pwc-archive/papers-with-abstracts huggingface dataset:
Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication
Authors: Uyoata E. Uyoata, Mobayode O. Akinsolu, Enoruwa Obayiuwana, Abimbola Sangodoyin, Ramoni Adeogun
Date: 2024-08-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

The use of Intelligent Reflecting Surfaces (IRSs) is considered a potential enabling technology for enhancing the spectral and energy efficiency of beyond 5G communication systems. In this paper, a joint relay and intelligent reflecting surface (IRS)-assisted communication is considered to investigate the gains of optimizing both the phase angles and selection of relays. The combination of successive refinement and reinforcement learning is proposed. Successive refinement algorithm is used for phase optimization and reinforcement learning is used for relay selection. Experimental results indicate that the proposed approach offers improved achievable rate performance and scales better with number of relays compared to considered benchmark approaches.
Desynchronization Index: a New Approach for Exploring Complex Epileptogenic Networks in Stereoelectroencephalography
Authors: Federico Mason, Lorenzo Ferri, Lidia Di Vito, Lara Alvisi, Luca Zanuttini, Matteo Martinoni, Roberto Mai, Francesco Cardinale, Paolo Tinuper, Roberto Michelucci, Elena Pasini, Francesca Bisulli
Date: 2024-08-29 00:00:00

In this work, we propose a new computational framework to assist neurophysiologists in Stereoelectroencephalography (SEEG) analysis, with the final aim of improving the definition of the Epileptogenic Zone (EZ) in patients with drug-resistant epilepsy. We design a new algorithm, named Desynchronization Index (DI), that classifies as most epileptogenic those SEEG channels that show independent behavior during the seconds preceding the seizure propagation. We test the proposed DI algorithm against the Epileptogenic Index (EI) algorithm on a clinical dataset of 11 patients, considering the neurophysiological evaluation of the EZ as the clinical ground truth. Our results denote that DI overcomes EI in terms of area under the ROC curve (AUC=0.81 vs AUC=0.74) while combining the two algorithms as a unique tool leads to the best performance (AUC=0.87). The DI algorithm underscores connectivity dynamics that can hardly be identified with a pure visual analysis, increasing the accuracy of the EZ definition compared to traditional methods. This technique can lead to the definition of a new effective biomarker of the EZ, reducing the burden required by the SEEG review in the case of extensive implants and improving our understanding of the dynamics leading to the generation of seizures.
Meta-Learning Empowered Graph Neural Networks for Radio Resource Management
Authors: Kai Huang, Le Liang, Xinping Yi, Hao Ye, Shi Jin, Geoffrey Ye Li
Date: 2024-08-29 00:00:00
Tasks: Fairness, Management, Meta-Learning

In this paper, we consider a radio resource management (RRM) problem in the dynamic wireless networks, comprising multiple communication links that share the same spectrum resource. To achieve high network throughput while ensuring fairness across all links, we formulate a resilient power optimization problem with per-user minimum-rate constraints. We obtain the corresponding Lagrangian dual problem and parameterize all variables with neural networks, which can be trained in an unsupervised manner due to the provably acceptable duality gap. We develop a meta-learning approach with graph neural networks (GNNs) as parameterization that exhibits fast adaptation and scalability to varying network configurations. We formulate the objective of meta-learning by amalgamating the Lagrangian functions of different network configurations and utilize a first-order meta-learning algorithm, called Reptile, to obtain the meta-parameters. Numerical results verify that our method can efficiently improve the overall throughput and ensure the minimum rate performance. We further demonstrate that using the meta-parameters as initialization, our method can achieve fast adaptation to new wireless network configurations and reduce the number of required training data samples.
On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform
Authors: Maxwell McManus, Yuqing Cui, Zhaoxi Zhang, Elizabeth Serena Bentley, Michael Medley, Nicholas Mastronarde, Zhangyu Guan
Date: 2024-08-26 00:00:00
Tasks: Model Selection

Digital Twin (DT) technology is expected to play a pivotal role in NextG wireless systems. However, a key challenge remains in the evaluation of data-driven algorithms within DTs, particularly the transfer of learning from simulations to real-world environments. In this work, we investigate the sim-to-real gap in developing a digital twin for the NSF PAWR Platform, POWDER. We first develop a 3D model of the University of Utah campus, incorporating geographical measurements and all rooftop POWDER nodes. We then assess the accuracy of various path loss models used in training modeling and control policies, examining the impact of each model on sim-to-real link performance predictions. Finally, we discuss the lessons learned from model selection and simulation design, offering guidance for the implementation of DT-enabled wireless networks.
Symbiotic Sensing and Communication: Framework and Beamforming Design
Authors: Fanghao Xia, Zesong Fei, Xinyi Wang, Weijie Yuan, Qingqing Wu, Yuanwei Liu, Tony Q. S. Quek
Date: 2024-08-28 00:00:00

In this paper, we propose a novel symbiotic sensing and communication (SSAC) framework, comprising a base station (BS) and a passive sensing node. In particular, the BS transmits communication waveform to serve vehicle users (VUEs), while the sensing node is employed to execute sensing tasks based on the echoes in a bistatic manner, thereby avoiding the issue of self-interference. Besides the weak target of interest, the sensing node tracks VUEs and shares sensing results with BS to facilitate sensing-assisted beamforming. By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system. We first derive the Cramer-Rao lower bound (CRLB) of the two-dimensional angles of arrival estimation as the sensing metric. Next, we formulate an achievable sum rate maximization problem under the CRLB constraint, where the channel state information is reconstructed based on the sensing results. Then, we propose two penalty dual decomposition (PDD)-based alternating algorithms for fully digital and HAD arrays, respectively. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate with effective localization capability for both VUEs and the weak target. In particular, the HAD beamforming design exhibits remarkable performance gain compared to conventional schemes, especially with fewer radio frequency chains.
Software-defined Programmable Metamaterial Lens System for Dynamic Wireless Power Transfer Applications
Authors: Eistiak Ahamed, Rasool Keshavarz, Daniel Franklin, Negin Shariati
Date: 2024-08-28 00:00:00

A software-defined 2-bit Programmable Transmit Metamaterial (PTM) array surface with beam steering capabilities is proposed for indoor dynamic Wireless Power Transfer (DWPT) applications. The novel metamaterial unit cell structure is designed based on transmission phase, amplitude, and electric field response to model DWPT using PTM. Theoretical analysis of the electric field (e-field), coupling effect, and current distribution at the metamaterial interface enhances the intelligence and reconfigurability of the PTM lens. The modeled 2-bit 6x6 array PTM is designed to operate at a frequency of 4 GHz. The reconfigurable architecture comprises a fixed system with a single-layer PTM lens capable of 60{\deg} beam scanning. The PTM lens, along with a distribution board, is fabricated and experimentally tested. The results between simulation and measurements are in good agreement. The system enables dynamic optimisation of the beam pattern to track the positions of mobile users with minimal software-hardware complexity. This novel work presents a low-cost experiment achieving an average 90.7% beamforming accuracy throughout the analytical and measurement processes of DWPT for movable users, utilizing a programmable transmit metamaterial array.
Joint Offloading and Beamforming Design in Integrating Sensing, Communication, and Computing Systems: A Distributed Approach
Authors: Peng Liu, Zesong Fei, Xinyi Wang, Jingxuan Huang, Jie Hu, J. Andrew Zhang
Date: 2024-08-28 00:00:00
Tasks: Edge-computing, ISAC

When applying integrated sensing and communications (ISAC) in future mobile networks, many sensing tasks have low latency requirements, preferably being implemented at terminals. However, terminals often have limited computing capabilities and energy supply. In this paper, we investigate the effectiveness of leveraging the advanced computing capabilities of mobile edge computing (MEC) servers and the cloud server to address the sensing tasks of ISAC terminals. Specifically, we propose a novel three-tier integrated sensing, communication, and computing (ISCC) framework composed of one cloud server, multiple MEC servers, and multiple terminals, where the terminals can optionally offload sensing data to the MEC server or the cloud server. The offload message is sent via the ISAC waveform, whose echo is used for sensing. We jointly optimize the computation offloading and beamforming strategies to minimize the average execution latency while satisfying sensing requirements. In particular, we propose a low-complexity distributed algorithm to solve the problem. Firstly, we use the alternating direction method of multipliers (ADMM) and derive the closed-form solution for offloading decision variables. Subsequently, we convert the beamforming optimization sub-problem into a weighted minimum mean-square error (WMMSE) problem and propose a fractional programming based algorithm. Numerical results demonstrate that the proposed ISCC framework and distributed algorithm significantly reduce the execution latency and the energy consumption of sensing tasks at a lower computational complexity compared to existing schemes.
Globally Optimal Movable Antenna-Enhanced multi-user Communication: Discrete Antenna Positioning, Motion Power Consumption, and Imperfect CSI
Authors: Yifei Wu, Dongfang Xu, Derrick Wing Kwan Ng, Wolfgang Gerstacker, Robert Schober
Date: 2024-08-27 00:00:00

Movable antennas (MAs) represent a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by dynamically adapting the positions of antenna elements within a designated transmit area. In particular, by employing electro-mechanical MA drivers, the positions of the MA elements can be adjusted to shape a favorable spatial correlation for improving system performance. Although preliminary research has explored beamforming designs for MA systems, the intricacies of the power consumption and the precise positioning of MA elements are not well understood. Moreover, the assumption of perfect CSI adopted in the literature is impractical due to the significant pilot overhead and the extensive time to acquire perfect CSI. To address these challenges, we model the motion of MA elements through discrete steps and quantify the associated power consumption as a function of these movements. Furthermore, by leveraging the properties of the MA channel model, we introduce a novel CSI error model tailored for MA systems that facilitates robust resource allocation design. In particular, we optimize the beamforming and the MA positions at the BS to minimize the total BS power consumption, encompassing both radiated and MA motion power while guaranteeing a minimum required SINR for each user. To this end, novel algorithms exploiting the branch and bound (BnB) method are developed to obtain the optimal solution for perfect and imperfect CSI. Moreover, to support practical implementation, we propose low-complexity algorithms with guaranteed convergence by leveraging successive convex approximation (SCA). Our numerical results validate the optimality of the proposed BnB-based algorithms. Furthermore, we unveil that both proposed SCA-based algorithms approach the optimal performance within a few iterations, thus highlighting their practical advantages.
Seamless 5G Automotive Connectivity with Integrated Satellite Terrestrial Networks in C-Band
Authors: Hung Nguyen-Kha, Vu Nguyen Ha, Eva Lagunas, Symeon Chatzinotas, Joel Grotz
Date: 2024-08-27 00:00:00

This paper examines integrated satellite-terrestrial networks (ISTNs) in urban environments, where terrestrial networks (TNs) and non-terrestrial networks (NTNs) share the same frequency band in the C-band which is considered the promising band for both systems. The dynamic issues in ISTNs, arising from the movement of low Earth orbit satellites (LEOSats) and the mobility of users (UEs), are addressed. The goal is to maximize the sum rate by optimizing link selection for UEs over time. To tackle this challenge, an efficient iterative algorithm is developed. Simulations using a realistic 3D map provide valuable insights into the impact of urban environments on ISTNs and also demonstrates the effectiveness of the proposed algorithm.
Extended Road-Aware Line-of-Sight Probability Model for Urban Air Mobility
Authors: Abdullah Abu Zaid, Baha Eddine Youcef Belmekki, Mohamed-Slim Alouini
Date: 2024-08-27 00:00:00

As urban air mobility (UAM) emerges as a transformative solution to urban transportation, the demand for robust communication frameworks capable of supporting high-density aerial traffic becomes increasingly critical. An essential area of communications improvement is reliably characterizing and minimizing interference on UAM aircraft from other aircraft and ground vehicles. To achieve this, accurate line-of-sight (LOS) models must be used. In this work, we highlight the limitations of a LOS probability model extensively used in the literature in accurately predicting interference caused by ground vehicles. Then, we introduce a modified probability of LOS model that improves interference prediction by incorporating the urban topography and the dynamic positioning of ground vehicles on streets. Our model's parameters are derived from extensive simulations and validated through real-world urban settings to ensure reliability and applicability.
A High Altitude Platform-Based 3D Geometrical Channel Model for Beamforming Characterization in Future 6G Flying Ad-Hoc Networks
Authors: Muhammet Kirik, Liza Afeef, Huseyin Arslan
Date: 2024-08-27 00:00:00
Tasks: 3D geometry

Growing requirements of future wireless communication systems, such as high data rates, high reliability, and low latency, make the active usage of Non-Terrestrial Networks (NTN) an inevitable necessity. In this regard, High Altitude Platforms (HAPs) have drawn great attention in recent years due to their unique characteristics such as high coverage, long operational durability, and ad-hoc movement. However, for the active usage of HAPs, channel models for their various usage scenarios must be well-defined, especially in those cases where the sophisticated multiple-input multiple-output (MIMO) techniques, such as beamforming, are utilized to increase the data rate. Therefore, in this study, an air-to-air (A2A) three dimensional (3D) geometrical channel model is proposed to characterize the beamforming capabilities of non-stationary HAP networks operating at millimeter wave (mmWave) frequency band. In this regard, the 3D geometry of the two HAPs in the air is analyzed, and the effect of Doppler due to the movement of HAPs is interrogated as well as its effect on the signal-to-noise ratio (SNR). The final outputs of this study show that the proposed A2A channel model is applicable to characterize the future sixth generation (6G) HAP networks when the mmWave is used to utilize beamforming with a large number of antennas.
Ultra-Low-Complexity, Non-Linear Processing for MU-MIMO Systems
Authors: Chathura Jayawardena, Konstantinos Nikitopoulos
Date: 2024-08-27 00:00:00

Non-linear detection schemes can substantially improve the achievable throughput and connectivity capabilities of uplink MU-MIMO systems that employ linear detection. However, the complexity requirements of existing non-linear soft detectors that provide substantial gains compared to linear ones are at least an order of magnitude more complex, making their adoption challenging. In particular, joint soft information computation involves solving multiple vector minimization problems, each with a complexity that scales exponentially with the number of users. This work introduces a novel ultra-low-complexity, non-linear detection scheme that performs joint Detection and Approximate Reliability Estimation (DARE). For the first time, DARE can substantially improve the achievable throughput (e.g., 40%) with less than 2x the complexity of linear MMSE, making non-linear processing extremely practical. To enable this, DARE includes a novel procedure to approximate the reliability of the received bits based on the region of the received observable that can efficiently approach the accurately calculated soft detection performance. In addition, we show that DARE can achieve a better throughput than linear detection when using just half the base station antennas, resulting in substantial power savings (e.g., 500 W). Consequently, DARE is a very strong candidate for future power-efficient MU-MIMO developments, even in the case of software-based implementations, as in the case of emerging Open-RAN systems. Furthermore, DARE can achieve the throughput of the state-of-the-art non-linear detectors with complexity requirements that are orders of magnitude lower.
Optimal Joint Radar and Communication User Association in Cell-Free mMIMO Systems
Authors: Ahmed Naeem, El Mehdi Amhoud, Hüseyin Arslan
Date: 2024-08-27 00:00:00

The cell-free massive multiple-input multiple-output (CF-mMIMO) systems are crucial for 6G development due to their high spectral efficiency and uniform user-experienced data rates. A key aspect of CF-mMIMO is user association (UA) and optimal cluster formation. Traditional methods focusing solely on communication-related metrics fall short in this context, as sensing is becoming integral to 6G. This study delves into a framework for joint radar and communication (JRC) in CF-mMIMO systems and investigates JRC-based UA techniques. We propose a novel method to optimize UA, enhancing both communication spectral efficiency and sensing accuracy. Existing literature has not explored this dual requirement integration for UA. Our proposed two-step scheme optimizes UA clusters for both communication and sensing. The first step involves selecting access points (APs) based on channel quality, followed by a second step that further refines the selection by choosing APs from the initial group that are also optimal for sensing. We utilize the signal-clutter plus noise ratio to exclude APs with clutter in front of the user equipment (UE) and the AP view angle, ensuring that radar echoes are received only from the specific UE, not the surrounding clutter. Theoretical analysis and simulations demonstrate that the same APs optimized for communication are not necessarily optimal for sensing, highlighting the need for schemes that incorporate sensing requirements in UA. The results show the effectiveness of the proposed method, showing its potential to improve CF-mMIMO system performance in JRC scenarios.
A Superdirective Beamforming Approach based on MultiTransUNet-GAN
Authors: Yali Zhang, Haifan Yin, Liangcheng Han
Date: 2024-08-24 00:00:00

In traditional multiple-input multiple-output (MIMO) communication systems, the antenna spacing is often no smaller than half a wavelength. However, by exploiting the coupling between more closely-spaced antennas, a superdirective array may achieve a much higher beamforming gain than traditional MIMO. In this paper, we present a novel utilization of neural networks in the context of superdirective arrays. Specifically, a new model called MultiTransUNet-GAN is proposed, which aims to forecast the excitation coefficients to achieve ``superdirectivity" or ``super-gain" in the compact uniform linear or planar antenna arrays. In this model, we integrate a multi-level guided attention and a multi-scale skip connection. Furthermore, generative adversarial networks are integrated into our model. To improve the prediction accuracy and convergence speed of our model, we introduce the warm up aided cosine learning rate (LR) schedule during the model training, and the objective function is improved by incorporating the normalized mean squared error (NMSE) between the generated value and the actual value. Simulations demonstrate that the array directivity and array gain achieved by our model exhibit a strong agreement with the theoretical values. Overall, it shows the advantage of enhanced precision over the existing models, and a reduced requirement for measurement and the computation of the excitation coefficients.
Vibration Sensor Dataset for Estimating Fan Coil Motor Health
Authors: Heitor Lifsitch, Gabriel Rocha, Hendrio Bragança, Cláudio Filho, Leandro Okimoto, Allan Amorin, Fábio Cardoso
Date: 2024-08-26 00:00:00
Tasks: Scheduling

To enhance the field of continuous motor health monitoring, we present FAN-COIL-I, an extensive vibration sensor dataset derived from a Fan Coil motor. This dataset is uniquely positioned to facilitate the detection and prediction of motor health issues, enabling a more efficient maintenance scheduling process that can potentially obviate the need for regular checks. Unlike existing datasets, often created under controlled conditions or through simulations, FAN-COIL-I is compiled from real-world operational data, providing an invaluable resource for authentic motor diagnosis and predictive maintenance research. Gathered using a high-resolution 32KHz sampling rate, the dataset encompasses comprehensive vibration readings from both the forward and rear sides of the Fan Coil motor over a continuous two-week period, offering a rare glimpse into the dynamic operational patterns of these systems in a corporate setting. FAN-COIL-I stands out not only for its real-world applicability but also for its potential to serve as a reliable benchmark for researchers and practitioners seeking to validate their models against genuine engine conditions.
Multi-Beam Object-Localization for Millimeter-Wave ISAC-Aided Connected Autonomous Vehicles
Authors: Jitendra Singh, Awadhesh Gupta, Aditya K. Jagannatham, Lajos Hanzo
Date: 2024-08-26 00:00:00
Tasks: Autonomous Vehicles, Integrated sensing and communication, ISAC, Object Localization

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems capable of integrated sensing and communication (ISAC) constitute a key technology for connected autonomous vehicles (CAVs). In this context, we propose a multi-beam object-localization (MBOL) model for enhancing the sensing beampattern (SBP) gain of adjacent objects in CAV scenarios. Given the ultra-narrow beams of mmWave MIMO systems, a single pencil beam is unsuitable for closely located objects, which tend to require multiple beams. Hence, we formulate the SBP gain maximization problem, considering also the constraints on the signal-to-interference and noise ratio (SINR) of the communication users (CUs), on the transmit power, and the constant modulus of the phase-shifters in the mmWave hybrid transceiver. To solve this non-convex problem, we propose a penalty-based triple alternating optimization algorithm to design the hybrid beamformer. Finally, simulation results are provided for demonstrating the efficacy of the proposed model.