pwc-archive/papers-with-abstracts huggingface dataset:
Analog OFDM based on Real-Time Fourier Transformation
Authors: Xiaolu Yang, Oscar Céspedes Vicente, Christophe Caloz
Date: 2025-06-25 00:00:00

This paper proposes an analog orthogonal frequency division multiplexing (OFDM) architecture based on the real-time Fourier transform (RTFT). The core enabling component is a linear-chirp phaser with engineered group velocity dispersion (GVD), which realizes RTFT and performs frequency-to-time mapping in the analog domain. In this architecture, conventional digital fast Fourier transform (FFT) and inverse FFT (IFFT) processors are replaced by two linear-chirp phasers with opposite group delay dispersions, respectively. Theoretical analysis demonstrates that, under specific phaser conditions, the OFDM signal generated by the RTFT-based analog system is mathematically equivalent to that of a conventional digital OFDM system. This equivalence is further supported by simulation results, which confirm accurate symbol transmission and recovery, as well as robustness to multipath fading when a prefix is applied. Benefiting from the use of passive microwave components, the analog OFDM system offers ultra-fast processing with reduced power consumption. Overall, this work establishes a foundation for fully analog or hybrid analog-digital OFDM system, offering a promising solution for next-generation high-speed, wideband, and energy-efficient wireless communication platforms.
Superimposed DMRS for Spectrally Efficient 6G Uplink Multi-User OFDM: Classical vs AI/ML Receivers
Authors: Sajad Rezaie, Mikko Honkala, Dani Korpi, Dick Carrillo Melgarejo, Tomasz Izydorczyk, Dimitri Gold, Oana-Elena Barbu
Date: 2025-06-25 00:00:00

Fifth-generation (5G) systems utilize orthogonal demodulation reference signals (DMRS) to enable channel estimation at the receiver. These orthogonal DMRS-also referred to as pilots-are effective in avoiding pilot contamination and interference from both the user's own data and that of others. However, this approach incurs a significant overhead, as a substantial portion of the time-frequency resources must be reserved for pilot transmission. Moreover, the overhead increases with the number of users and transmission layers. To address these limitations in the context of emerging sixth-generation (6G) systems and to support data transmission across the entire time-frequency grid, the superposition of data and DMRS symbols has been explored as an alternative DMRS transmission strategy. In this study, we propose an enhanced version of DeepRx, a deep convolutional neural network (CNN)-based receiver, capable of estimating the channel from received superimposed (SI) DMRS symbols and reliably detecting the transmitted data. We also design a conventional receiver for comparison, which estimates the channel from SI DMRS using classical signal processing techniques. Extensive evaluations in both uplink single-user and multi-user scenarios demonstrate that DeepRx consistently outperforms the conventional receivers in terms of performance.
Time and covariance smoothing for restoration of bivariate signals
Authors: Yusuf Yigit Pilavci, Pierre Palud, Julien Flamant, Pierre-Antoine Thouvenin, Jérémie Boulanger, Pierre Chainais
Date: 2025-06-25 00:00:00

In many applications and physical phenomena, bivariate signals are polarized, i.e. they trace an elliptical trajectory over time when viewed in the 2D planes of their two components. The smooth evolution of this elliptical trajectory, called polarization ellipse, is highly informative to solve ill-posed inverse problems involving bivariate signals where the signal is collected through indirect, noisy or incomplete measurements. This work proposes a novel formulation and an efficient algorithm for reconstructing bivariate signals with polarization regularization. The proposed formulation leverages the compact representation of polarization through the instantaneous covariance matrices. To address the resulting quartic optimization problem, we propose a well-suited parameter splitting strategy which leads to an efficient iterative algorithm (alternating direction method of multipliers (ADMM)) with convex subproblems at each iteration. The performance of the proposed method is illustrated on numerical synthetic data experiments.
Sensing-Aware Transmit Waveform/Receive Filter Design for OFDM-MBS Systems
Authors: Xinghe Li, Kainan Cheng, Huiyong Li, Ziyang Cheng
Date: 2025-06-25 00:00:00

In this letter, we study the problem of cooperative sensing design for an orthogonal frequency division multiplexing (OFDM) multiple base stations (MBS) system. We consider a practical scenario where the base stations (BSs) exploit certain subcarriers to realize a sensing function. Since the high sidelobe level (SLL) of OFDM waveforms degrades radar detection for weak targets, and the cross-correlation generated by other BSs further exacerbates detection performance, we devise a joint design scheme for OFDM sequence and receive filter by minimizing the integrated sidelobe level (ISL) while satisfying mainlobe level, peak-to-average power ratio (PAPR) and spectrum allocation constraints. To address this non-convex problem, we propose an alternating optimization (AO)-based algorithm. Numerical simulations validate the effectiveness of the proposed method, demonstrating the superiority of SSL reduction in the MBS system over the matched filtering method.
Joint Quantization and Pruning Neural Networks Approach: A Case Study on FSO Receivers
Authors: Mohanad Obeed, Ming Jian
Date: 2025-06-25 00:00:00
Tasks: Quantization

Towards fast, hardware-efficient, and low-complexity receivers, we propose a compression-aware learning approach and examine it on free-space optical (FSO) receivers for turbulence mitigation. The learning approach jointly quantize, prune, and train a convolutional neural network (CNN). In addition, we propose to have the CNN weights of power of two values so we replace the multiplication operations bit-shifting operations in every layer that has significant lower computational cost. The compression idea in the proposed approach is that the loss function is updated and both the quantization levels and the pruning limits are optimized in every epoch of training. The compressed CNN is examined for two levels of compression (1-bit and 2-bits) over different FSO systems. The numerical results show that the compression approach provides negligible decrease in performance in case of 1-bit quantization and the same performance in case of 2-bits quantization, compared to the full-precision CNNs. In general, the proposed IM/DD FSO receivers show better bit-error rate (BER) performance (without the need for channel state information (CSI)) compared to the maximum likelihood (ML) receivers that utilize imperfect CSI when the DL model is compressed whether with 1-bit or 2-bit quantization.
Low-Complexity Ordered Reliability Direct Error Pattern Testing (ORDEPT) Decoding with Likelihood Thresholding
Authors: Reza Hadavian, Dmitri Truhachev
Date: 2025-06-25 00:00:00

We propose a reduced complexity approach to pattern-based soft decoding of block codes. We start from the ORDEPT decoding algorithm which tests a list of partial error patterns organized in the order of their likelihood and attempts to complete the patterns creating candidate codewords. We then propose an early termination criterion. Once a candidate codeword is found, its log-likelihood difference to the received sequence is compared to a preset threshold and the decoding decision is instantly made in case the likelihood deviation is below the threshold. We demonstrate that while keeping the same block error rate (BLER) performance, the proposed algorithm's latency and complexity is multiple times smaller than that of the state-of-the art competitors including the Chase II, ORBGRAND, GCD, and the very recent ORDEPT with Soft-Output GRAND termination which necessitates several multiplications in each query processing.
Near-Field Energy Harvesting Using XL-MIMO Over Non-Stationary Channels
Authors: Muhammad Zeeshan Mumtaz, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou
Date: 2025-06-25 00:00:00

This paper explores the maximization of the harvested power efficiency (HPE) in a modular extremely large multiple-input multiple-output (XL-MIMO) system, which supports energy harvesting (EH) for near-field users. These users are located in spatially distinct visibility regions (VRs) with non-stationary channel characteristics. We propose to determine which sub-arrays are switched on or off as well the power control coefficients at the sub-arrays to maximize the HPE. The design can be processed via a multi-tier joint optimization framework based on fractional programming. The numerical results showcase that the HPE performance of the proposed algorithm is nearly optimal, comparable to that of exhaustive search. As a matter of fact, it achieves up to a 120% gain over the benchmark scheme which uses the entire XL-MIMO array with equal power allocation (PA) across sub-arrays, while significantly reducing the computational time.
Near-Field SWIPT Using XL-MIMO: Power Allocation and Subarray Activation
Authors: Muhammad Zeeshan Mumtaz, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou
Date: 2025-06-24 00:00:00

This paper investigates the simultaneous wireless information and power transfer (SWIPT) capability of a modular extremely large multiple-input multiple-output (XL-MIMO) system, in the context of power consumption (PC) efficiency. The network users are divided into two functional categories: information decoding (ID) users and energy harvesting (EH) users. Non-stationary near-field channels are considered whilst the users are located in spatially distinct visibility regions (VRs). We formulate a two-tier joint optimization problem to minimize the PC, taking into account the power allocation (PA) for ID and EH users, along with the activation of constituent XL-MIMO subarrays. This complicated mixed-integer problem is transformed into more tractable formulations and efficient algorithms are proposed for solving them. The numerical results demonstrate that the overall PC of the XL-MIMO system for the proposed method is reduced by more than 60% in comparison to the benchmark scheme of equal PA with full subarray activation (SA) and 30% against the case of optimized PA with full SA, while satisfying the quality-of-service (QoS) constraints on both the downlink rate of the ID users and harvested energy at the EH users.
Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM
Authors: Benjamin J. B. Deutschmann, Xuhong LI, Florian Meyer, Erik Leitinger
Date: 2025-06-24 00:00:00
Tasks: Position, Simultaneous Localization and Mapping

Radio-frequency simultaneous localization and mapping (RF-SLAM) methods jointly infer the position of mobile transmitters and receivers in wireless networks, together with a geometric map of the propagation environment. An inferred map of specular surfaces can be used to exploit non-line-of-sight components of the multipath channel to increase robustness, bypass obstructions, and improve overall communication and positioning performance. While performance bounds for user location are well established, the literature lacks performance bounds for map information. This paper derives the mapping error bound (MEB), i.e., the posterior Cram\'er-Rao lower bound on the position and orientation of specular surfaces, for RF-SLAM. In particular, we consider a very general scenario with single- and double-bounce reflections, as well as distributed anchors. We demonstrate numerically that a state-of-the-art RF-SLAM algorithm asymptotically converges to this MEB. The bounds assess not only the localization (position and orientation) but also the mapping performance of RF-SLAM algorithms in terms of global features.
Revisiting R: Statistical Envelope Analysis for Lightweight RF Modulation Classification
Authors: Srinivas Rahul Sapireddy, Mostafizur Rahman
Date: 2025-06-24 00:00:00
Tasks: Classification

Modulation classification plays a crucial role in wireless communication systems, enabling applications such as cognitive radio, spectrum monitoring, and electronic warfare. Conventional techniques often involve deep learning or complex feature extraction, which, while effective, require substantial computational resources and memory. An early approach by Chan and Gadbois in 1985 introduced a theoretical method for modulation classification using a mathematically derived parameter called R. The authors proved that the R value - the ratio of the variance to the square of the mean of the signal envelope - can be a distinguishing feature for classification. In this work, we revisit the R value and show that classification accuracy can be improved further through statistical methods. We extend R-value analysis to demonstrate its effectiveness even after signals are transformed using the Hilbert transform followed by the Short-Time Fourier Transform (STFT). Our analysis includes testing on 300000 signals across AM, DSB, and SSB classes, with each class having 100000 random variations. On average, we achieve 98.60, 97.30, and 97.90 percent classification accuracy for AM, DSB, and SSB signals after applying the Hilbert transform. Similar or better accuracies are observed after applying the STFT, reaching 98.80, 99.10, and 99.00 percent, respectively, for AM, DSB, and SSB types.
Beyond 200 Gb/s/lane: An Analytical Approach to Optimal Detection in Shaped IM-DD Optical Links with Relative Intensity Noise
Authors: Felipe Villenas, Kaiquan Wu, Yunus Can Gültekin, Jamal Riani, Alex Alvarado
Date: 2025-06-24 00:00:00

Next-generation intensity-modulation (IM) and direct-detection (DD) systems used in data centers are expected to operate at 400 Gb/s/lane and beyond. Such rates can be achieved by increasing the system bandwidth or the modulation format, which in turn requires maintaining or increasing the signal-to-noise ratio (SNR). Such SNR requirements can be achieved by increasing the transmitted optical power. This increase in optical power causes the emergence of relative intensity noise (RIN), a signal-dependent impairment inherent to the transmitter laser, which ultimately limits the performance of the system. In this paper, we develop an analytical symbol error rate (SER) expression for the optimal detector for the IM-DD optical link under study. The developed expression takes into account the signal-dependent nature of RIN and does not make any assumptions on the geometry or probability distribution of the constellation. Our expression is therefore applicable to general probabilistically and/or geometrically shaped systems. Unlike results available in the literature, our proposed expression provides a perfect match to numerical simulations of probabilistic and geometrically shaped systems.
On Error Rate Approximations for FSO Systems with Weak Turbulence and Pointing Errors
Authors: Carmen Álvarez Roa, Yunus Can Gültekin, Kaiquan Wu, Cornelis Willem Korevaar, Alex Alvarado
Date: 2025-06-24 00:00:00

Atmospheric attenuation, atmospheric turbulence, geometric spread, and pointing errors, degrade the performance of free-space optical transmission. In the weak turbulence regime, the probability density function describing the distribution of the channel fading coefficient that models these four effects is known in the literature. This function is an integral equation, which makes it difficult to find simple analytical expressions of important performance metrics such as the bit error rate (BER) and symbol error rate (SER). In this paper, we present simple and accurate approximations of the average BER and SER for pulse-amplitude modulation (PAM) in the weak turbulence regime for an intensity modulation and direct detection system. Our numerical results show that the proposed expressions exhibit excellent accuracy when compared against Monte Carlo simulations. To demonstrate the usefulness of the developed approximations, we perform two asymptotic analyses. First, we investigate the additional transmit power required to maintain the same SER when the spectral efficiency increases by 1 bit/symbol. Second, we study the asymptotic behavior of our SER approximation for dense PAM constellations and high transmit power.
A Wireless Self-Calibrating Ultrasound Microphone Array with Sub-Microsecond Synchronization
Authors: Dennis Laurijssen, Rens Baeyens, Walter Daems, Jan Steckel
Date: 2025-06-24 00:00:00
Tasks: Sound Source Localization

We present a novel system architecture for a distributed wireless, self-calibrating ultrasound microphone network for synchronized in-air acoustic sensing. Once deployed the embedded nodes determine their position in the environment using the infrared optical tracking system found in the HTC Vive Lighthouses. After self-calibration, the nodes start sampling the ultrasound microphone while embedding a synchronization signal in the data which is established using a wireless Sub-1GHz RF link. Data transmission is handled via the Wi-Fi 6 radio that is embedded in the nodes' SoC, decoupling synchronization from payload transport. A prototype system with a limited amount of network nodes was used to verify the proposed distributed microphone array's wireless data acquisition and synchronization capabilities. This architecture lays the groundwork for scalable, deployable ultrasound arrays for sound source localization applications in bio-acoustic research and industrial acoustic monitoring.
Reconfigurable Intelligent Surfaces for 6G and Beyond: A Comprehensive Survey from Theory to Deployment
Authors: Prasetyo Putranto, Anis Amazigh Hamza, Sameh Mabrouki, Nasrullah Armi, Iyad Dayoub
Date: 2025-06-24 00:00:00
Tasks: Survey

As the wireless research community moves toward shaping the vision of sixth-generation (6G) networks, reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for controlling the propagation environment. Although RIS has not yet been standardized, its versatile applications and enabling capabilities have attracted growing attention in both academia and industry. This survey presents a comprehensive review of RIS technology spanning theoretical foundations, design aspects, and practical deployment considerations. In contrast to existing surveys that focus on isolated aspects, this work offers an integrated view covering use cases, control mechanisms, channel sounding methodologies, and channel estimation strategies. Each of these topics is reviewed through the lens of recent literature, synthesizing the latest advancements to provide updated insights for both academic researchers and industry practitioners. It further addresses emerging topics such as standardization activities and industrial perspectives, which are often overlooked in prior literature. By bridging theoretical insights with practical challenges, this survey aims to provide a holistic understanding of RIS and support its evolution from a research concept toward real-world implementation.
Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems
Authors: Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li
Date: 2025-06-24 00:00:00
Tasks: Federated Learning

Future wireless networks are expected to be AI-empowered, making their performance highly dependent on the quality of training datasets. However, physical-layer entities often observe only partial wireless environments characterized by different power delay profiles. Federated learning is capable of addressing this limited observability, but often struggles with data heterogeneity. To tackle this challenge, we propose a neural collapse (NC) inspired deep supervised federated learning (NCDSFL) algorithm.
Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search
Authors: Seunghun Lee, Jihong Park, Jinho Choi, HyunCheol Park
Date: 2025-06-24 00:00:00

Tokens are fundamental processing units of generative AI (GenAI) and large language models (LLMs), and token communication (TC) is essential for enabling remote AI-generate content (AIGC) and wireless LLM applications. Unlike traditional bits, each of which is independently treated, the semantics of each token depends on its surrounding context tokens. This inter-token dependency makes TC vulnerable to outage channels, where the loss of a single token can significantly distort the original message semantics. Motivated by this, this paper focuses on optimizing token packetization to maximize the average token similarity (ATS) between the original and received token messages under outage channels. Due to inter-token dependency, this token grouping problem is combinatorial, with complexity growing exponentially with message length. To address this, we propose a novel framework of semantic packet aggregation with lookahead search (SemPA-Look), built on two core ideas. First, it introduces the residual semantic score (RSS) as a token-level surrogate for the message-level ATS, allowing robust semantic preservation even when a certain token packet is lost. Second, instead of full search, SemPA-Look applies a lookahead search-inspired algorithm that samples intra-packet token candidates without replacement (fixed depth), conditioned on inter-packet token candidates sampled with replacement (fixed width), thereby achieving linear complexity. Experiments on a remote AIGC task with the MS-COCO dataset (text captioned images) demonstrate that SemPA-Look achieves high ATS and LPIPS scores comparable to exhaustive search, while reducing computational complexity by up to 40$\times$. Compared to other linear-complexity algorithms such as the genetic algorithm (GA), SemPA-Look achieves 10$\times$ lower complexity, demonstrating its practicality for remote AIGC and other TC applications.
Holographic Communication via Recordable and Reconfigurable Metasurface
Authors: Jinzhe Wang, Qinghua Guo, Xiaojun Yuan
Date: 2025-06-24 00:00:00

Holographic surface based communication technologies are anticipated to play a significant role in the next generation of wireless networks. The existing reconfigurable holographic surface (RHS)-based scheme only utilizes the reconstruction process of the holographic principle for beamforming, where the channel sate information (CSI) is needed. However, channel estimation for CSI acquirement is a challenging task in metasurface based communications. In this study, inspired by both the recording and reconstruction processes of holography, we develop a novel holographic communication scheme by introducing recordable and reconfigurable metasurfaces (RRMs), where channel estimation is not needed thanks to the recording process. Then we analyze the input-output mutual information of the RRM-based communication system and compare it with the existing RHS based system. Our results show that, without channel estimation, the proposed scheme achieves performance comparable to that of the RHS scheme with perfect CSI, suggesting a promising alternative for future wireless communication networks.