious virus particles. These particles lacked the HIV-1 Env surface protein required for cell entry and failed to form the mature capsid cone required for infectivity. These studies improve our understanding of NSV and the regulation of viral functions in the 5'-leader with implications for rationalized care in individuals with NSV.
A network-driven framework for enhancing gene-disease association studies in coronary artery disease
Authors: Gutama Ibrahim Mohammad, Johan LM Björkegren, Tom Michoel
Date: 2025-01-31 00:00:00

Over the last decade, genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with complex diseases. These associations have the potential to reveal the molecular mechanisms underlying complex diseases and lead to the identification of novel drug targets. Despite these advancements, the biological pathways and mechanisms linking genetic variants to complex diseases are still not fully understood. Most trait-associated variants reside in non-coding regions and are presumed to influence phenotypes through regulatory effects on gene expression. Yet, it is often unclear which genes they regulate and in which cell types this regulation occurs. Transcriptome-wide association studies (TWAS) aim to bridge this gap by detecting trait-associated tissue gene expression regulated by GWAS variants. However, traditional TWAS approaches frequently overlook the critical contributions of trans-regulatory effects and fail to integrate comprehensive regulatory networks. Here, we present a novel framework that leverages tissue-specific gene regulatory networks (GRNs) to integrate cis- and trans-genetic regulatory effects into the TWAS framework for complex diseases. We validate our approach using coronary artery disease (CAD), utilizing data from the STARNET project, which provides multi-tissue gene expression and genetic data from around 600 living patients with cardiovascular disease. Preliminary results demonstrate the potential of our GRN-driven framework to uncover more genes and pathways that may underlie CAD. This framework extends traditional TWAS methodologies by utilizing tissue-specific regulatory insights and advancing the understanding of complex disease genetic architecture.
The role of oscillations in grid cells' toroidal topology
Authors: Giovanni di Sarra, Siddharth Jha, Yasser Roudi
Date: 2025-01-31 00:00:00

Persistent homology applied to the activity of grid cells in the Medial Entorhinal Cortex suggests that this activity lies on a toroidal manifold. By analyzing real data and a simple model, we show that neural oscillations play a key role in the appearance of this toroidal topology. To quantitatively monitor how changes in spike trains influence the topology of the data, we first define a robust measure for the degree of toroidality of a dataset. Using this measure, we find that small perturbations ($\sim$ 100 ms) of spike times have little influence on both the toroidality and the hexagonality of the ratemaps. Jittering spikes by $\sim$ 100-500 ms, however, destroys the toroidal topology, while still having little impact on grid scores. These critical jittering time scales fall in the range of the periods of oscillations between the theta and eta bands. We thus hypothesized that these oscillatory modulations of neuronal spiking play a key role in the appearance and robustness of toroidal topology and the hexagonal spatial selectivity is not sufficient. We confirmed this hypothesis using a simple model for the activity of grid cells, consisting of an ensemble of independent rate-modulated Poisson processes. When these rates were modulated by oscillations, the network behaved similarly to the real data in exhibiting toroidal topology, even when the position of the fields were perturbed. In the absence of oscillations, this similarity was substantially lower. Furthermore, we find that the experimentally recorded spike trains indeed exhibit temporal modulations at the eta and theta bands, and that the ratio of the power in the eta band to that of the theta band, $A_{\eta}/A_{\theta}$, correlates with the critical jittering time at which the toroidal topology disappears.
Subtle variations in stiff dimensions of brain networks account for individual differences in cognitive ability
Authors: Sida Chen, Qianyuan Tang, Taro Toyoizumi, Werner Sommer, Lianchun Yu, Changsong Zhou
Date: 2025-01-31 00:00:00

Explaining individual differences in cognitive abilities requires both identifying brain parameters that vary across individuals and understanding how brain networks are recruited for specific tasks. Typically, task performance relies on the integration and segregation of functional subnetworks, often captured by parameters like regional excitability and connectivity. Yet, the high dimensionality of these parameters hinders pinpointing their functional relevance. Here, we apply stiff-sloppy analysis to human brain data, revealing that certain subtle parameter combinations ("stiff dimensions") powerfully influence neural activity during task processing, whereas others ("sloppy dimensions") vary more extensively but exert minimal impact. Using a pairwise maximum entropy model of task fMRI, we show that even small deviations in stiff dimensions-derived through Fisher Information Matrix analysis-govern the dynamic interplay of segregation and integration between the default mode network (DMN) and a working memory network (WMN). Crucially, separating a 0-back task (vigilant attention) from a 2-back task (working memory updating) uncovers partially distinct stiff dimensions predicting performance in each condition, along with a global DMN-WMN segregation shared across both tasks. Altogether, stiff-sloppy analysis challenges the conventional focus on large parameter variability by highlighting these subtle yet functionally decisive parameter combinations.
Multilayer Networks in Neuroimaging
Authors: Vesna Vuksanovic
Date: 2025-01-31 00:00:00

Recent advances in network science, applied to \textit{in vivo} brain recordings, have paved the way for better understanding of the structure and function of the brain. However, despite its obvious usefulness in neuroscience, traditional network science lacks tools for -- so important -- simultaneous investigation of the inter-relationship between the two domains. In this chapter, I explore the increasing role of multilayer networks in building brain generative models and abilities of such models to uncover the full information about the brain complex spatiotemporal interactions that span across multiple scales and modalities. First, I begin with the theoretical foundation of brain networks accompanied by a brief overview of traditional networks and their role in constructing multilayer network models. Then, I delve into the applications of multilayer networks in neuroscience, particularly in deciphering structure-function relationship, modelling diseases, and integrating multi-scale and multi-modal data. Finally, I demonstrate how incorporating the multilayer framework into network neuroscience has brought to light previously hidden features of brain networks and, how multilayer networks can provide new insights and a description of the structure and function of the brain.
Dissociated Neuronal Cultures as Model Systems for Self-Organized Prediction
Authors: Amit Yaron, Zhuo Zhang, Dai Akita, Tomoyo Isoguchi Shiramatsu, Zenas Chao, Hirokazu Takahashi
Date: 2025-01-30 00:00:00

Dissociated neuronal cultures provide a simplified yet effective model system for investigating self-organized prediction and information processing in neural networks. This review consolidates current research demonstrating that these in vitro networks display fundamental computational capabilities, including predictive coding, adaptive learning, goal-directed behavior, and deviance detection. We examine how these cultures develop critical dynamics optimized for information processing, detail the mechanisms underlying learning and memory formation, and explore the relevance of the free energy principle within these systems. Building on these insights, we discuss how findings from dissociated neuronal cultures inform the design of neuromorphic and reservoir computing architectures, with the potential to enhance energy efficiency and adaptive functionality in artificial intelligence. The reduced complexity of neuronal cultures allows for precise manipulation and systematic investigation, bridging theoretical frameworks with practical implementations in bio-inspired computing. Finally, we highlight promising future directions, emphasizing advancements in three-dimensional culture techniques, multi-compartment models, and brain organoids that deepen our understanding of hierarchical and predictive processes in both biological and artificial systems. This review aims to provide a comprehensive overview of how dissociated neuronal cultures contribute to neuroscience and artificial intelligence, ultimately paving the way for biologically inspired computing solutions.