pwc-archive/papers-with-abstracts huggingface dataset:
Design and Development of a Robust Tolerance Optimisation Framework for Automated Optical Inspection in Semiconductor Manufacturing
Authors: Shruthi Kogileru, Mark McBride, Yaxin Bi, Kok Yew Ng
Date: 2025-05-06 00:00:00

Automated Optical Inspection (AOI) is widely used across various industries, including surface mount technology in semiconductor manufacturing. One of the key challenges in AOI is optimising inspection tolerances. Traditionally, this process relies heavily on the expertise and intuition of engineers, making it subjective and prone to inconsistency. To address this, we are developing an intelligent, data-driven approach to optimise inspection tolerances in a more objective and consistent manner. Most existing research in this area focuses primarily on minimising false calls, often at the risk of allowing actual defects to go undetected. This oversight can compromise product quality, especially in critical sectors such as medical, defence, and automotive industries. Our approach introduces the use of percentile rank, amongst other logical strategies, to ensure that genuine defects are not overlooked. With continued refinement, our method aims to reach a point where every flagged item is a true defect, thereby eliminating the need for manual inspection. Our proof of concept achieved an 18% reduction in false calls at the 80th percentile rank, while maintaining a 100% recall rate. This makes the system both efficient and reliable, offering significant time and cost savings.
Memory-Efficient Distributed Unlearning
Authors: Natalie Lang, Alon Helvitz, Nir Shlezinger
Date: 2025-05-06 00:00:00
Tasks: Machine Unlearning

Machine unlearning considers the removal of the contribution of a set of data points from a trained model. In a distributed setting, where a server orchestrates training using data available at a set of remote users, unlearning is essential to cope with late-detected malicious or corrupted users. Existing distributed unlearning algorithms require the server to store all model updates observed in training, leading to immense storage overhead for preserving the ability to unlearn. In this work we study lossy compression schemes for facilitating distributed server-side unlearning with limited memory footprint. We propose memory-efficient distributed unlearning (MEDU), a hierarchical lossy compression scheme tailored for server-side unlearning, that integrates user sparsification, differential thresholding, and random lattice coding, to substantially reduce memory footprint. We rigorously analyze MEDU, deriving an upper bound on the difference between the desired model that is trained from scratch and the model unlearned from lossy compressed stored updates. Our bound outperforms the state-of-the-art known bounds for non-compressed decentralized server-side unlearning, even when lossy compression is incorporated. We further provide a numerical study, which shows that suited lossy compression can enable distributed unlearning with notably reduced memory footprint at the server while preserving the utility of the unlearned model.
Hybrid NOMA Assisted Heterogeneous Semantic and Bit Users Communication
Authors: Ishtiaque Ahmed, Leila Musavian
Date: 2025-05-06 00:00:00

In this paper, we utilize a downlink hybrid Non-Orthogonal Multiple Access (NOMA) framework to support multiple semantic and bit users within the communication network. The hybrid NOMA setup exploits both NOMA and Orthogonal Multiple Access (OMA) which has the benefit of enhancing Spectral Efficiency (SE) by allowing users to dynamically access the resources in multiple heterogeneous slots. This enables integrating semantic and bit users based on their channel gains, while adopting bit-to-semantic decoding order in slots including heterogeneous users. An optimization problem for the power allocation is formulated with the aim of maximizing the equivalent ergodic semantic SE with a constraint on the total available power of the Access Point (AP). The proposed algorithm uses NOMA in shared slots and OMA in bit-user-only slots. Simulation results validate the benefits of heterogeneous users hybrid NOMA setup in comparison to OMA-only for heterogeneous users.
Multi-dimensional Parameter Estimation in RIS-aided MU-MIMO Channels
Authors: Linlin Mo, Yi Song, Fabio Saggese, Xinhua Lu, Zhongyong Wang, Petar Popovski
Date: 2025-05-05 00:00:00
Tasks: parameter estimation

We address the channel estimation problem in reconfigurable intelligent surface (RIS) aided broadband systems by proposing a dual-structure and multi-dimensional transformations (DS-MDT) algorithm. The proposed approach leverages the dual-structure features of the channel parameters to assist users experiencing weaker channel conditions, thereby enhancing estimation performance. Moreover, given that the channel parameters are distributed across multiple dimensions of the received tensor, the proposed algorithm employs multi-dimensional transformations to effectively isolate and extract distinct parameters. The numerical results demonstrate the proposed algorithm reduces the normalized mean square error (NMSE) by up to 10 dB while maintaining lower complexity compared to state-of-the-art methods.
Sensing Framework Design and Performance Optimization with Action Detection for ISCC
Authors: Weiwei Chen, Yinghui He, Guanding Yu, JianFeng Wang, Haiyan Luo
Date: 2025-05-05 00:00:00
Tasks: Action Detection

Integrated sensing, communication, and computation (ISCC) has been regarded as a prospective technology for the next-generation wireless network, supporting humancentric intelligent applications. However, the delay sensitivity of these computation-intensive applications, especially in a multidevice ISCC system with limited resources, highlights the urgent need for efficient sensing task execution frameworks. To address this, we propose a resource-efficient sensing framework in this paper. Different from existing solutions, it features a novel action detection module deployed at each device to detect the onset of an action. Only time windows filled with signals of interest are offloaded to the edge server and processed by the edge recognition module, thus reducing overhead. Furthermore, we quantitatively analyze the sensing performance of the proposed sensing framework and formulate a sensing accuracy maximization problem under power, delay, and resource limitations for the multi-device ISCC system. By decomposing it into two subproblems, we develop an alternating direction method of multipliers (ADMM)-based distributed algorithm. It alternatively solves a sensing accuracy maximization subproblem at each device and employs a closed-form computation resource allocation strategy at the edge server till convergence. Finally, a real-world test is conducted using commodity wireless devices to validate the sensing performance analysis. Extensive test results demonstrate that our proposal achieves higher sensing accuracy under the limited resource compared to two baselines.
Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems
Authors: Kai Zhang, Wentao Yu, Hengtao He, Shenghui Song, Jun Zhang, Khaled B. Letaief
Date: 2025-05-05 00:00:00
Tasks: Beam Prediction, Deep Learning, Integrated sensing and communication, ISAC, Mixture-of-Experts, Multimodal Deep Learning, Prediction

Integrated sensing and communication (ISAC) systems operating at terahertz (THz) bands are envisioned to enable both ultra-high data-rate communication and precise environmental awareness for next-generation wireless networks. However, the narrow width of THz beams makes them prone to misalignment and necessitates frequent beam prediction in dynamic environments. Multimodal sensing, which integrates complementary modalities such as camera images, positional data, and radar measurements, has recently emerged as a promising solution for proactive beam prediction. Nevertheless, existing multimodal approaches typically employ static fusion architectures that cannot adjust to varying modality reliability and contributions, thereby degrading predictive performance and robustness. To address this challenge, we propose a novel and efficient multimodal mixture-of-experts (MoE) deep learning framework for proactive beam prediction in THz ISAC systems. The proposed multimodal MoE framework employs multiple modality-specific expert networks to extract representative features from individual sensing modalities, and dynamically fuses them using adaptive weights generated by a gating network according to the instantaneous reliability of each modality. Simulation results in realistic vehicle-to-infrastructure (V2I) scenarios demonstrate that the proposed MoE framework outperforms traditional static fusion methods and unimodal baselines in terms of prediction accuracy and adaptability, highlighting its potential in practical THz ISAC systems with ultra-massive multiple-input multiple-output (MIMO).
Tri-Hybrid Multi-User Precoding Based on Electromagnetically Reconfigurable Antennas
Authors: Pinjun Zheng, Yuchen Zhang, Tareq Y. Al-Naffouri, Md. Jahangir Hossain, Anas Chaaban
Date: 2025-05-04 00:00:00

The tri-hybrid precoding architecture based on electromagnetically reconfigurable antennas (ERAs) is a promising solution for overcoming key limitations in multiple-input multiple-output communication systems. Aiming to further understand its potential, this paper investigates the tri-hybrid multi-user precoding problem using pattern reconfigurable ERAs. To reduce model complexity and improve practicality, we characterize each antenna's radiation pattern using a spherical harmonics decomposition. While mathematically tractable, this approach may lead to over-optimized patterns that are physically unrealizable. To address this, we introduce a projection step that maps the optimized patterns onto a realizable set. Simulation results demonstrate that spherical harmonics-based radiation pattern optimization significantly enhances sum rate performance. However, after projection onto a realizable set obtained from real ERA hardware, the performance gain is notably reduced or even negligible, underscoring the need for more effective projection techniques and improved reconfigurable antenna hardware.
Spreading over OFDM for Integrated Sensing and Communications (ISAC) Ranging: Multi-user Interference Mitigation
Authors: Karim A. Said, Ibraheem Alturki, Husheng Li, Elizabeth Bentley, Lingjia Liu
Date: 2025-05-04 00:00:00
Tasks: Integrated sensing and communication, ISAC

In the context of communication-centric integrated sensing and communication (ISAC), the orthogonal frequency division multiplexing (OFDM) waveform was proven to be optimal in minimizing ranging sidelobes when random signaling is used. A typical assumption in OFDM-based ranging is that the max target delay is less than the cyclic prefix (CP) length, which is equivalent to performing a \textit{periodic} correlation between the signal reflected from the target and the transmitted signal. In the multi-user case, such as in Orthogonal Frequency Division Multiple Access (OFDMA), users are assigned disjoint subsets of subcarriers which eliminates mutual interference between the communication channels of the different users. However, ranging involves an aperiodic correlation operation for target ranges with delays greater than the CP length. Aperiodic correlation between signals from disjoint frequency bands will not be zero, resulting in mutual interference between different user bands. We refer to this as \textit{inter-band} (IB) cross-correlation interference. In this work, we analytically characterize IB interference and quantify its impact on the integrated sidelobe levels (ISL). We introduce an orthogonal spreading layer on top of OFDM that can reduce IB interference resulting in ISL levels significantly lower than for OFDM without spreading in the multi-user setup. We validate our claims through simulations, and using an upper bound on IB energy which we show that it can be minimized using our proposed spreading. However, for orthogonal spreading to be effective, a price must be paid in terms of spectral utilization, which is yet another manifestation of the trade-off between sensing accuracy and data communication capacity
Physical-Layer Security in Mixed Near-Field and Far-Field Communication Systems
Authors: Tianyu Liu, Changsheng You, Cong Zhou, Yunpu Zhang, Shiqi Gong, Heng Liu, Guangchi Zhang
Date: 2025-04-28 00:00:00

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology to improve the spectrum efficiency and spatial resolution of future wireless systems. Different from existing works that mostly considered physical layer security (PLS) in either the far-field or near-field, we consider in this paper a new and practical scenario, where legitimate users (Bobs) are located in the far-field of a base station (BS) while eavesdroppers (Eves) are located in the near-field for intercepting confidential information at short distance, referred to as the mixed near-field and far-field PLS. Specifically, we formulate an optimization problem to maximize the sum-secrecy-rate of all Bobs by optimizing the power allocation of the BS, subject to the constraint on the total BS transmit power. To shed useful insights, we first consider a one-Bob-one-Eve system and characterize the insecure-transmission region of the Bob in closed form. Interestingly, we show that the insecure-transmission region is significantly \emph{expanded} as compared to that in conventional far-field PLS systems, due to the energy-spread effect in the mixed-field scenario. Then, we further extend the analysis to a two-Bob-one-Eve system. It is revealed that as compared to the one-Bob system, the interferences from the other Bob can be effectively used to weaken the capability of Eve for intercepting signals of target Bobs, thus leading to enhanced secrecy rates. Furthermore, we propose an efficient algorithm to obtain a high-quality solution to the formulated non-convex problem by leveraging the successive convex approximation (SCA) technique. Finally, numerical results demonstrate that our proposed algorithm achieves a higher sum-secrecy-rate than the benchmark scheme where the power allocation is designed based on the (simplified) far-field channel model.
Predictive and Proactive Power Allocation For Energy Efficiency in Dynamic OWC Networks
Authors: Walter Zibusiso Ncube, Ahmad Adnan Qidan, Taisir El-Gorashi, Jaafar M. H. Elmirghani
Date: 2025-05-03 00:00:00

Driven by the exponential growth in data traffic and the limitations of Radio Frequency (RF) networks, Optical Wireless Communication (OWC) has emerged as a promising solution for high data rate communication. However, the inherently dynamic nature of OWC environments resulting from user mobility, and time-varying user demands poses significant challenges for enhanced and sustainable performance. Energy efficiency (EE) is a critical metric for the sustainable operation of next generation wireless networks. Achieving high EE in dynamic OWC environments, especially under time-varying user distributions and heterogeneous service requirements, remains a complex task. In this work, we formulate a discrete-time EE optimisation problem in a dynamic OWC to maximise energy efficiency through determining user connectivity and power allocation. Solving this problem in real time is computationally demanding due to the coupling of user association and power allocation variables over discrete time slots. Therefore, we propose a Probabilistic Demand Prediction and Optimised Power Allocation (PDP-OPA) framework which predicts user arrivals, departures, and traffic demands. Based on these predictions, the framework proactively determines AP-user associations and allocates power dynamically using a Lagrangian-based optimisation strategy. Simulation results demonstrate that the proposed PDP-OPA significantly enhances system performance, providing solutions close to the optimal ones. The proposed framework improves energy efficiency, sum rate, and bit error rate (BER) compared to distance-based user association and uniform power allocation, validating its effectiveness for real time and adaptive resource management in dynamic OWC systems.
Securing 5G and Beyond-Enabled UAV Networks: Resilience Through Multiagent Learning and Transformers Detection
Authors: Joseanne Viana, Hamed Farkhari, Victor P Gil Jimenez
Date: 2025-05-03 00:00:00
Tasks: Multi-agent Reinforcement Learning

Achieving resilience remains a significant challenge for Unmanned Aerial Vehicle (UAV) communications in 5G and 6G networks. Although UAVs benefit from superior positioning capabilities, rate optimization techniques, and extensive line-of-sight (LoS) range, these advantages alone cannot guarantee high reliability across diverse UAV use cases. This limitation becomes particularly evident in urban environments, where UAVs face vulnerability to jamming attacks and where LoS connectivity is frequently compromised by buildings and other physical obstructions. This paper introduces DET-FAIR- WINGS ( Detection-Enhanced Transformer Framework for AI-Resilient Wireless Networks in Ground UAV Systems), a novel solution designed to enhance reliability in UAV communications under attacks. Our system leverages multi-agent reinforcement learning (MARL) and transformer-based detection algorithms to identify attack patterns within the network and subsequently select the most appropriate mechanisms to strengthen reliability in authenticated UAV-Base Station links. The DET-FAIR-WINGS approach integrates both discrete and continuous parameters. Discrete parameters include retransmission attempts, bandwidth partitioning, and notching mechanisms, while continuous parameters encompass beam angles and elevations from both the Base Station (BS) and user devices. The detection part integrates a transformer in the agents to speed up training. Our findings demonstrate that replacing fixed retransmission counts with AI-integrated flexible approaches in 5G networks significantly reduces latency by optimizing decision-making processes within 5G layers.
Artificial Intelligence implementation of onboard flexible payload and adaptive beamforming using commercial off-the-shelf devices
Authors: Luis Manuel Garcés-Socarrás, Amirhosein Nik, Flor Ortiz, Juan A. Vásquez-Peralvo, Jorge Luis González Rios, Mouhamad Chehailty, Marcele Kuhfuss, Eva Lagunas, Jan Thoemel, Sumit Kumar, Vishal Singh, Juan Carlos Merlano Duncan, Sahar Malmir, Swetha Varadajulu, Jorge Querol, Symeon Chatzinotas
Date: 2025-05-03 00:00:00

Very High Throughput satellites typically provide multibeam coverage, however, a common problem is that there can be a mismatch between the capacity of each beam and the traffic demand: some beams may fall short, while others exceed the requirements. This challenge can be addressed by integrating machine learning with flexible payload and adaptive beamforming techniques. These methods allow for dynamic allocation of payload resources based on real-time capacity needs. As artificial intelligence advances, its ability to automate tasks, enhance efficiency, and increase precision is proving invaluable, especially in satellite communications, where traditional optimization methods are often computationally intensive. AI-driven solutions offer faster, more effective ways to handle complex satellite communication tasks. Artificial intelligence in space has more constraints than other fields, considering the radiation effects, the spaceship power capabilities, mass, and area. Current onboard processing uses legacy space-certified general-purpose processors, costly application-specific integrated circuits, or field-programmable gate arrays subjected to a highly stringent certification process. The increased performance demands of onboard processors to satisfy the accelerated data rates and autonomy requirements have rendered current space-graded processors obsolete. This work is focused on transforming the satellite payload using artificial intelligence and machine learning methodologies over available commercial off-the-shelf chips for onboard processing. The objectives include validating artificial intelligence-driven scenarios, focusing on flexible payload and adaptive beamforming as machine learning models onboard. Results show that machine learning models significantly improve signal quality, spectral efficiency, and throughput compared to conventional payload.
Near-field 5D Pose Estimation using Reconfigurable Intelligent Surfaces
Authors: Srikar Sharma Sadhu, Praful D. Mankar, Santosh Nannuru
Date: 2025-05-03 00:00:00
Tasks: Pose Estimation

The advent of 6G is expected to enable many use cases which may rely on accurate knowledge of the location and orientation of user equipment (UE). The conventional localization methods suffer from limitations such as synchronization and high power consumption required for multiple active anchors. This can be mitigated by utilizing a large dimensional passive reconfigurable intelligent surface (RIS). This paper presents a novel low-complexity approach for the estimation of 5D pose (i.e. 3D location and 2D orientation) of a UE in near-field RIS-assisted multiple-input multiple-output (MIMO) systems. The proposed approach exploits the symmetric arrangement of uniform planar array of RIS and uniform linear array of UE to decouple the 5D problem into five 1D sub-problems. Further, we solve these sub-problems using a total least squares ESPRIT inspired approach to obtain closed-form solutions.
Antenna Activation and Resource Allocation in Multi-Waveguide Pinching-Antenna Systems
Authors: Kaidi Wang, Zhiguo Ding, George K. Karagiannidis
Date: 2025-05-03 00:00:00

Pinching antennas, as a novel flexible-antenna technology capable of establishing line of sight (LoS) connections and effectively mitigating large-scale path loss, have recently attracted considerable research interests. However, the implementation of ideal pinching-antenna systems involves determining and adjusting pinching antennas to an arbitrary position on waveguides, which presents challenges to both practical deployment and related optimization. This paper investigates a practical pinching-antennas system in multi-waveguide scenarios, where pinching antennas are installed at pre-configured discrete positions to serve downlink users with non-orthogonal multiple access (NOMA). To improve system throughput, a sophisticated optimization problem is formulated by jointly considering waveguide assignment, antenna activation, successive interference cancellation (SIC) decoding order design, and power allocation. By treating waveguide assignment and antenna activation as two coalition-formation games, a novel game-theoretic algorithm is developed, in which the optimal decoding order is derived and incorporated. For power allocation, monotonic optimization and successive convex approximation (SCA) are employed to construct global optimal and low-complexity solutions, respectively. Simulation results demonstrate that the NOMA-based pinching-antenna system exhibits superior performance compared to the considered benchmark systems, and the proposed solutions provide significant improvement in terms of sum rate and outage probability.