pwc-archive/papers-with-abstracts huggingface dataset:
Tone Reservation-Based PAPR Reduction Using Manifold Optimization for OFDM-ISAC Systems
Authors: Getuar Rexhepi, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, David González G
Date: 2024-09-24 00:00:00
Tasks: Integrated sensing and communication, ISAC

We consider the peak-to-average power ratio (PAPR) reduction challenge of orthogonal frequency division multiplexing (OFDM) systems utilizing tone reservation (TR) under a sensing-enabling constraint, such that the signals placed in the reserved tones (RTs) can be exploited for Integrated Sensing and Communication (ISAC). To that end, the problem is first cast as an unconstrained manifold optimization problem, and then solved via an iterative projected gradient descent algorithm assisted by an approximation of the infinity norm. Simulation results show that the proposed method, while maintaining a level of PAPR reduction similar to state of the art (SotA), not only has lower computational complexity but also outperforms the alternatives in terms of sensing performance.
Joint Long-Term Processed Task and Communication Delay Optimization in UAV-Assisted MEC Systems Using DQN
Authors: Maryam Farajzadeh Dehkordi, Bijan Jabbari
Date: 2024-09-24 00:00:00
Tasks: Edge-computing

Mobile Edge Computing (MEC) assisted by Unmanned Aerial Vehicle (UAV) has been widely investigated as a promising system for future Internet-of-Things (IoT) networks. In this context, delay-sensitive tasks of IoT devices may either be processed locally or offloaded for further processing to a UAV or to the cloud. This paper, by attributing task queues to each IoT device, the UAV, and the cloud, proposes a real-time resource allocation framework in a UAV-aided MEC system. Specifically, aimed at characterizing a long-term trade-off between the time-averaged aggregate processed data (PD) and the time-averaged aggregate communication delay (CD), a resource allocation optimization problem is formulated. This problem optimizes communication and computation resources as well as the UAV motion trajectory, while guaranteeing queue stability. To address this long-term time-averaged problem, a Lyapunov optimization framework is initially leveraged to obtain an equivalent short-term optimization problem. Subsequently, we reformulate the short-term problem in a Markov Decision Process (MDP) form, where a Deep Q Network (DQN) model is trained to optimize its variables. Extensive simulations demonstrate that the proposed resource allocation scheme improves the system performance by up to 36\% compared to baseline models.
BCRLB Under the Fusion Extended Kalman Filter
Authors: Mushen Lin, Fenggang Yan, Lingda Ren, Xiangtian Meng, Maria Greco, Fulvio Gini, Ming Jin
Date: 2024-09-24 00:00:00

In the process of tracking multiple point targets in space using radar, since the targets are spatially well separated, the data between them will not be confused. Therefore, the multi-target tracking problem can be transformed into a single-target tracking problem. However, the data measured by radar nodes contains noise, clutter, and false targets, making it difficult for the fusion center to directly establish the association between radar measurements and real targets. To address this issue, the Probabilistic Data Association (PDA) algorithm is used to calculate the association probability between each radar measurement and the target, and the measurements are fused based on these probabilities. Finally, an extended Kalman filter (EKF) is used to predict the target states. Additionally, we derive the Bayesian Cram\'er-Rao Lower Bound (BCRLB) under the PDA fusion framework.
Self-Supervised Representation Learning with Augmentations of Continuous Training Data Improves the Feel and Performance of Myoelectric Control
Authors: Shriram Tallam Puranam Raghu, Dawn MacIsaac, Erik Scheme
Date: 2024-09-24 00:00:00
Tasks: Representation Learning, Self-Supervised Learning

Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and continuous dynamic data augmented with a self-supervised learning technique (VICReg). An online Fitts' Law test with $20$ participants evaluated the usability and effectiveness of each classifier. Results demonstrate that temporal models, particularly LSTMs trained with continuous dynamic data, significantly outperformed traditional approaches. Furthermore, VICReg pre-training led to additional improvements in online performance and user experience. Qualitative feedback highlighted the importance of smooth, jitter-free control and consistent performance across movement classes. These findings underscore the potential of continuous dynamic data and self-supervised learning for advancing sEMG-PR-based myoelectric control, paving the way for more intuitive and user-friendly prosthetic devices.
EMF-Aware Waveform for Dual-functional Radar Communication Systems
Authors: Mariem Chemingui, Ahmed Elzanaty, Rahim Tafazolli
Date: 2024-09-24 00:00:00

Emerging dual-functional radar communication(RadCom) systems promise to revolutionize wireless systems by enabling radar sensing and communication on a shared platform, thereby enhancing spectral efficiency. However, the high transmit power required for efficient radar operation poses risks by potentially exceeding the electromagnetic field (EMF) exposure limits enforced by the regulations. To address this challenge, we propose an EMF-aware signalling design that enhances RadCom system performance while complying with EMF constraints. Our approach considers exposure levels not only experienced by network users but also in sensitive areas such as schools and hospitals, where the exposure must be further reduced. First, we model the exposure metric for the users and the sectors that encounter sensitive areas. Then, we design the waveform by exploiting the trade-off between radar and communication while satisfying the exposure constraints. We reformulate the problem as a convex optimization program and solve it in closed form using Karush Kuhn Tucker (KKT) conditions. The numerical results demonstrate the feasibility of developing a robust RadCom system with low electromagnetic (EM) radiations.
Movable Antenna Enabled ISAC Beamforming Design for Low-Altitude Airborne Vehicles
Authors: Yue Xiu, Songjie Yang, Wanting Lyu, Phee Lep Yeoh, Yonghui Li, Yi Ai
Date: 2024-09-24 00:00:00
Tasks: Integrated sensing and communication, ISAC, Position

In mobile systems with low-altitude vehicles, integrated sensing and communication (ISAC) is considered an effective approach to increase the transmission rate due to limited spectrum resources. To further improve the ISAC performance, this paper proposes a novel method called integrated sensing and communication-movable antenna (ISAC-MA) to optimize the antenna's position. Our goal is to support low-space vehicles by optimizing radar and communication joint beamforming and antenna position in the presence of clutter. This scheme not only guarantees the required signal-to-noise ratio (SNR) for sensing but also further improves the SNR for communication. A successive convex approximation (SCA)-based block coordinate descent (BCD) algorithm is proposed to maximize communication capacity under the condition of sensing SNR. Numerical results show that, compared with the traditional ISAC system and various benchmark schemes, the proposed ISAC-MA system can achieve higher communication capacity under the same sensing SNR constraints.
Robust Beamforming Design for Near-Field DMA-NOMA mmWave Communications With Imperfect Position Information
Authors: Yue Xiu, Yang Zhao, Songjie Yang, Yufeng Zhang, Dusit Niyato, Hongyang Du, Ning Wei
Date: 2024-09-24 00:00:00
Tasks: Position

For millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communication systems, we propose an innovative near-field (NF) transmission framework based on dynamic metasurface antenna (DMA) technology. In this framework, a base station (BS) utilizes the DMA hybrid beamforming technology combined with the NOMA principle to maximize communication efficiency between near-field users (NUs) and far-field users (FUs). In conventional communication systems, obtaining channel state information (CSI) requires substantial pilot signals, significantly reducing system communication efficiency. We propose a beamforming design scheme based on position information to address with this challenge. This scheme does not depend on pilot signals but indirectly obtains CSI by analyzing the geometric relationship between user position information and channel models. However, in practical applications, the accuracy of position information is challenging to guarantee and may contain errors. We propose a robust beamforming design strategy based on the worst-case scenario to tackle this issue. Facing with the multi-variable coupled non-convex problems, we employ a dual-loop iterative joint optimization algorithm to update beamforming using block coordinate descent (BCD) and derive the optimal power allocation (PA) expression. We analyze its convergence and complexity to verify the proposed algorithm's performance and robustness thoroughly. We validate the theoretical derivation of the CSI error bound through simulation experiments. Numerical results show that our proposed scheme performs better than traditional beamforming schemes. Additionally, the transmission framework exhibits strong robustness to NU and FU position errors, laying a solid foundation for the practical application of mmWave NOMA communication systems.
Self Calibration by ON/OFF Sensitivity Switching - Feasibility Study of a Resonant Accelerometer
Authors: Alexandra Zobova, Slava Krylov
Date: 2024-09-24 00:00:00
Tasks: Sensitivity

This research provides the theoretical feasibility study of a novel architecture of a MEMS differential resonant accelerometer, with switchable and tunable electrostatic transmission between the proof mass and the vibrating sensing beams. The same beams are used for sensing of the inertial force, while the transmission is switched ON, and for the device's calibration, when the transmission is OFF. Therefore, the beams' response in the OFF state is affected by the same factors (temperature, electronics, packaging) as in the ON state, with the only exception for the acceleration. This unique ability to physically disconnect the inertial force from the sensing elements opens possibilities for new schemes of the signal processing, including sensitivity tuning, zero-bias correction and on-the-fly self-calibration of the sensor. The device includes a proof mass (PM) and two force-transmitting frames that are attached to the substrate by the suspension springs, such that there is no direct mechanical connection between the PM and the frames. Two identical sensing beams are attached at their ends to the frames and the substrate. When the electrostatic transmission is switched ON by applying a voltage between the PM and frames, the force is transmitted from the PM through the frame to the beams. Disturbance in the electrostatic field, due to the acceleration-dependent displacement of the PM, results in the shift in the beam axial force and, therefore, in its resonant frequency, assuring the device's acceleration sensing. Furthermore, the change in the control voltage tunes the transmission of the input signal, and therefore the scale factor and the dynamic range of the sensor. An analytic model of the generic device is formulated and verified using finite elements analysis. The tunability of the device and the compensation of the scale factor thermal sensitivity are demonstrated using the model.
Accuracy Simulation of MF R-Mode Systems Using TOA Variance
Authors: Jaewon Yu, Pyo-Woong Son
Date: 2024-09-20 00:00:00

To ensure consistent navigation services despite GNSS signal disruptions, Korea is developing the R-Mode system. This study focuses on enhancing the simulation accuracy of the MF R-Mode system's performance by integrating data from the Eocheong transmitter with existing data from the Palmi and Chungju transmitters. Additional measurements from these three transmitters were gathered using the DARBS receiver to model the Time-of-Arrival(TOA) variance. Analysis of this data facilitated the calculation of new constants and transmitter specific jitter values, which were then used to determine coverage areas based on the updated parameters.
Computational Ghost Imaging with Low-Density Parity-Check Code
Authors: Shuang Liu, Yunkai Hu, Jinquan Qi, Shensheng Han, Zihuai Lin
Date: 2024-09-23 00:00:00

Ghost imaging (GI) is a high-resolution imaging technology that has been a subject of interest to many fields in the past 20 years. Most GI researchers focus on the reconstruction of signal under-sampling, nevertheless, how to use information redundancy to improve the result's belief in a complex environment has hardly been studied. Motivated by this, we propose a computational GI system based on the low-density parity-check (LDPC) coded radiation field by exploiting the signal redundancy. The non-ideal factors generated within the imaging process can be eliminated by setting up the matching fading channel model. We have derived the analytical lower bound on the bit error rate for the proposed LDPC-coded GI system. The effectiveness and performance of the LDPC-coded GI system are further validated through numerical and experiment results.
Energy Saving in 6G O-RAN Using DQN-based xApp
Authors: Qiao Wang, Swarna Chetty, Ahmed Al-Tahmeesschi, Xuanyu Liang, Yi Chu, Hamed Ahmadi
Date: 2024-09-23 00:00:00
Tasks: Reinforcement Learning (RL)

Open Radio Access Network (RAN) is a transformative paradigm that supports openness, interoperability, and intelligence, with the O-RAN architecture being the most recognized framework in academia and industry. In the context of Open RAN, the importance of Energy Saving (ES) is heightened, especially with the current direction of network densification in sixth generation of mobile networks (6G). Traditional energy-saving methods in RAN struggle with the increasing dynamics of the network. This paper proposes using Reinforcement Learning (RL), a subset of Machine Learning (ML), to improve ES. We present a novel deep RL method for ES in 6G O-RAN, implemented as xApp (ES-xApp). We developed two Deep Q-Network (DQN)-based ES-xApps. ES-xApp-1 uses RSS and User Equipment (UE) geolocations, while ES-xApp-2 uses only RSS. The proposed models significantly outperformed heuristic and baseline xApps, especially with over 20 UEs. With 50 UEs, 50% of Radio Cards (RCs) were switched off, compared to 17% with the heuristic algorithm. We have observed that more informative inputs may lead to more stable training and results. This paper highlights the necessity of energy conservation in wireless networks and offers practical strategies and evidence for future research and industry practices.
A Cupola-Shaped Multimode Multiport Antenna for Aerial Direction Finding
Authors: Lukas Grundmann, Marc Gerlach, Wolfgang Schäfer, Dirk Manteuffel
Date: 2024-09-23 00:00:00
Tasks: Collision Avoidance

A multimode multiport antenna (M$^3$PA), constisting of a single conducting structure, is proposed for use with the airborne collision avoidance system (ACAS). Its cupola-shaped surface is selected from a set of aerodynamically suitable and symmetric structures. The selection is based on a presented statistical evaluation procedure for direction-finding (DF) antennas and algorithms, which predicts the root mean square error (RMSE) of the estimation using characteristic modes (CMs). A demonstrator antenna and feed network is manufactured and its three ports are measured in an anechoic chamber. The results match the intended far-fields of an electric monopole and two magnetic dipoles. The presented flight test results show that the antenna is capable of unambiguous DF across the desired angular range in a real world environment.
Hybrid Beamforming and Waveform Design for Over-the-air Integrated Signal
Authors: Chonghao Zhao
Date: 2024-09-23 00:00:00
Tasks: Management

The future wireless communications are expected to provide new use scenarios with emerging techniques. This paper focuses on vehicle to everything (V2X) network, where vehicles should cooperatively implement information obtaining, data sharing, and information postprocessing. Conventionally, the above three operations are considered in different layers or separated waveforms, leading to unavoidable interference and inefficient resource management when the number of devices becomes large. In this paper, we exploit the hybrid beamforming to design a cost-effective over-the-air integrated framework, and further consider the integrated waveform design problem with the constant-modulus constrain (CMC) and similarity constraint (SC). To solve these non-convex problems, an alternating optimization (AO) approach is proposed to jointly optimize the digital precoder as well as the hybrid combiner, using the successive convex approximation (SCA) and Riemannian conjugate gradient (RCG) algorithm. Additionally, we use the semidefinite relaxation (SDR) method to handle the practical waveform design problem. Numerical results demonstrate the effectiveness of the proposed hybrid beamforming and waveform design.
Energy-Efficient Multi-UAV-Enabled MEC Systems over Space-Air-Ground Integrated Networks
Authors: Wenchao Liu, Xuhui Zhang, Jinke Ren, Yanyan Shen, Shuqiang Wang, Bo Yang, Xinping Guan, Shuguang Cui
Date: 2024-09-23 00:00:00
Tasks: Edge-computing

With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computation-intensive and latency-sensitive tasks. However, existing mobile computing networks struggle to support the rapidly growing computational needs of the MUs. Fortunately, space-air-ground integrated network (SAGIN) supported mobile edge computing (MEC) is regarded as an effective solution, offering the MUs multi-tier and efficient computing services. In this paper, we consider an SAGIN supported MEC system, where a low Earth orbit satellite and multiple unmanned aerial vehicles (UAVs) are dispatched to provide computing services for MUs. An energy efficiency maximization problem is formulated, with the joint optimization of the MU-UAV association, the UAV trajectory, the task offloading decision, the computing frequency, and the transmission power control. Since the problem is non-convex, we decompose it into four subproblems, and propose an alternating optimization based algorithm to solve it. Simulation results confirm that the proposed algorithm outperforms the benchmarks.
Improving Physical-Layer Security in ISAC-UAV System: Beamforming and Trajectory Optimization
Authors: Yue Xiu, Wanting Lyu, Phee Lep Yeoh, Yi Ai, Yonghui Li, Ning Wei
Date: 2024-09-22 00:00:00
Tasks: ISAC

This paper investigates a novel unmanned aerial vehicle (UAV) secure communication system with integrated sensing and communications. We consider wireless security enhancement for a multiple-antenna UAV transmitting ISAC waveforms to communicate with multiple ground Internet-of-Thing devices and detect the surrounding environment. Specifically, we aim to maximize the average communication secrecy rate by optimizing the UAV trajectory and beamforming vectors. Given that the UAV trajectory optimization problem is non-convex due to multi-variable coupling develop an efficient algorithm based on the successive convex approximation (SCA) algorithm. Numerical results show that our proposed algorithm can ensure the accuracy of sensing targets and improve the communication secrecy rate.
Delay Minimization for Movable Antennas-Enabled Anti-Jamming Communications With Mobile Edge Computing
Authors: Yue Xiu, Yang Zhao, Songjie Yang, Minrui Xu, Dusit Niyato, Yueyang Li, Ning Wei
Date: 2024-09-22 00:00:00
Tasks: Edge-computing, Position

In future 6G networks, anti-jamming will become a critical challenge, particularly with the development of intelligent jammers that can initiate malicious interference, posing a significant security threat to communication transmission. Additionally, 6G networks have introduced mobile edge computing (MEC) technology to reduce system delay for edge user equipment (UEs). Thus, one of the key challenges in wireless communications is minimizing the system delay while mitigating interference and improving the communication rate. However, the current fixed-position antenna (FPA) techniques have limited degrees of freedom (DoF) and high power consumption, making them inadequate for communication in highly interfering environments. To address these challenges, this paper proposes a novel MEC anti-jamming communication architecture supported by mobile antenna (MA) technology. The core of the MA technique lies in optimizing the position of the antennas to increase DoF. The increase in DoF enhances the system's anti-jamming capabilities and reduces system delay. In this study, our goal is to reduce system delay while ensuring communication security and computational requirements. We design the position of MAs for UEs and the base station (BS), optimize the transmit beamforming at the UEs and the receive beamforming at the BS, and adjust the offloading rates and resource allocation for computation tasks at the MEC server. Since the optimization problem is a non-convex multi-variable coupled problem, we propose an algorithm based on penalty dual decomposition (PDD) combined with successive convex approximation (SCA). The simulation results demonstrate that the proposed MA architecture and the corresponding schemes offer superior anti-jamming capabilities and reduce the system delay compared to FPA.
Preamble Design for Joint Frame Synchronization, Frequency Offset Estimation, and Channel Estimation in Upstream Burst-mode Detection of Coherent PONs
Authors: Yongxin Sun, Hexun Jiang, Yicheng Xu, Mengfan Fu, Yixiao Zhu, Lilin Yi, Weisheng Hu, Qunbi Zhuge
Date: 2024-09-22 00:00:00

Coherent optics has demonstrated significant potential as a viable solution for achieving 100 Gb/s and higher speeds in single-wavelength passive optical networks (PON). However, upstream burst-mode coherent detection is a major challenge when adopting coherent optics in access networks. To accelerate digital signal processing (DSP) convergence with a minimal preamble length, we propose a novel burst-mode preamble design based on a constant amplitude zero auto-correlation sequence. This design facilitates comprehensive estimation of linear channel effects in the frequency domain, including polarization state rotation, differential group delay, chromatic dispersion, and polarization-dependent loss, providing overall system response information for channel equalization pre-convergence. Additionally, this preamble utilizes the same training unit to jointly achieve three key DSP functions: frame synchronization, frequency offset estimation, and channel estimation. This integration contributes to a significant reduction in the preamble length. The feasibility of the proposed preamble with a length of 272 symbols and corresponding DSP was experimentally verified in a 15 Gbaud coherent system using dual-polarization 16 quadrature amplitude modulation. The experimental results based on this scheme showed a superior performance of the convergence acceleration.