pwc-archive/papers-with-abstracts huggingface dataset:
A Derivative-Free Position Optimization Approach for Movable Antenna Multi-User Communication Systems
Authors: Xianlong Zeng, Jun Fang, Peilan Wang, Weidong Mei, Ying-Chang Liang
Date: 2025-05-25 00:00:00
Tasks: Position

Movable antennas (MAs) have emerged as a disruptive technology in wireless communications for enhancing spatial degrees of freedom through continuous antenna repositioning within predefined regions, thereby creating favorable channel propagation conditions. In this paper, we study the problem of position optimization for MA-enabled multi-user MISO systems, where a base station (BS), equipped with multiple MAs, communicates with multiple users each equipped with a single fixed-position antenna (FPA). To circumvent the difficulty of acquiring the channel state information (CSI) from the transmitter to the receiver over the entire movable region, we propose a derivative-free approach for MA position optimization. The basic idea is to treat position optimization as a closed-box optimization problem and calculate the gradient of the unknown objective function using zeroth-order (ZO) gradient approximation techniques. Specifically, the proposed method does not need to explicitly estimate the global CSI. Instead, it adaptively refines its next movement based on previous measurements such that it eventually converges to an optimum or stationary solution. Simulation results show that the proposed derivative-free approach is able to achieve higher sample and computational efficiencies than the CSI estimation-based position optimization approach, particularly for challenging scenarios where the number of multi-path components (MPCs) is large or the number of pilot signals is limited.
Waveform Coexistence-Driven RSMA: A Pioneering Strategy for Future 6G Networks
Authors: Kenza Abela, Shaima Abidrabbu, Ayoub Ammar Boudjelal, Huseyin Arslan
Date: 2025-05-24 00:00:00

Two critical approaches have emerged in the literature for the successful realization of 6G wireless networks: the coexistence of multiple waveforms and the adoption of non-orthogonal multiple access. These strategies hold transformative potential for addressing the limitations of current systems and enabling the robust and scalable design of next-generation wireless networks. This paper presents a novel rate splitting multiple access (RSMA) framework that leverages the coexistence of affine frequency division multiplexing (AFDM) and orthogonal frequency division multiplexing (OFDM). By transmitting common data via AFDM at higher power in the affine domain and private data via OFDM at lower power in the frequency domain, the proposed framework eliminates the reliance on successive interference cancellation (SIC), significantly simplifying receiver design. Furthermore, two data mapping approaches are proposed: a clean pilot method, where pilots are allocated without any data overlapping, ensuring clear separation, and an embedded pilot method, where pilots overlap with data for more efficient resource utilization. Channel estimation is then performed for different channel types. Simulation results demonstrate the robustness and efficiency of the proposed approach, achieving superior performance in efficiency, reliability, and adaptability under diverse channel conditions. This framework transforms non-orthogonal multi-access design, paving the way for scalable and efficient solutions in 6G networks.
EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking
Authors: Zhixing Wang, Le Zheng, Shi Yan, Ruud J. G. van Sloun, Nir Shlezinger, Yonina C. Eldar
Date: 2025-05-24 00:00:00
Tasks: Object, Object Tracking

Extended object tracking methods based on random matrices, founded on Bayesian filters, have been able to achieve efficient recursive processes while jointly estimating the kinematic states and extension of the targets. Existing random matrix approaches typically assume that the evolution of state and extension follows a first-order Markov process, where the current estimate of the target depends solely on the previous moment. However, in real-world scenarios, this assumption fails because the evolution of states and extension is usually non-Markovian. In this paper, we introduce a novel extended object tracking method: a Bayesian recursive neural network assisted by deep memory. Initially, we propose an equivalent model under a non-Markovian assumption and derive the implementation of its Bayesian filtering framework. Thereafter, Gaussian approximation and moment matching are employed to derive the analytical solution for the proposed Bayesian filtering framework. Finally, based on the closed-form solution, we design an end-to-end trainable Bayesian recursive neural network for extended object tracking. Experiment results on simulated and real-world datasets show that the proposed methods outperforms traditional extended object tracking methods and state-of-the-art deep learning approaches.
FDMA-Based Passive Multiple Users SWIPT Utilizing Resonant Beams
Authors: Yixuan Guo, Mingliang Xiong, Wen Fang, Qingwei Jiang, Qingwen Liu, Gang Yan
Date: 2025-05-24 00:00:00

The rapid development of IoT technology has led to a shortage of spectrum resources and energy, giving rise to simultaneous wireless information and power transfer (SWIPT) technology. However, traditional multiple input multiple output (MIMO)-based SWIPT faces challenges in target detection. We have designed a passive multi-user resonant beam system (MU-RBS) that can achieve efficient power transfer and communication through adaptive beam alignment. The frequency division multiple access (FDMA) is employed in the downlink (DL) channel, while frequency conversion is utilized in the uplink (UL) channel to avoid echo interference and co-channel interference, and the system architecture design and corresponding mathematical model are presented. The simulation results show that MU-RBS can achieve adaptive beam-forming without the target transmitting pilot signals, has high directivity, and as the number of iterations increases, the power transmission efficiency, signal-to-noise ratio and spectral efficiency of the UL and DL are continuously optimized until the system reaches the optimal state.
Frequency and Bandwidth Design of FR3-Band Acoustic Filters
Authors: Taran Anusorn, Omar Barrera, Jack Kramer, Ian Anderson, Ziqian Yao, Vakhtang Chulukhadze, Ruochen Lu
Date: 2025-05-23 00:00:00

This article presents an approach to control the operating frequency and fractional bandwidth (FBW) of miniature acoustic filters in thin-film lithium niobate (TFLN). More specifically, we used the first-order antisymmetric (A1) mode in lateral-field-excited bulk acoustic wave resonators (XBARs) to achieve efficient operation at 20.5 GHz. Our technique leverages the thickness-dependent resonance frequency of A1 XBARs, combined with the in-plane anisotropic properties of 128$^\circ$ Y-cut TFLN, to customize filter characteristics. The implemented three-element ladder filter prototype achieves an insertion loss (IL) of only 1.79 dB and a controlled 3-dB FBW of 8.58% at 20.5 GHz, with an out-of-band (OoB) rejection greater than 14.9 dB across the entire FR3 band, while featuring a compact footprint of 0.90 $\times$ 0.74 mm$^2$. Moreover, an eight-element filter prototype shows an IL of 3.80 dB, an FBW of 6.12% at 22.0 GHz, and a high OoB rejection of 22.97 dB, demonstrating the potential for expanding to higher-order filters. As frequency allocation requirements become more stringent in future FR3 bands, our technique showcases promising capability in enabling compact and monolithic filter banks toward next-generation acoustic filters for 6G and beyond.
Current-Steering DAC Architecture Design for Amplitude Mismatch Error Minimization
Authors: Ramin Babaee, Shahab Oveis Gharan, Martin Bouchard
Date: 2025-05-23 00:00:00

We propose a novel digital-to-analog converter (DAC) weighting architecture that statistically minimizes the distortion caused by random current mismatches. Unlike binary, thermometer-coded, and segmented DACs, the current weights of the proposed architecture are not an integer power of 2 or any other integer number. We present a heuristic algorithm for a static mapping of DAC input codewords into corresponding DAC switches. High-level Matlab simulations are performed to illustrate the static performance improvement over the segmented structure.
Emergence of Hebbian Dynamics in Regularized Non-Local Learners
Authors: David Koplow, Tomaso Poggio, Liu Ziyin
Date: 2025-05-23 00:00:00
Tasks: Autonomous Vehicles

Stochastic Gradient Descent (SGD) has emerged as a remarkably effective learning algorithm, underpinning nearly all state-of-the-art machine learning models, from large language models to autonomous vehicles. Despite its practical success, SGD appears fundamentally distinct from biological learning mechanisms. It is widely believed that the biological brain can not implement gradient descent because it is nonlocal, and we have found little (if any) experimental evidence for it. In contrast, the brain is widely thought to learn via local Hebbian learning principles, which have been seen as incompatible with gradient descent. In this paper, we establish a theoretical and empirical connection between the learning signals of neural networks trained using SGD with weight decay and those trained with Hebbian learning near convergence. We show that SGD with regularization can appear to learn according to a Hebbian rule, and SGD with injected noise according to an anti-Hebbian rule. We also provide empirical evidence that Hebbian learning properties can emerge in a network with weight decay from virtually any learning rule--even random ones. These results may bridge a long-standing gap between artificial and biological learning, revealing Hebbian properties as an epiphenomenon of deeper optimization principles and cautioning against interpreting their presence in neural data as evidence against more complex hetero-synaptic mechanisms.
Faulty RIS-aided Integrated Sensing and Communication: Modeling and Optimization
Authors: Lu Wang, Gui Zhou, Changheng Li, Luis F. Abanto-Leon, Nairy Moghadas Gholian, Matthias Hollick, Arash Asadi
Date: 2025-05-23 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

This work investigates a practical reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where a subset of RIS elements fail to function properly and reflect incident signals randomly towards unintended directions, thereby degrading system performance. To date, no study has addressed such impairments caused by faulty RIS elements in ISAC systems. This work aims to fill the gap. First, to quantify the impact of faulty elements on ISAC performance, we derive the misspecified Cram\'er-Rao bound (MCRB) for sensing parameter estimation and signal-to-interference-and-noise ratio (SINR) for communication quality. Then, to mitigate the performance loss caused by faulty elements, we jointly design the remaining functional RIS phase shifts and transmit beamforming to minimize the MCRB, subject to the communication SINR and transmit power constraints. The resulting optimization problem is highly non-convex due to the intricate structure of the MCRB expression and constant-modulus constraint imposed on RIS. To address this, we reformulate it into a more tractable form and propose a block coordinate descent (BCD) algorithm that incorporates majorization-minimization (MM), successive convex approximation (SCA), and penalization techniques. Simulation results demonstrate that our proposed approach reduces the MCRB performance loss by 24.36% on average compared to the case where the presence of faulty elements is ignored. Furthermore, the performance gain becomes more evident as the number of faulty elements increases.
LLM4SG: Large Language Models for Scatterer Generation via Synesthesia of Machines
Authors: Zengrui Han, Lu Bai, Ziwei Huang, Xiang Cheng
Date: 2025-05-23 00:00:00

Guided by Synesthesia of Machines (SoM), the nonlinear mapping relationship between sensory and communication information serves as a powerful tool to enhance both the accuracy and generalization of vehicle-to-vehicle (V2V) multi-modal intelligent channel modeling (MMICM) in intelligent transportation systems (ITSs). To explore the general mapping relationship between physical environment and electromagnetic space, a new intelligent sensing-communication integration dataset, named V2V-M3, is constructed for multiple scenarios in V2V communications with multiple frequency bands and multiple vehicular traffic densities (VTDs). Leveraging the strong representation and cross-modal inference capabilities of large language models (LLMs), a novel LLM-based method for Scatterer Generation (LLM4SG) from light detection and ranging (LiDAR) point clouds is developed. To address the inherent and significant differences across multi-modal data, synergistically optimized four-module architecture, i.e., preprocessor, embedding, backbone, and output modules, are designed by considering the sensing/channel characteristics and electromagnetic propagation mechanism. On the basis of cross-modal representation alignment and positional encoding, the network of LLM4SG is fine-tuned to capture the general mapping relationship between LiDAR point clouds and scatterers. Simulation results demonstrate that the proposed LLM4SG achieves superior performance in full-sample and generalization testing, significantly outperforming small models across different frequency bands, scenarios, and VTDs.
CC-OTDR Sequence Shaping Enabling Joint Co-directional Sensing and Communication
Authors: M. Ali Allousch, André Sandmann
Date: 2025-05-23 00:00:00

CC-OTDR signal envelope shaping is introduced to reduce the impact of non-linear signal interactions on a neighboring wavelength data channel when co-propagating the probing signal with the data signal. Joint co-directional acoustic sensing and 200 Gbps transmission are demonstrated over a 50 km link.
Enhancing Fourier-based Doppler Resolution with Diffusion Models
Authors: Denisa Qosja, Kilian Barth, Simon Wagner
Date: 2025-05-23 00:00:00

In radar systems, high resolution in the Doppler dimension is important for detecting slow-moving targets as it allows for more distinct separation between these targets and clutter, or stationary objects. However, achieving sufficient resolution is constrained by hardware capabilities and physical factors, leading to the development of processing techniques to enhance the resolution after acquisition. In this work, we leverage artificial intelligence to increase the Doppler resolution in range-Doppler maps. Based on a zero-padded FFT, a refinement via the generative neural networks of diffusion models is achieved. We demonstrate that our method overcomes the limitations of traditional FFT, generating data where closely spaced targets are effectively separated.
Interference Modulation: A Novel Technique for Low-Rate and Power Efficient Multiple Access
Authors: M. Yaser Yagan, Ali E. Pusane, Ali Gorcin, Ibrahim Hokelek
Date: 2025-05-23 00:00:00

The majority of spatial signal processing techniques focus on increasing the total system capacity and providing high data rates for intended user(s). Unlike the existing studies, this paper introduces a novel interference modulation method that exploits the correlation between wireless channels to enable low-data-rate transmission towards additional users with a minimal power allocation. The proposed method changes the interference power at specific channels to modulate a low-rate on-off keying signal. This is achieved by appropriately setting the radiation pattern of front-end components of a transmitter, i.e., analog beamforming weights or metasurface configuration. The paper investigates theoretical performance limits and analyzes the efficiency in terms of sum rate. Bit error rate simulation results are closely matched with theoretical findings. The initial findings indicate that the proposed technique can be instrumental in providing reduced capability communication using minimal power consumption in 6G networks.
Computing phylogenetic invariants for time-reversible models: from TN93 to its submodels
Authors: Marta Casanellas, Jennifer Garbett, Roser Homs, Annachiara Korchmaros, Niharika Chakrabarty Paul
Date: 2025-05-26 00:00:00
Tasks: Philosophy

Phylogenetic invariants are equations that vanish on algebraic varieties associated with Markov processes that model molecular substitutions on phylogenetic trees. For practical applications, it is essential to understand these equations across a wide range of substitution models. Recent work has shown that, for equivariant models, phylogenetic invariants can be derived from those of the general Markov model by restricting to the linear space defined by the model (namely, the space of mixtures of distributions on the model). Following this philosophy, we describe the space of mixtures and phylogenetic invariants for time-reversible models that are not equivariant. Specifically, we study two submodels of the Tamura-Nei nucleotide substitution model (Felsenstein 81 and 84) using an orthogonal change of basis recently introduced for algebraic time-reversible models. For tripods, we prove that the algebraic variety of each submodel coincides with the variety of Tamura-Nei intersected with the linear space of the submodel. In the case of quartets, we show that it is an irreducible component of this intersection. Moreover, we demonstrate that it suffices to consider only the binomial equations defining the linear space, which correspond to the natural symmetries of the model in the new coordinates. For each submodel, we explicitly provide equations defining a local complete intersection that characterizes the phylogenetic variety on a dense open subset containing the biologically relevant points.
Higher-Order Group Synchronization
Authors: Adriana L. Duncan, Joe Kileel
Date: 2025-05-28 00:00:00

Group synchronization is the problem of determining reliable global estimates from noisy local measurements on networks. The typical task for group synchronization is to assign elements of a group to the nodes of a graph in a way that respects group elements given on the edges which encode information about local pairwise relationships between the nodes. In this paper, we introduce a novel higher-order group synchronization problem which operates on a hypergraph and seeks to synchronize higher-order local measurements on the hyperedges to obtain global estimates on the nodes. Higher-order group synchronization is motivated by applications to computer vision and image processing, among other computational problems. First, we define the problem of higher-order group synchronization and discuss its mathematical foundations. Specifically, we give necessary and sufficient synchronizability conditions which establish the importance of cycle consistency in higher-order group synchronization. Then, we propose the first computational framework for general higher-order group synchronization; it acts globally and directly on higher-order measurements using a message passing algorithm. We discuss theoretical guarantees for our framework, including convergence analyses under outliers and noise. Finally, we show potential advantages of our method through numerical experiments. In particular, we show that in certain cases our higher-order method applied to rotational and angular synchronization outperforms standard pairwise synchronization methods and is more robust to outliers. We also show that our method has comparable performance on simulated cryo-electron microscopy (cryo-EM) data compared to a standard cryo-EM reconstruction package.
Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization
Authors: Hongzheng Yang, Cheng Chen, Yueyao Chen, Markus Scheppach, Hon Chi Yip, Qi Dou
Date: 2023-11-05 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Scene Segmentation, Segmentation

Uncertainty estimation plays an important role for future reliable deployment of deep segmentation models in safety-critical scenarios such as medical applications. However, existing methods for uncertainty estimation have been limited by the lack of explicit guidance for calibrating the prediction risk and model confidence. In this work, we propose a novel fine-grained reward maximization (FGRM) framework, to address uncertainty estimation by directly utilizing an uncertainty metric related reward function with a reinforcement learning based model tuning algorithm. This would benefit the model uncertainty estimation through direct optimization guidance for model calibration. Specifically, our method designs a new uncertainty estimation reward function using the calibration metric, which is maximized to fine-tune an evidential learning pre-trained segmentation model for calibrating prediction risk. Importantly, we innovate an effective fine-grained parameter update scheme, which imposes fine-grained reward-weighting of each network parameter according to the parameter importance quantified by the fisher information matrix. To the best of our knowledge, this is the first work exploring reward optimization for model uncertainty estimation in safety-critical vision tasks. The effectiveness of our method is demonstrated on two large safety-critical surgical scene segmentation datasets under two different uncertainty estimation settings. With real-time one forward pass at inference, our method outperforms state-of-the-art methods by a clear margin on all the calibration metrics of uncertainty estimation, while maintaining a high task accuracy for the segmentation results. Code is available at \url{https://github.com/med-air/FGRM}.
Hadaptive-Net: Efficient Vision Models via Adaptive Cross-Hadamard Synergy
Authors: Xuyang Zhang, Xi Zhang, Liang Chen, Hao Shi, Qingshan Guo
Date: 2025-05-28 00:00:00

Recent studies have revealed the immense potential of Hadamard product in enhancing network representational capacity and dimensional compression. However, despite its theoretical promise, this technique has not been systematically explored or effectively applied in practice, leaving its full capabilities underdeveloped. In this work, we first analyze and identify the advantages of Hadamard product over standard convolutional operations in cross-channel interaction and channel expansion. Building upon these insights, we propose a computationally efficient module: Adaptive Cross-Hadamard (ACH), which leverages adaptive cross-channel Hadamard products for high-dimensional channel expansion. Furthermore, we introduce Hadaptive-Net (Hadamard Adaptive Network), a lightweight network backbone for visual tasks, which is demonstrated through experiments that it achieves an unprecedented balance between inference speed and accuracy through our proposed module.