arXiv subjects

Muhammad Nabeel

Publications and source records attributed to Muhammad Nabeel.

6 recordsLinked to original sources

arXiv2026

Achievable Accuracy and Cramer Rao Bounds for SSB Based LEO Positioning in NR NTN

Advanced Low Earth Orbit (LEO) satellite networks, such as Starlinks Mobile Satellite Service (MSS), will adopt the 5G New Radio (NR) Non-Terrestrial Network (NTN) standard. This enables the use of ubiquitous Synchronization Signal Blocks (SSBs) for opportunistic receiver positioning based on pseudorange and Doppler measurements. In this work, we characterize the estimation theoretic limits of SSB-based positioning by deriving single SSB Cramer Rao lower bounds (CRLBs) for delay and carrier frequency observables associated with pseudorange and Doppler. These bounds are obtained using the full SSB time-frequency energy distribution and extended into a multi epoch, multi satellite Fisher information framework that jointly bounds stationary user position, clock bias, and clock drift. Each SSB contribution is weighted according to a range dependent CRLB determined by the received SNR, so the estimator and the bound share a common noise model. In simulation, the resulting bound closely matches achievable performance, and a physically weighted least squares estimator approaches the CRLB at realistic operating SNR. Using a Starlink based constellation, we analyze the operating SNR experienced by a ground user and demonstrate sub-meter positioning accuracy.

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arXiv2026

Opportunistic Positioning with LEO Satellites based on SSB from NR NTN

Forthcoming Low Earth Orbit (LEO) satellite networks such as Starlink's Mobile Satellite Service (MSS) will incorporate the New Radio (NR) Non-Terrestrial Network (NTN) standard. The Synchronization Signal Block (SSB) specified as part of NR is periodically broadcast for cell search and initial access. We propose to exploit the SSB for opportunistic receiver positioning. Doppler shift measurements are modeled and pseudoranges are derived from SSB while also taking into account the receiver's clock bias and drift. The resulting per satellite integer ambiguity in the pseudorange is resolved by geometry alone, without inter-satellite differencing or an a-priori position. Measurements are taken from SSBs of multiple satellites and at multiple occasions per satellite, whereby the SSBs are subject to different transmission timings and varying propagation delays. Finally, a simulation model is developed for positioning based on the actual Starlink constellation and the NR NTN standard to evaluate the positioning accuracy to be expected. The proposed approach achieves a mean positioning error of less than 10m without requiring any modification of the NR NTN standard.

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arXiv2023

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.

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arXiv2021

Link-Level Performance Evaluation of IMT-2020 Candidate Technology: DECT-2020 New Radio

The ETSI has recently introduced the DECT-2020 New Radio (NR) as an IMT-2020 candidate technology for the mMTC and URLLC use cases. To consider DECT-2020 NR as an IMT-2020 technology, the ITU-R has determined different independent evaluation groups to assess its performance against the IMT-2020 requirements. These independent evaluation groups are now in process of investigating the DECT-2020 NR. In order to successfully assess a technology, one important aspect is to fully understand the underlying physical layer and its performance in different environments. Therefore, in this paper, we focus on the physical layer of DECT-2020 NR and investigate its link-level performance with standard channel models provided by the ITU-R for evaluation. We perform extensive simulations to analyze the performance of DECT-2020 NR for both URLLC and mMTC use cases. The results presented in this work are beneficial for the independent evaluation groups and researchers as these results can help calibrating their physical layer performance curves. These results can also be used directly for future system-level evaluations of DECT-2020 NR.

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arXiv2020

AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App

Background: The inability to test at scale has become humanity's Achille's heel in the ongoing war against the COVID-19 pandemic. A scalable screening tool would be a game changer. Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app. The app, named AI4COVID-19 records and sends three 3-second cough sounds to an AI engine running in the cloud, and returns a result within two minutes. Methods: Cough is a symptom of over thirty non-COVID-19 related medical conditions. This makes the diagnosis of a COVID-19 infection by cough alone an extremely challenging multidisciplinary problem. We address this problem by investigating the distinctness of pathomorphological alterations in the respiratory system induced by COVID-19 infection when compared to other respiratory infections. To overcome the COVID-19 cough training data shortage we exploit transfer learning. To reduce the misdiagnosis risk stemming from the complex dimensionality of the problem, we leverage a multi-pronged mediator centered risk-averse AI architecture. Results: Results show AI4COVID-19 can distinguish among COVID-19 coughs and several types of non-COVID-19 coughs. The accuracy is promising enough to encourage a large-scale collection of labeled cough data to gauge the generalization capability of AI4COVID-19. AI4COVID-19 is not a clinical grade testing tool. Instead, it offers a screening tool deployable anytime, anywhere, by anyone. It can also be a clinical decision assistance tool used to channel clinical-testing and treatment to those who need it the most, thereby saving more lives.

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arXiv2020

Can Machine Learning Be Used to Recognize and Diagnose Coughs?

Emerging wireless technologies, such as 5G and beyond, are bringing new use cases to the forefront, one of the most prominent being machine learning empowered health care. One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections. Since cough is an essential symptom of many respiratory infections, an automated system to screen for respiratory diseases based on raw cough data would have a multitude of beneficial research and medical applications. In literature, machine learning has already been successfully used to detect cough events in controlled environments. In this paper, we present a low complexity, automated recognition and diagnostic tool for screening respiratory infections that utilizes Convolutional Neural Networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e., bronchitis, bronchiolitis and pertussis) based on their unique cough audio features. Both proposed detection and diagnosis models achieve an accuracy of over 89%, while also remaining computationally efficient. Results show that the proposed system is successfully able to detect and separate cough events from background noise. Moreover, the proposed single diagnosis model is capable of distinguishing between different illnesses without the need of separate models.

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