pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Beamforming Design for Multi-RIS-Aided MU-MIMO Systems with Imperfect CSIT
Authors: Mintaek Oh, Jinseok Choi
Date: 2025-01-02 00:00:00

A reconfigurable intelligent surface (RIS) has emerged as a promising solution for enhancing the capabilities of wireless communications. This paper presents a scalable beamforming design for maximizing the spectral efficiency (SE) of multi-RIS-aided communications through joint optimization of the precoder and RIS phase shifts in multi-user multiple-input multiple-output (MU-MIMO) systems under imperfect channel state information at the transmitter (CSIT). To address key challenges of the joint optimization problem, we first decompose it into two subproblems with deriving a proper lower bound. We then leverage a generalized power iteration (GPI) approach to identify a superior local optimal precoding solution. We further extend this approach to the RIS design using regularization; we set a RIS regularization function to efficiently handle the unitmodulus constraints, and also find the superior local optimal solution for RIS phase shifts under the GPI-based optimization framework. Subsequently, we propose an alternating optimization method. In particular, utilizing the block-diagonal structure of the matrices the GPI method, the proposed algorithm offers multi-RIS scalable beamforming as well as superior SE performance. Simulations validate the proposed method in terms of both the sum SE performance and the scalability.
Rethink Delay Doppler Channels and Time-Frequency Coding
Authors: Xiang-Gen Xia
Date: 2024-12-31 00:00:00
Tasks: Math

In this paper, we rethink delay Doppler channels (also called doubly selective channels). We prove that no modulation schemes (including the current active VOFDM/OTFS) can compensate a non-trivial Doppler spread well. We then discuss some of the existing methods to deal with time-varying channels, in particular time-frequency (TF) coding in an OFDM system. TF coding is equivalent to space-time coding in the math part. We also summarize state of the art on space-time coding that was an active research topic over a decade ago.
NeuroSleepNet: A Multi-Head Self-Attention Based Automatic Sleep Scoring Scheme with Spatial and Multi-Scale Temporal Representation Learning
Authors: Muhammad Sudipto Siam Dip, Mohammod Abdul Motin, Chandan Karmakar, Thomas Penzel, Marimuthu Palaniswami
Date: 2024-12-31 00:00:00
Tasks: Computational Efficiency, Representation Learning

Objective: Automatic sleep scoring is crucial for diagnosing sleep disorders. Existing frameworks based on Polysomnography often rely on long sequences of input signals to predict sleep stages, which can introduce complexity. Moreover, there is limited exploration of simplifying representation learning in sleep scoring methods. Methods: In this study, we propose NeuroSleepNet, an automatic sleep scoring method designed to classify the current sleep stage using only the microevents in the current input signal, without the need for past inputs. Our model employs supervised spatial and multi-scale temporal context learning and incorporates a transformer encoder to enhance representation learning. Additionally, NeuroSleepNet is optimized for balanced performance across five sleep stages by introducing a logarithmic scale-based weighting technique as a loss function. Results: NeuroSleepNet achieved similar and comparable performance with current state-of-the-art results. The best accuracy, macro-F1 score, and Cohen's kappa were 86.1 percent, 80.8 percent, and 0.805 for Sleep-EDF expanded; 82.0 percent, 76.3 percent, and 0.753 for MESA; 80.5 percent, 76.8 percent, and 0.738 for Physio2018; and 86.7 percent, 80.9 percent, and 0.804 for the SHHS database. Conclusion: NeuroSleepNet demonstrates that even with a focus on computational efficiency and a purely supervised learning approach, it is possible to achieve performance that is comparable to state-of-the-art methods. Significance: Our study simplifies automatic sleep scoring by focusing solely on microevents in the current input signal while maintaining remarkable performance. This offers a streamlined alternative for sleep diagnosis applications.
Jointly optimal array geometries and waveforms in active sensing: New insights into array design via the Cramér-Rao bound
Authors: Ids van der Werf, Robin Rajamäki, Geert Leus
Date: 2024-12-31 00:00:00

This paper investigates jointly optimal array geometry and waveform designs for active sensing. Specifically, we focus on minimizing the Cram\'er-Rao lower bound (CRB) of the angle of a single target in white Gaussian noise. We first find that several array-waveform pairs can yield the same CRB by virtue of sequences with equal sums of squares, i.e., solutions to certain Diophantine equations. Furthermore, we show that under physical aperture and sensor number constraints, the CRB-minimizing receive array geometry is unique, whereas the transmit array can be chosen flexibly. We leverage this freedom to design a novel sparse array geometry that not only minimizes the single-target CRB given an optimal waveform, but also has a nonredundant and contiguous sum co-array, a desirable property when launching independent waveforms, with relevance also to the multi-target case.
SLoG-Net: Algorithm Unrolling for Source Localization on Graphs
Authors: Chang Ye, Gonzalo Mateos
Date: 2024-12-31 00:00:00

We present a novel model-based deep learning solution for the inverse problem of localizing sources of network diffusion. Starting from first graph signal processing (GSP) principles, we show that the problem reduces to joint (blind) estimation of the forward diffusion filter and a sparse input signal that encodes the source locations. Despite the bilinear nature of the observations in said blind deconvolution task, by requiring invertibility of the diffusion filter we are able to formulate a convex optimization problem and solve it using the alternating-direction method of multipliers (ADMM). We then unroll and truncate the novel ADMM iterations to arrive at a parameterized neural network architecture for Source Localization on Graphs (SLoG-Net), that we train in an end-to-end fashion using labeled data. This supervised learning approach offers several advantages such as interpretability, parameter efficiency, and controllable complexity during inference. Our reproducible numerical experiments corroborate that SLoG-Net exhibits performance on par with the iterative ADMM baseline, but with markedly faster inference times and without needing to manually tune step-size or penalty parameters. Overall, our approach combines the best of both worlds by incorporating the inductive biases of a GSP model-based solution within a data-driven, trainable deep learning architecture for blind deconvolution of graph signals.
Precoder Implementation and Optimization in 5G NR Massive MIMO Radio
Authors: Kalyani Bhukya, Shahid Aamir Sheikh, Radha Krishna Ganti
Date: 2024-12-31 00:00:00

The evolution of 5G New Radio (NR) has brought significant improvements in signal strength and service quality for users. By integrating Multiple Input Multiple Output (MIMO) systems into communications, multiple data streams can be transmitted simultaneously across multiple antennas. Additionally, the incorporation of precoding in MIMO systems enables enhanced data rates and spectral efficiency. In wireless networks, precoders are used to steer high-gain beams intended for specific users. This paper focuses on the implementation of 16, 32, and 64 channel linear precoders in the Remote Radio Head (RRH) of the indigenously developed 5G testbed at IIT Madras. These precoders include a memory module to store channel matrices and a multiplier module to perform matrix multiplications between the channel matrices and user data within a slot duration of 500 microseconds. The system demonstrates DSP utilization levels of 9.75%, 19.5%, and 39% for (16 x 8), (32 x 8), and (64 x 8) antenna-layer configurations, respectively, while maintaining Block RAM (BRAM) usage within 2.28%, 3.91%, and 7.16%. Additionally, a throughput of 1.2 Gbps with four active layers highlights the system's optimized performance under hardware constraints.
Quantum-MUSIC: Multiple Signal Classification for Quantum Wireless Sensing
Authors: Hanvit Kim, Hyunwoo Park, Sunwoo Kim
Date: 2024-12-31 00:00:00

This paper proposes a Quantum-MUSIC, the first multiple signal classification (MUSIC) algorithm for quantum wireless sensing of multi-user. Since an atomic receiver for quantum wireless sensing can only measure the magnitude of a received signal, sensing performance degradation of traditional antenna-based signal processing algorithms is inevitable. To overcome this limitation, the proposed algorithm recovers the channel information and incorporates the traditional MUSIC algorithm, enabling the sensing of multi-user with magnitude-only measurement. Simulation results showed that the proposed algorithm outperforms the existing MUSIC algorithm, validating the superior potential of quantum wireless sensing.
Multipath Component-Aided Signal Processing for Integrated Sensing and Communication Systems
Authors: Haotian Liu, Zhiqing Wei, Xiyang Wang, Yangyang Niu, Yixin Zhang, Huici Wu, Zhiyong Feng
Date: 2024-12-31 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has emerged as a pivotal enabling technology for sixth-generation (6G) mobile communication system. The ISAC research in dense urban areas has been plaguing by severe multipath interference, propelling the thorough research of ISAC multipath interference elimination. However, transforming the multipath component (MPC) from enemy into friend is a viable and mutually beneficial option. In this paper, we preliminarily explore the MPC-aided ISAC signal processing and apply a space-time code to improve the ISAC performance. Specifically, we propose a symbol-level fusion for MPC-aided localization (SFMC) scheme to achieve robust and high-accuracy localization, and apply a Khatri-Rao space-time (KRST) code to improve the communication and sensing performance in rich multipath environment. Simulation results demonstrate that the proposed SFMC scheme has more robust localization performance with higher accuracy, compared with the existing state-of-the-art schemes. The proposed SFMC would benefit highly reliable communication and sub-meter level localization in rich multipath scenarios.
Linear Precoding Design for OTFS Systems in Time/Frequency Selective Fading Channels
Authors: Yao Ge, Lingsheng Meng, David González G., Miaowen Wen, Yong Liang Guan, Pingzhi Fan
Date: 2024-12-31 00:00:00
Tasks: Diversity

Even orthogonal time frequency space (OTFS) has been shown as a promising modulation scheme for high mobility doubly-selective fading channels, its attainability of full diversity order in either time or frequency selective fading channels has not been clarified. By performing pairwise error probability (PEP) analysis, we observe that the original OTFS system can not always guarantee full exploitation of the embedded diversity in either time or frequency selective fading channels. To address this issue and further improve system performance, this work proposes linear precoding solutions based on algebraic number theory for OTFS systems over time and frequency selective fading channels, respectively. The proposed linear precoded OTFS systems can guarantee the maximal diversity and potential coding gains in time/frequency selective fading channels without any transmission rate loss and do not require the channel state information (CSI) at the transmitter. Simulation results are finally provided to illustrate the superiority of our proposed precoded OTFS over both the original unprecoded and the existing phase rotation OTFS systems in time/frequency selective fading channels.
Hierarchical Functionality Prioritization in Multicast ISAC: Optimal Admission Control and Discrete-Phase Beamforming
Authors: Luis F. Abanto-Leon, Setareh Maghsudi
Date: 2024-12-31 00:00:00
Tasks: ISAC

We investigate the joint admission control and discrete-phase multicast beamforming design for integrated sensing and communications (ISAC) systems, where sensing and communications functionalities have different hierarchies. Specifically, the ISAC system first allocates resources to the higher-hierarchy functionality and opportunistically uses the remaining resources to support the lower-hierarchy one. This resource allocation problem is a nonconvex mixed-integer nonlinear program (MINLP). We propose an exact mixed-integer linear program (MILP) reformulation, leading to a globally optimal solution. In addition, we implemented three baselines for comparison, which our proposed method outperforms by more than 39%.
Model of an Open, Decentralized Computational Network with Incentive-Based Load Balancing
Authors: German Rodikov
Date: 2025-01-02 00:00:00

This paper proposes a model that enables permissionless and decentralized networks for complex computations. We explore the integration and optimize load balancing in an open, decentralized computational network. Our model leverages economic incentives and reputation-based mechanisms to dynamically allocate tasks between operators and coprocessors. This approach eliminates the need for specialized hardware or software, thereby reducing operational costs and complexities. We present a mathematical model that enhances restaking processes in blockchain systems by enabling operators to delegate complex tasks to coprocessors. The model's effectiveness is demonstrated through experimental simulations, showcasing its ability to optimize reward distribution, enhance security, and improve operational efficiency. Our approach facilitates a more flexible and scalable network through the use of economic commitments, adaptable dynamic rating models, and a coprocessor load incentivization system. Supported by experimental simulations, the model demonstrates its capability to optimize resource allocation, enhance system resilience, and reduce operational risks. This ensures significant improvements in both security and cost-efficiency for the blockchain ecosystem.
Minimal Models for RNA Simulations
Authors: D. Thirumalai, Naoto Hori, Hung T. Nguyen
Date: 2024-12-31 00:00:00

The increasing importance of RNA as a prime player in biology can hardly be overstated. Problems in RNA, such as folding and RNA-RNA interactions that drive phase separation, require cations. Because experiments alone cannot reveal the dynamics of cation-RNA interactions, well calibrated theory and computations are needed to predict how ions control the behavior of RNA. The perspective describes the development and use of coarse-grained models at different resolutions. We focus on single- and three-interaction site interaction models, in which electrostatic interactions are treated using a combination of explicit and implicit representations. Applications to the folding of ribozymes and riboswitches are discussed, with emphasis on the role of monovalent and divalent cations. We also discuss phase separation in low complexity sequences. Challenges in the simulation of complex problems such as ribosome assembly and RNA chaperones, requiring developments of models for RNA-protein interactions, are pointed out.
Infectious diseases, imposing density-dependent mortality on MHC/HLA variation, can account for balancing selection and MHC/HLA polymorphism
Authors: D. P. L. Green
Date: 2025-01-01 00:00:00

The human MHC transplantation loci (HLA-A, -B, -C, -DPB1, -DQB1, -DRB1) are the most polymorphic in the human genome. It is generally accepted this polymorphism reflects a role in presenting pathogen-derived peptide to the adaptive immune system. Proposed mechanisms for the polymorphism such as negative frequency-dependent selection (NFDS) and heterozygote advantage (HA) focus on HLA alleles, not haplotypes. Here, we propose a model for the polymorphism in which infectious diseases impose independent density-dependent regulation on HLA haplotypes. More specifically, a complex pathogen environment drives extensive host polymorphism through a guild of HLA haplotypes that are specialised and show incomplete peptide recognition. Separation of haplotype guilds is maintained by limiting similarity. The outcome is a wide and stable range of haplotype densities at steady-state in which effective Fisher fitnesses are zero. Densities, and therefore frequencies, emerge theoretically as alternative measures of fitness. A catalogue of ranked frequencies is therefore one of ranked fitnesses. The model is supported by data from a range of sources including a Caucasian HLA dataset compiled by the US National Marrow Donor Program (NMDP). These provide evidence of positive selection on the top 350-2000 5-locus HLA haplotypes taken from an overall NMDP sample set of 10E5. High-fitness haplotypes drive the selection of 137 high-frequency alleles spread across the 5 HLA loci under consideration. These alleles demonstrate positive epistasis and pleiotropy in the formation of haplotypes. Allelic pleiotropy creates a network of highly inter-related HLA haplotypes that account for 97% of the census sample. We suggest this network has properties of a quasi-species and is itself under selection. We also suggest this is the origin of balancing selection in the HLA system.
Towards an integrative approach to the study of brain-environment interactions in human and non-human primate
Authors: David Thura
Date: 2025-01-01 00:00:00

By retracing my scientific journey that began 20 years ago, I highlight in this thesis the need to consider the organization of the brain, which is certainly globally hierarchical, but also highly distributed and mixed in the neuronal response of its different areas (by focusing on the sensorimotor cortex-basal ganglia network). I also emphasize the importance of adopting behavioral paradigms that reflect as much as possible the characteristics of the scenarios encountered in the real life of animals. And finally, I mention the importance of "disintegrating" the way data analysis is traditionally carried out, and of taking into account the dynamic nature of behavior, for example by favoring the study of behavioral variables and so-called "latent" neuronal activities. I conclude this thesis by presenting the vision of my ideal laboratory in a perspective of 5 to 10 years from today. This laboratory would have two experimental devices, a first, classic, for carrying out tests in a constrained and therefore unnatural environment, the data of which would be compared to those from a second device called "naturalistic" in which the animals could potentially express their entire behavioral repertoire. The tasks tested would be characterized by strong ecological principles, such as in those simulating the properties of foraging, and the data would make it possible to test hypotheses based on the progressive addition of ecological components in these tasks, all this in order to maintain control over the interpretability of these data which are complex by nature.
Realistic overground gait transitions are not sharp but involve gradually changing walk-run mixtures as per energy optimality
Authors: Nicholas S. Baker, Leroy Long, Manoj Srinivasan
Date: 2025-01-01 00:00:00

Humans use two qualitatively different gaits for locomotion, namely, walking and running -- usually using walking at lower speeds and running at higher speeds. Researchers have examined when humans switch between walking and running on treadmills and have noted hystereses in these gait transition speeds. Here, we consider an ecologically realistic overground locomotion task, one requiring traveling a given long distance (800 meters or 2400 meters) in a prescribed time duration. Unlike on a treadmill, this task allows the human to change speed or gait during the trial to reach the destination on time: this task is akin to traveling to an appointment at a particular time from your office to another office, arriving neither early or late. We find that gait transition is not sharp, but instead involves a 'gait transition regime' in which humans use a mixture of walking and running, using mostly walking atlower speeds and mostly running higher speeds -- supporting earlier results over short distances (120 m). The presence of this gradually changing walk-run mixture is predicted by energy optimality. We hypothesize that this energy optimal behavior in this realistic overground conditions accounts for the hysteretic behavior in treadmill experiments, apparently switching earlier than predicted by energy optimality.
The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies
Authors: Alessandro T. Gifford, Domenic Bersch, Marie St-Laurent, Basile Pinsard, Julie Boyle, Lune Bellec, Aude Oliva, Gemma Roig, Radoslaw M. Cichy
Date: 2024-12-31 00:00:00

There is growing symbiosis between artificial and biological intelligence sciences: neural principles inspire new intelligent machines, which are in turn used to advance our theoretical understanding of the brain. To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject.com/). In collaboration with the Courtois Project on Neuronal Modelling (CNeuroMod), this edition aims to bring forth a new generation of brain encoding models that are multimodal and that generalize well beyond their training distribution, by training them on the largest dataset of fMRI responses to movie watching available to date. Open to all, the 2025 challenge provides transparent, directly comparable results through a public leaderboard that is updated automatically after each submission to facilitate rapid model assessment and guide development. The challenge will end with a session at the 2025 Cognitive Computational Neuroscience (CCN) conference that will feature winning models. We welcome researchers interested in collaborating with the Algonauts Project by contributing ideas and datasets for future challenges.
Higher serum 25(OH)D concentration is associated with lower risk of metabolic syndrome among Aboriginal and Torres Strait Islander peoples in Australia
Authors: Belinda Neo, Dale Tilbrook, Noel Nannup, John Jacky, Carol Michie, Cindy Prior, Eleanor Dunlop, Brad Farrant, Won Sun Chen, Carrington C. J. Shepherd, Lucinda J. Black, .
Date: 2025-01-02 00:00:00

Although previous observational studies have shown associations between serum 25-hydroxyvitamin D (25(OH)D) concentration and metabolic syndrome, this association has not yet been investigated among Aboriginal and Torres Strait Islander peoples. We aimed to investigate the association between serum 25(OH)D concentration and metabolic syndrome and its risk factors in this population group. We used cross-sectional data from the 2012-2013 Australian Aboriginal and Torres Strait Islander Health Survey. Metabolic syndrome is defined as having 3 or more risk factors: elevated waist circumference, elevated triglycerides, low high-density lipoprotein (HDL) cholesterol, elevated blood pressure, or elevated fasting blood glucose. We used binomial logistic regression to test associations between serum 25(OH)D concentration and metabolic syndrome, and multiple linear regression to test associations between serum 25(OH)D concentration and each risk factor. We included the following covariates: age, sex, smoking status, education level, socio-economic status, remoteness of location, season, and body mass index (BMI). After adjusting for covariates, we found that each 10 nmol/L increase in serum 25(OH)D concentration was statistically significantly associated with a 16% lower risk of metabolic syndrome (odds ratio: 0.84, 95% confidence interval: 0.76, 0.92) and a 2.1 cm (95% confidence interval: 1.65, 2.57) lower waist circumference (BMI was not included in the model for waist circumference). We found small inverse associations between serum 25(OH)D concentration and all other risk factors except systolic blood pressure. Given that higher serum 25(OH)D concentration may confer metabolic health benefits, promoting vitamin D sufficiency may be beneficial for this population.