pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Intelligent Sensing and Communications for 6G: Architecture and Use Cases
Authors: Kyriakos Stylianopoulos, Giyyarpuram Madhusudan, Guillaume Jornod, Sami Mekki, Francesca Costanzo, Hui Chen, Placido Mursia, Maurizio Crozzoli, Emilio Calvanese Strinati, George C. Alexandropoulos, Henk Wymeersch
Date: 2025-04-17 00:00:00
Tasks: Decision Making, Integrated sensing and communication, ISAC

The Distributed Intelligent Sensing and Communication (DISAC) framework redefines Integrated Sensing and Communication (ISAC) for 6G by leveraging distributed architectures to enhance scalability, adaptability, and resource efficiency. This paper presents key architectural enablers, including advanced data representation, seamless target handover, support for heterogeneous devices, and semantic integration. Two use cases illustrate the transformative potential of DISAC: smart factory shop floors and Vulnerable Road User (VRU) protection at smart intersections. These scenarios demonstrate significant improvements in precision, safety, and operational efficiency compared to traditional ISAC systems. The preliminary DISAC architecture incorporates intelligent data processing, distributed coordination, and emerging technologies such as Reconfigurable Intelligent Surfaces (RIS) to meet 6G's stringent requirements. By addressing critical challenges in sensing accuracy, latency, and real-time decision-making, DISAC positions itself as a cornerstone for next-generation wireless networks, advancing innovation in dynamic and complex environments.
Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining
Authors: Kyriakos Stylianopoulos, George C. Alexandropoulos
Date: 2025-04-17 00:00:00

In this paper, we demonstrate that an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) wireless system with appropriate analog combining components exhibits the properties of a universal function approximator, similar to a feedforward neural network. By treating the XL MIMO channel coefficients as the random nodes of a hidden layer, and the receiver's analog combiner as a trainable output layer, we cast the end-to-end system to the Extreme Learning Machine (ELM) framework, leading to a novel formulation for Over-The-Air (OTA) edge inference without requiring traditional digital processing nor pre-processing at the transmitter. Through theoretical analysis and numerical evaluation, we showcase that XL-MIMO-ELM enables near-instantaneous training and efficient classification, suggesting the paradigm shift of beyond massive MIMO systems as neural networks alongside their profound communications role. Compared to deep learning approaches and conventional ELMs, the proposed framework achieves on par performance with orders of magnitude lower complexity, making it highly attractive for ultra low power wireless devices.
TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations
Authors: Yihang Lu, Yangyang Xu, Qitao Qing, Xianwei Meng
Date: 2025-04-17 00:00:00
Tasks: Time Series, Time Series Forecasting

Recent deep learning models for Long-term Time Series Forecasting (LTSF) often emphasize complex, handcrafted designs, while simpler architectures like linear models or MLPs have often outperformed these intricate solutions. In this paper, we revisit and organize the core ideas behind several key techniques, such as redundancy reduction and multi-scale modeling, which are frequently employed in advanced LTSF models. Our goal is to streamline these ideas for more efficient deep learning utilization. To this end, we introduce TimeCapsule, a model built around the principle of high-dimensional information compression that unifies these techniques in a generalized yet simplified framework. Specifically, we model time series as a 3D tensor, incorporating temporal, variate, and level dimensions, and leverage mode production to capture multi-mode dependencies while achieving dimensionality compression. We propose an internal forecast within the compressed representation domain, supported by the Joint-Embedding Predictive Architecture (JEPA), to monitor the learning of predictive representations. Extensive experiments on challenging benchmarks demonstrate the versatility of our method, showing that TimeCapsule can achieve state-of-the-art performance.
High-Resolution Multipath Angle Estimation Based on Power-Angle-Delay Profile for Directional Scanning Sounding
Authors: Huixin Xu, Jianhua Zhang, Pan Tang, Hongbo Xing, Lei Tian, Qixing Wang
Date: 2025-04-17 00:00:00

Directional scanning sounding (DSS) has become widely adopted for high-frequency channel measurements because it effectively compensates for severe path loss. However, the resolution of existing multipath component (MPC) angle estimation methods is constrained by the DSS angle sampling interval. Therefore, this communication proposes a high-resolution MPC angle estimation method based on power-angle-delay profile (PADP) for DSS. By exploiting the mapping relationship between the power difference of adjacent angles in the PADP and MPC offset angle, the resolution of MPC angle estimation is refined, significantly enhancing the accuracy of MPC angle and amplitude estimation without increasing measurement complexity. Numerical simulation results demonstrate that the proposed method reduces the mean squared estimation errors of angle and amplitude by one order of magnitude compared to traditional omnidirectional synthesis methods. Furthermore, the estimation errors approach the Cram\'er-Rao Lower Bounds (CRLBs) derived for wideband DSS, thereby validating its superior performance in MPC angle and amplitude estimation. Finally, experiments conducted in an indoor scenario at 37.5 GHz validate the excellent performance of the proposed method in practical applications.
Sub-Scalp Brain-Computer Interface Device Design and Fabrication
Authors: Timothy B. Mahoney, David B. Grayden, Sam E. John
Date: 2025-04-17 00:00:00
Tasks: Brain Computer Interface, EEG

Current brain-computer interfaces (BCI) face limitations in signal acquisition. While sub-scalp EEG offers a potential solution, existing devices prioritize chronic seizure monitoring and lack features suited for BCI applications. This work addresses this gap by outlining key specifications for sub-scalp BCI devices, focusing on channel count, sampling rate, power efficiency, and form factor. We present the Set-And-Forget EEG (SAFE) system, a custom-built amplifier and wireless transmitter meeting these criteria. This compact (12x12 mm), six-channel device offers 1024 Hz sampling and Bluetooth Low Energy data transmission. Validation using generated sinusoids and electrocorticography recordings of visual evoked potentials in sheep models demonstrated low noise recording. Future animal studies will assess sub-scalp EEG signal quality for BCI applications. This data lays the groundwork for human trials, ultimately paving the way for chronic, in-home BCIs that empower individuals with physical disabilities.
AI for CSI Prediction in 5G-Advanced and Beyond
Authors: Chengyong Jiang, Jiajia Guo, Xiangyi Li, Shi Jin, Jun Zhang
Date: 2025-04-17 00:00:00
Tasks: Management, Prediction

Artificial intelligence (AI) is pivotal in advancing fifth-generation (5G)-Advanced and sixth-generation systems, capturing substantial research interest. Both the 3rd Generation Partnership Project (3GPP) and leading corporations champion AI's standardization in wireless communication. This piece delves into AI's role in channel state information (CSI) prediction, a sub-use case acknowledged in 5G-Advanced by the 3GPP. We offer an exhaustive survey of AI-driven CSI prediction, highlighting crucial elements like accuracy, generalization, and complexity. Further, we touch on the practical side of model management, encompassing training, monitoring, and data gathering. Moreover, we explore prospects for CSI prediction in future wireless communication systems, entailing integrated design with feedback, multitasking synergy, and predictions in rapid scenarios. This article seeks to be a touchstone for subsequent research in this burgeoning domain.
Attention-Infused Autoencoder for Massive MIMO CSI Compression
Authors: Kangzhi Lou, Xiping Wu
Date: 2025-04-16 00:00:00

As the number of multiple-input multiple-output (MIMO) antennas increases drastically with the development towards 6G systems, channel state information (CSI) compression becomes crucial for mitigating feedback overhead. In recent years, learning models such as autoencoders (AE) have been studied for CSI compression, aiming to eliminate model assumptions and reduce compression loss. However, current learning methods are often designed and trained mainly for individual channel scenarios, with limited generalizability across different scenarios, of which the channel characteristics are prominently discrepant. Motivated by this, we propose a novel AE-based learning method named attention-infused autoencoder network (AiANet), which can parallelly and adaptively extract channel-wise and spatial features of CSI with an attention fusion mechanism. In addition, a locally-aware self-attention mechanism is developed to extract both global and local spatial patterns, to better capture the unique CSI features of different scenarios. Moreover, a mixed-training scheme is introduced to enable the proposed AiANet to gain generalizability across indoor and outdoor scenarios. Results show that when trained and tested in the same scenario, AiANet can substantially outperform the existing AE-based methods such as ACRNet, with an improvement of up to 3.42 dB in terms of normalized mean squared error (NMSE). With the mixed-training scheme, AiANet exhibits superior cross-scenario generalizability compared to the benchmark methods which are trained in one scenario and misused in another.
Experimental Analysis of Multipath Characteristics in Indoor Distributed Massive MIMO Channels
Authors: YingJie Xu, Xuesong Cai, Sara Willhammar, Fredrik Tufvesson
Date: 2025-04-16 00:00:00

Distributed massive multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, has emerged as a promising technology for sixth-generation (6G) wireless networks. This letter introduces an indoor channel measurement campaign designed to explore the behavior of multipath components (MPCs) in distributed massive MIMO channels. Fully coherent channels were measured between eight distributed uniform planar arrays (128 elements in total) and a 12-meter user equipment route. Furthermore, a method is introduced to determine the order (single- or multi-bounce) of MPC interaction by leveraging map information and MPC parameters. In addition, a Kalman filter-based framework is used for identifying the MPC interaction mechanisms (reflection or scattering/diffraction/mixed). Finally, a comprehensive MPC-level characterization is performed based on the measured channels, including the significance of the single-bounce MPCs, the spherical wavefront features, the birth-and-death processes of the MPCs, and the spatial distribution of reflections. The findings serve as a valuable reference for understanding MPC propagation behavior, which is necessary for accurate modeling of indoor distributed massive MIMO channels.
Mobile Distributed MIMO (MD-MIMO) for NextG: Mobility Meets Cooperation in Distributed Arrays
Authors: Karim A. Said, Yibin Liang, Usama Saeed, Ramin Safavinejad, Kumar Sai Bondada, Evan Allen, Nima Mohammadi, Benjamin Pimentel, Brian Kelley, Jeff Reed, Nishith Tripathi, Michael Buehrer, Yang, Yi, Daniel Jakubisin, Lingjia Liu
Date: 2025-04-16 00:00:00

Distributed multiple-input multiple-output (D\mbox{-}MIMO) is a promising technology to realize the promise of massive MIMO gains by fiber-connecting the distributed antenna arrays, thereby overcoming the form factor limitations of co-located MIMO. In this paper, we introduce the concept of mobile D-MIMO (MD-MIMO) network, a further extension of the D-MIMO technology where distributed antenna arrays are connected to the base station with a wireless link allowing all radio network nodes to be mobile. This approach significantly improves deployment flexibility and reduces operating costs, enabling the network to adapt to the highly dynamic nature of next-generation (NextG) networks. We discuss use cases, system design, network architecture, and the key enabling technologies for MD-MIMO. Furthermore, we investigate a case study of MD-MIMO for vehicular networks, presenting detailed performance evaluations for both downlink and uplink. The results show that an MD-MIMO network can provide substantial improvements in network throughput and reliability.
Interacting Object-Enabled Clustering and Characterization of Distributed MIMO Channels
Authors: YingJie Xu, Michiel Sandra, Xuesong Cai, Sara Willhammar, Fredrik Tufvesson
Date: 2025-04-16 00:00:00
Tasks: Clustering

Distributed multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, emerges as a promising technology for sixth-generation (6G) systems to support uniform coverage and reliable communication. For the design and optimization of such systems, measurement-based investigations of real-world distributed MIMO channels are essential. In this paper, we present an indoor channel measurement campaign, featuring eight distributed antenna arrays with 128 elements in total. Multi-link channels are measured at 50 positions along a 12-meter user route. A clustering algorithm enabled by interacting objects is proposed to identify clusters in the measured channels. The algorithm jointly clusters the multipath components for all links, effectively capturing the dynamic contributions of common clusters to different links. In addition, a Kalman filter-based tracking framework is introduced for cluster prediction, tracking, and updating along the user movement. Using the clustering and tracking results, cluster-level characterization of the measured channels is performed. First, the number of clusters and their visibility at both link ends are analyzed. Next, a maximum-likelihood estimator is utilized to determine the entire cluster visibility region length. Finally, key cluster-level properties, including the common cluster ratio, cluster power, shadowing, spread, among others, are statistically investigated. The results provide valuable insights into cluster behavior in typical multi-link channels, necessary for accurate modeling of distributed MIMO channels.
Deep Generative Models for Bayesian Inference on High-Rate Sensor Data: Applications in Automotive Radar and Medical Imaging
Authors: Tristan S. W. Stevens, Jeroen Overdevest, Oisín Nolan, Wessel L. van Nierop, Ruud J. G. van Sloun, Yonina C. Eldar
Date: 2025-04-16 00:00:00
Tasks: Bayesian Inference, Denoising, Image Restoration, Super-Resolution

Deep generative models have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restoration, such as denoising, inpainting, and super-resolution. In recent years, generative modeling for Bayesian inference on sensory data has also gained traction. Nevertheless, the direct application of generative modeling techniques initially designed for natural images on raw sensory data is not straightforward, requiring solutions that deal with high dynamic range signals acquired from multiple sensors or arrays of sensors that interfere with each other, and that typically acquire data at a very high rate. Moreover, the exact physical data-generating process is often complex or unknown. As a consequence, approximate models are used, resulting in discrepancies between model predictions and the observations that are non-Gaussian, in turn complicating the Bayesian inverse problem. Finally, sensor data is often used in real-time processing or decision-making systems, imposing stringent requirements on, e.g., latency and throughput. In this paper, we will discuss some of these challenges and offer approaches to address them, all in the context of high-rate real-time sensing applications in automotive radar and medical imaging.
Super-LoRa: Enhancing LoRa Throughput via Payload Superposition
Authors: Salah Abdeljabar, Mohamed-Slim Alouini
Date: 2025-04-16 00:00:00

This paper presents Super-LoRa, a novel approach to enhancing the throughput of LoRa networks by leveraging the inherent robustness of LoRa modulation against interference. By superimposing multiple payload symbols, Super-LoRa significantly increases the data rate while maintaining lower transmitter and receiver complexity. Our solution is evaluated through both simulations and real-world experiments, showing a potential throughput improvement of up to 5x compared to standard LoRa. This advancement positions Super-LoRa as a viable solution for data-intensive IoT applications such as smart cities and precision agriculture, which demand higher data transmission rates.
Channel-Adaptive Robust Resource Allocation for Highly Reliable IRS-Assisted V2X Communications
Authors: Peng Wang, Weihua Wu
Date: 2025-04-16 00:00:00
Tasks: Self-Learning

This paper addresses the challenges of resource allocation in vehicular networks enhanced by Intelligent Reflecting Surfaces (IRS), considering the uncertain Channel State Information (CSI) typical of vehicular environments due to the Doppler shift. Leveraging the 3GPP's Mode 1 cellular V2X architecture, our system model facilitates efficient subcarrier usage and interference reduction through coordinated V2I and V2V communications. Each Cellular User Equipment (CUE) shares its spectrum with at most one Vehicular User Equipment (VUE) in a one-to-one reuse pattern. We formulate a joint optimization problem for vehicular transmit power, Multi-User Detection (MUD) matrices, V2V link spectrum reuse, and IRS reflection coefficients in IRS-aided V2X communication with imperfect CSI. To tackle this, a novel robust resource allocation algorithm is developed by first decomposing the problem into manageable sub-problems such as power allocation, MUD matrices optimization and IRS phase shifts, and then using the Block Coordinate Descent (BCD) method to alternately optimize these subproblems for optimal resource allocation. Our contributions include efficient approaches for self-learning based power allocation and phase shift optimization that adapt to CSI uncertainties, significantly enhancing the reliability and efficiency of vehicular communications. Simulation results validate the effectiveness of the proposed solutions in improving the Quality of Service (QoS) and managing the complex interference inherent in dense vehicular networks.
Scalable Multi-task Edge Sensing via Task-oriented Joint Information Gathering and Broadcast
Authors: Huawei Hou, Suzhi Bi, Xian Li, Shuoyao Wang, LiPing Qian, Zhi Quan
Date: 2025-04-16 00:00:00
Tasks: Edge-computing

The recent advance of edge computing technology enables significant sensing performance improvement of Internet of Things (IoT) networks. In particular, an edge server (ES) is responsible for gathering sensing data from distributed sensing devices, and immediately executing different sensing tasks to accommodate the heterogeneous service demands of mobile users. However, as the number of users surges and the sensing tasks become increasingly compute-intensive, the huge amount of computation workloads and data transmissions may overwhelm the edge system of limited resources. Accordingly, we propose in this paper a scalable edge sensing framework for multi-task execution, in the sense that the computation workload and communication overhead of the ES do not increase with the number of downstream users or tasks. By exploiting the task-relevant correlations, the proposed scheme implements a unified encoder at the ES, which produces a common low-dimensional message from the sensing data and broadcasts it to all users to execute their individual tasks. To achieve high sensing accuracy, we extend the well-known information bottleneck theory to a multi-task scenario to jointly optimize the information gathering and broadcast processes. We also develop an efficient two-step training procedure to optimize the parameters of the neural network-based codecs deployed in the edge sensing system. Experiment results show that the proposed scheme significantly outperforms the considered representative benchmark methods in multi-task inference accuracy. Besides, the proposed scheme is scalable to the network size, which maintains almost constant computation delay with less than 1% degradation of inference performance when the user number increases by four times.
Properties of Near Field Focusing for Cylindrical Dipole Arrays in Enclosed Array Volume
Authors: Jiawang Li
Date: 2025-04-16 00:00:00

Motivated by large intelligent surface applications, the electric field properties of near field focusing using phase conjugation method are analyzed for cylindrical dipole arrays. Firstly, for the transmitting antennas featuring vertical polarization, the polarization characteristic is decomposed along the x, y, and z directions. Three typical cases are studied when the focal points are at (xf , 0, 0), (0, yf , 0), and (0, 0, zf ). When the length of the cylindrical dipole array is significantly larger compared to its radius, certain unique insights emerge. When the focal point is positioned along (0, 0, zf ), apart from the region on both sides, the ratio between Ez and Ex/Ey remains {\pi}/2. Additionally, When the focal point is located within the cylinder, the electric field of each polarization is approximately the same everywhere. In other words, beam focusing does not incur losses due to different positions. The focusing resolution of Ez is the same in the transverse and longitudinal directions. Different from the situation where the 3 - dB focal beam depth is much smaller than the focal beam width for the most of arrays, the resolution in the longitudinal can be improved, respectively. Through a comprehensive grasp of these design principles, we can gain a deeper understanding of the specific areas with significant potential for practical applications.
EdgePrompt: A Distributed Key-Value Inference Framework for LLMs in 6G Networks
Authors: Jiahong Ning, Pengyan Zhu, Ce Zheng, Gary Lee, Sumei Sun, Tingting Yang
Date: 2025-04-16 00:00:00
Tasks: Management, Privacy Preserving

As sixth-generation (6G) networks advance, large language models (LLMs) are increasingly integrated into 6G infrastructure to enhance network management and intelligence. However, traditional LLMs architecture struggle to meet the stringent latency and security requirements of 6G, especially as the increasing in sequence length leads to greater task complexity. This paper proposes Edge-Prompt, a cloud-edge collaborative framework based on a hierarchical attention splicing mechanism. EdgePrompt employs distributed key-value (KV) pair optimization techniques to accelerate inference and adapt to network conditions. Additionally, to reduce the risk of data leakage, EdgePrompt incorporates a privacy preserving strategy by isolating sensitive information during processing. Experiments on public dataset show that EdgePrompt effectively improves the inference throughput and reduces the latency, which provides a reliable solution for LLMs deployment in 6G environments.