pwc-archive/papers-with-abstracts huggingface dataset:
Three mechanistically different variability and noise sources in the trial-to-trial fluctuations of responses to brain stimulation
Authors: Ke Ma, Siwei Liu, Mengjie Qin, Stefan Goetz
Date: 2024-12-22 00:00:00

Motor-evoked potentials (MEPs) are among the few directly observable responses to external brain stimulation and serve a variety of applications, often in the form of input-output (IO) curves. Previous statistical models with two variability sources inherently consider the small MEPs at the low-side plateau as part of the neural recruitment properties. However, recent studies demonstrated that small MEP responses under resting conditions are contaminated and over-shadowed by background noise of mostly technical quality, e.g., caused by the amplifier, and suggested that the neural recruitment curve should continue below this noise level. This work intends to separate physiological variability from background noise and improve the description of recruitment behaviour. We developed a triple-variability-source model around a logarithmic logistic function without a lower plateau and incorporated an additional source for background noise. Compared to models with two or fewer variability sources, our approach better described IO characteristics, evidenced by lower Bayesian Information Criterion scores across all subjects and pulse shapes. The model independently extracted hidden variability information across the stimulated neural system and isolated it from background noise, which led to an accurate estimation of the IO curve parameters. This new model offers a robust tool to analyse brain stimulation IO curves in clinical and experimental neuroscience and reduces the risk of spurious results from inappropriate statistical methods. The presented model together with the corresponding calibration method provides a more accurate representation of MEP responses and variability sources, advances our understanding of cortical excitability, and may improve the assessment of neuromodulation effects.
Distributed Cooperative Positioning in Dense Wireless Networks: A Neural Network Enhanced Fast Convergent Parametric Message Passing Method
Authors: Yue Cao, Shaoshi Yang, Zhiyong Feng
Date: 2024-12-22 00:00:00
Tasks: Graph Neural Network

Parametric message passing (MP) is a promising technique that provides reliable marginal probability distributions for distributed cooperative positioning (DCP) based on factor graphs (FG), while maintaining minimal computational complexity. However, conventional parametric MP-based DCP methods may fail to converge in dense wireless networks due to numerous short loops on FG. Additionally, the use of inappropriate message approximation techniques can lead to increased sensitivity to initial values and significantly slower convergence rates. To address the challenging DCP problem modeled by a loopy FG, we propose an effective graph neural network enhanced fast convergent parametric MP (GNN--FCPMP) method. We first employ Chebyshev polynomials to approximate the nonlinear terms present in the FG-based spatio-temporal messages. This technique facilitates the derivation of globally precise, closed-form representations for each message transmitted across the FG. Then, the parametric representations of spatial messages are meticulously refined through data-driven graph neural networks (GNNs). Conclusively, by performing inference on the FG, we derive more accurate closed-form expressions for the a posteriori distributions of node positions. Numerical results substantiate the capability of GNN--FCPMP to significantly enhance positioning accuracy within wireless networks characterized by high-density loops and ensure rapid convergence.
Optical Wireless Communications: Enabling the Next Generation Network of Networks
Authors: Aravindh Krishnamoorthy, Hossein Safi, Othman Younus, Hossein Kazemi, Isaac N. O. Osahon, Mingqing Liu, Yi Liu, Sina Babadi, Rizwana Ahmad, Asim Ihsan, Behnaz Majlesein, Yifan Huang, Johannes Herrnsdorf, Sujan Rajbhandari, Jonathan McKendry, Iman Tavakkolnia, Humeyra Caglayan, Henning Helmers, Graham Turnbull, Ifor D. W. Samuel, Martin Dawson, Robert Schober, Harald Haas
Date: 2024-12-21 00:00:00
Tasks: Gesture Recognition

Optical wireless communication (OWC) is a promising technology anticipated to play a key role in the next-generation network of networks, especially as a complementary technology to traditional radio frequency communications, for enhancing networking capabilities beyond conventional terrestrial networks. OWC is already a mature technology with diverse usage scenarios, and can enable integrated applications via wireless access and backhaul networks, dynamic drone and satellite networks, underwater networks, inter- and intra-system interconnecting networks, and vehicular communication networks. Furthermore, novel and emerging technological opportunities such as photovoltaic cells, orbital angular momentum-based modulation, optical reconfigurable intelligent surfaces, organic light-emitting and photo diodes, and recent advances in ultraviolet communications can help enhance future OWC capabilities even further. Moreover, OWC networks can also support value-added services such as enhanced positioning and gesture recognition. Hence, OWC provides unique functionalities that can play a crucial role in building convergent and resilient future network of networks alongside radio frequency and optical fiber technologies.
Beamspace-based Sensing in Dynamic Joint Communication and Sensing Systems
Authors: D. R. Raghavendra, V. V. Reddy
Date: 2024-12-21 00:00:00

Dual-function radar communication (DFRC) systems incorporate both radar and communication functions by sharing spectrum, hardware and RF signal processing chains. Future technologies, such as 6G, are envisioned to support multiple communication platforms along with radar sensing, thus leading to high dynamism and competition for the available resources. In such settings, whenever communication takes precedence, a likely scenario is dynamically changing RF chain and antenna availability for sensing. This necessitates real-time beam redesign to cover the field-of-view (FOV), solving which is intractable via computationally expensive optimization approaches. We propose that classic windowing techniques are still relevant and much more practical than optimization methods in such dynamic scenarios. Specifically, parametrized windows can be used in a strategic way to adapt to varying resource availability while sustaining sensing performance.
Joint Downlink-Uplink Channel Estimation for Non-Reciprocal RIS-Assisted Communications
Authors: Paulo R. B. Gomes, Amarilton L. Magalhães, André L. F. de Almeida
Date: 2024-12-20 00:00:00

Reconfigurable intelligent surface (RIS) is a recent low-cost and energy-efficient technology with potential applicability for future wireless communications. Performance gains achieved by employing RIS directly depend on accurate channel estimation (CE). It is common in the literature to assume channel reciprocity due to the facilities provided by this assumption, such as no channel feedback, beamforming simplification, and latency reduction. However, in practice, due to hardware limitations at the RIS and transceivers, the channel non-reciprocity may occur naturally, so such behavior needs to be considered. In this paper, we focus on the CE problem in a non-reciprocal RIS-assisted multiple-input multiple-output (MIMO) wireless communication system. Making use of a novel closed-loop three-phase protocol for non-reciprocal CE estimation, we propose a two-stage fourth-order Tucker decomposition-based CE algorithm. In contrast to classical time-division duplexing (TDD) and frequency-division duplexing (FDD) approaches the proposed method concentrates all the processing burden for CE on the base station (BS) side, thereby freeing hardware-limited user terminal (UT) from this task. Our simulation results show that the proposed method has satisfactory performance in terms of CE accuracy compared to benchmark FDD LS-based and tensor-based techniques.
Distributed Beam Alignment in sub-THz D2D Networks
Authors: Fernando Pedraza, Jan Christian Hauffen, Fabian Jaensch, Shuangyang Li, Giuseppe Caire
Date: 2024-12-20 00:00:00
Tasks: compressed sensing

Devices in a device-to-device (D2D) network operating in sub-THz frequencies require knowledge of the spatial channel that connects them to their peers. Acquiring such high dimensional channel state information entails large overhead, which drastically increases with the number of network devices. In this paper, we propose an accelerated method to achieve network-wide beam alignment in an efficient way. To this aim, we consider compressed sensing estimation enabled by a novel design of pilot sequences. Our designed pilots have constant envelope to alleviate hardware requirements at the transmitters, while they exhibit a "comb-like"' spectrum that flexibly allocates energy only on certain frequencies. This design enables multiple devices to transmit thier pilots concurrently while remaining orthogonal in frequency, achieving simultaneous alignment of multiple devices. Furthermore, we present a sequential partitioning strategy into transmitters and receivers that results in logarithmic scaling of the overhead with the number of devices, as opposed to the conventional linear scaling. Finally, we show via accurate modeling of the indoor propagation environment and ray tracing simulations that the resulting sub-THz channels after successful beamforming are approximately frequency flat, therefore suitable for efficient single carrier transmission without equalization. We compare our results against an "802.11ad inspired" baseline and show that our method is capable to greatly reduce the number of pilots required to achieve network-wide alignment.
Rethinking Hardware Impairments in Multi-User Systems: Can FAS Make a Difference?
Authors: Junteng Yao, Tuo Wu, Liaoshi Zhou, Ming Jin, Cunhua Pan, Maged Elkashlan, Fumiyuki Adachi, George K. Karagiannidis, Naofal Al-Dhahir, Chau Yuen
Date: 2024-12-20 00:00:00

In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs). Specifically, we investigate a scenario where a base station (BS) equipped with multiple fluid antennas communicates with multiple users (CUs), each equipped with a single fluid antenna. Our objective is to maximize the minimum communication rate among all users by jointly optimizing the BS's transmit beamforming, the positions of its transmit fluid antennas, and the positions of the CUs' receive fluid antennas. To address this non-convex problem, we propose a block coordinate descent (BCD) algorithm integrating semidefinite relaxation (SDR), rank-one constraint relaxation (SRCR), successive convex approximation (SCA), and majorization-minimization (MM). Simulation results demonstrate that FAS significantly enhances system performance and robustness, with notable gains when both the BS and CUs are equipped with fluid antennas. Even under low transmit power conditions, deploying FAS at the BS alone yields substantial performance gains. However, the effectiveness of FAS depends on the availability of sufficient movement space, as space constraints may limit its benefits compared to fixed antenna strategies. Our findings highlight the potential of FAS to mitigate HIs and enhance multi-user system performance, while emphasizing the need for practical deployment considerations.
Optimization of Pilot-Aided Joint Phase Recovery for Frequency Comb-Based Wideband Transmission
Authors: Gabriele Di Rosa, Ognjen Jovanovic, M. Ahmed Leghari, Jasper Müller, Benjamin Wohlfeil, Jörg-Peter Elbers
Date: 2024-12-20 00:00:00

We numerically investigate joint pilot-aided phase recovery for frequency comb-based long-haul wideband transmission. We report net information rate gains by optimizing the pilot overhead and phase estimation algorithm, outperforming per-channel processing at lower complexity.
Broadband Passive Sonar Track-Before-Detect Using Raw Acoustic Data
Authors: Daniel Bossér, Magnus Lundberg Nordenvaad, Gustaf Hendeby, Isaac Skog
Date: 2024-12-20 00:00:00

This article concerns the challenge of reliable broadband passive sonar target detection and tracking in complex acoustic environments. Addressing this challenge is becoming increasingly crucial for safeguarding underwater infrastructure, monitoring marine life, and providing defense during seabed warfare. To that end, a solution is proposed based on a vector-autoregressive model for the ambient noise and a heavy-tailed statistical model for the distribution of the raw hydrophone data. These models are integrated into a Bernoulli track-before-detect (TkBD) filter that estimates the probability of target existence, target bearing, and signal-to-noise ratio (SNR). The proposed solution is evaluated on both simulated and real-world data, demonstrating the effectiveness of the proposed ambient noise modeling and the statistical model for the raw hydrophone data samples to obtain early target detection and robust target tracking. The simulations show that the SNR at which the target can be detected is reduced by 4 dB compared to when using the standard constant false alarm rate detector-based tracker. Further, the test with real-world data shows that the proposed solution increases the target detection distance from 250 m to 390 m. The presented results illustrate that the TkBD technology, in combination with data-driven ambient noise modeling and heavy-tailed statistical signal models, can enable reliable broadband passive sonar target detection and tracking in complex acoustic environments and lower the SNR required to detect and track targets.
Resonant Beam Multi-Target DOA Estimation
Authors: Yixuan Guo, Qingwei Jiang, Mingliang Xiong, Wen Fang, Mingqing Liu, Qingqing Zhang, Qingwen Liu, Gang Yan
Date: 2024-12-20 00:00:00

With the increasing demand for internet of things (IoT) applications, especially for location-based services, how to locate passive mobile targets (MTs) with minimal beam control has become a challenge. Resonant beam systems are considered promising IoT technologies with advantages such as beam self-alignment and energy concentration. To establish a resonant system in the radio frequency (RF) band and achieve multi-target localization, this paper designs a multi-target resonant system architecture, allowing a single base station (BS) to independently connect with multiple MTs. By employing a retro-directive array, a multi-channel cyclic model is established to realize one-to-many electromagnetic wave propagation and MT direction-of-arrival (DOA) estimation through echo resonance. Simulation results show that the proposed system supports resonant establishment between the BS and multiple MTs. This helps the BS to still have high DOA estimation accuracy in the face of multiple passive MTs, and can ensure that the DOA error is less than 1 degree within a range of 6 meters at a 50degree field of view, with higher accuracy than active beamforming localization systems.
Resonant Beam Enabled Passive 3D Positioning
Authors: Yixuan Guo, Mingliang Xiong, Wen Fang, Qingwei Jiang, Mengyuan Xu, Qingwen Liu, Gang Yan
Date: 2024-12-20 00:00:00

With the rapid development of the internet of things (IoT), location-based services are becoming increasingly prominent in various aspects of social life, and accurate location information is crucial. However, RF-based indoor positioning solutions are severely limited in positioning accuracy due to signal transmission losses and directional difficulties, and optical indoor positioning methods require high propagation conditions. To achieve higher accuracy in indoor positioning, we utilize the principle of resonance to design a triangulation-based resonant beam positioning system (TRBPS) in the RF band. The proposed system employs phase-conjugation antenna arrays and resonance mechanism to achieve energy concentration and beam self-alignment, without requiring active signals from the target for positioning and complex beam control algorithms. Numerical evaluations indicate that TRBPS can achieve millimeter-level accuracy within a range of 3.6 m without the need for additional embedded systems.
Hybrid Network- and User-Centric Scalable Cell-Free Massive MIMO for Fronthaul Signaling Minimization
Authors: Phu Lai, Wei Xiang, William Damario Lukito, Khoa Tran Phan, Peng Cheng, Chang Liu, Guoqiang Mao
Date: 2024-12-20 00:00:00
Tasks: CPU

Cell-free massive multiple-input multiple-output (CFmMIMO) coordinates a great number of distributed access points (APs) with central processing units (CPUs), effectively reducing interference and ensuring uniform service quality for user equipment (UEs). However, its cooperative nature can result in intense fronthaul signaling between CPUs in large-scale networks. To reduce the inter-CPU fronthaul signaling for systems with limited fronthaul capacity, we propose a low-complexity online UE-AP association approach for scalable CFmMIMO that combines network- and user-centric clustering methodologies, relies on local channel information only, and can handle dynamic UE arrivals. Numerical results demonstrate that compared to the state-of-the-art method on fronthaul signaling minimization, our approach can save up to 94% of the fronthaul signaling load and 83% of the CPU processing power at the cost of only up to 8.6% spectral efficiency loss, or no loss in some cases.
Confidence Intervals for the Savitzky-Golay Filter with an Application to the Keeling Data for Atmospheric CO2
Authors: Paul W. Oxby
Date: 2024-12-19 00:00:00

The Savitzky-Golay FIR digital filter is based on a least-squares polynomial fit to a sample of equally spaced data. The polynomial fit gives the filter the ability to preserve moments of features in the data like peak width. However the S-G filter is not generally regarded as having a sound statistical basis. This puts the filter in the category of smoothing filters where the degree of smoothing depends on the somewhat arbitrary choice of the filter parameters. This arbitrariness makes the variance of the residuals between the filter input and output an unreliable estimate of the variance of the noise in the filter input. And without a reliable estimate of the input noise variance there is no basis for determining statistically meaningful confidence intervals on the filter output. This paper proposes a method of using the S-G filter to determine a reliable estimate of the variance of the noise in the data. This estimate is then used as the basis for selecting appropriate filter parameters and determining statistically meaningful confidence intervals on the filter output. To illustrate the proposed method an analysis of the Keeling measurements of atmospheric CO2 concentration is presented.
On Shaping Gain of Multidimensional Constellation in Linear and Nonlinear Optical Fiber Channel
Authors: Bin Chen, Zhiwei Liang, Yi Lei, Jingxin Deng, Shen Li, Gabriele Liga
Date: 2024-12-19 00:00:00

Utilizing the multi-dimensional (MD) space for constellation shaping has been proven to be an effective approach for achieving shaping gains. Despite there exists a variety of MD modulation formats tailored for specific optical transmission scenarios, there remains a notable absence of a dependable comparison method for efficiently and promptly re-evaluating their performance in arbitrary transmission systems. In this paper, we introduce an analytical nonlinear interference (NLI) power model-based shaping gain estimation method to enable a fast performance evaluation of various MD modulation formats in coherent dual-polarization (DP) optical transmission system. In order to extend the applicability of this method to a broader set of modulation formats, we extend the established NLI model to take the 4D joint distribution into account and thus able to analyze the complex interactions of non-iid signaling in DP systems. With the help of the NLI model, we conduct a comprehensive analysis of the state-of-the-art modulation formats and investigate their actual shaping gains in two types of optical fiber communication scenarios (multi-span and single-span). The numerical simulation shows that for arbitrary modulation formats, the NLI power and relative shaping gains in terms of signal-to-noise ratio can be more accurately estimated by capturing the statistics of MD symbols. Furthermore, the proposed method further validates the effectiveness of the reported NLI-tolerant modulation format in the literature, which reveals that the linear shaping gains and modulation-dependent NLI should be jointly considered for nonlinearity mitigation.
Low-Complexity Frequency-Dependent Linearizers Based on Parallel Bias-Modulus and Bias-ReLU Operations
Authors: Deijany Rodriguez Linares, Håkan Johansson
Date: 2024-12-17 00:00:00

This paper introduces low-complexity frequency-dependent (memory) linearizers designed to suppress nonlinear distortion in analog-to-digital interfaces. Two different linearizers are considered, based on nonlinearity models which correspond to sampling before and after the nonlinearity operations, respectively. The proposed linearizers are inspired by convolutional neural networks but have an order-of-magnitude lower implementation complexity compared to existing neural-network-based linearizer schemes. The proposed linearizers can also outperform the traditional parallel Hammerstein (as well as Wiener) linearizers even when the nonlinearities have been generated through a Hammerstein model. Further, a design procedure is proposed in which the linearizer parameters are obtained through matrix inversion. This eliminates the need for costly and time-consuming iterative nonconvex optimization which is traditionally associated with neural network training. The design effectively handles a wide range of wideband multi-tone signals and filtered white noise. Examples demonstrate significant signal-to-noise-and-distortion ratio (SNDR) improvements of some $20$--$30$ dB, as well as a lower implementation complexity than the Hammerstein linearizers.
The generic geometry of steady state varieties
Authors: Elisenda Feliu, Oskar Henriksson, Beatriz Pascual-Escudero
Date: 2024-12-23 00:00:00

We answer several fundamental geometric questions about reaction networks with power-law kinetics, on topics such as generic finiteness of the number of steady states, robustness, and nondegenerate multistationarity. In particular, we give an ideal-theoretic characterization of generic absolute concentration robustness, as well as conditions under which a network that admits multiple steady states also has the capacity for nondegenerate multistationarity. The key tools underlying our results come from the theory of vertically parametrized systems, and include a linear algebra condition that characterizes when the steady state system has positive nondegenerate zeros.