arXiv subjects

Ali Imran

Publications and source records attributed to Ali Imran.

32 records · Page 2Linked to original sources

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.

eess.AS↗

arXiv2020

Data Driven Optimization of Inter-Frequency Mobility Parameters for Emerging Multi-band Networks

Densification and multi-band operation in 5G and beyond pose an unprecedented challenge for mobility management, particularly for inter-frequency handovers. The challenge is aggravated by the fact that the impact of key inter-frequency mobility parameters, namely A5 time to trigger (TTT), A5 threshold1 and A5 threshold2 on the system's performance is not fully understood. These parameters are fixed to a gold standard value or adjusted through hit and trial. This paper presents a first study to analyze and optimize A5 parameters for jointly maximizing two key performance indicators (KPIs): Reference signal received power (RSRP) and handover success rate (HOSR). As analytical modeling cannot capture the system-level complexity, a data driven approach is used. By developing XGBoost based model, that outperforms other models in terms of accuracy, we first analyze the concurrent impact of the three parameters on the two KPIs. The results reveal three key insights: 1) there exist optimal parameter values for each KPI; 2) these optimal values do not necessarily belong to the current gold standard; 3) the optimal parameter values for the two KPIs do not overlap. We then leverage the Sobol variance-based sensitivity analysis to draw some insights which can be used to avoid the parametric conflict while jointly maximizing both KPIs. We formulate the joint RSRP and HOSR optimization problem, show that it is non-convex and solve it using the genetic algorithm (GA). Comparison with the brute force-based results show that the proposed data driven GA-aided solution is 48x faster with negligible loss in optimality.

cs.NI↗

arXiv2020

Towards Designing Systems with Large Number of Antennas for Range Extension in Ground-to-Air Communications

Providing broadband connectivity to airborne systems using ground based cellular networks is a promising solution as it offers several advantages over satellite-based solutions. However, limited range of terrestrial base stations is a key challenge in full realization of this approach. This paper addresses this problem by proposing a mathematical framework for range extension leveraging large number of antennas at the base station. In contrast to prior works where range is not considered as a design parameter, we model the signal to noise ratio as a function of both number of antennas as well as the range in line-of-sight ground-to-air systems. This allows us to derive analytical expressions to determine the number of antennas required to increase range in different frequency bands and tracking and non tracking scenarios.

eess.SP↗

arXiv2020

On the Trade-offs between Coverage Radius, Altitude and Beamwidth for Practical UAV Deployments

Current studies on Unmanned Aerial Vehicle (UAV) based cellular deployment consider UAVs as aerial base stations for air-to-ground communication. However, they analyze UAV coverage radius and altitude interplay while omitting or over-simplifying an important aspect of UAV deployment, i.e., effect of a realistic antenna pattern. This paper addresses the UAV deployment problem while using a realistic 3D directional antenna model. New trade-offs between UAV design space dimensions are revealed and analyzed in different scenarios. The sensitivity of coverage area to both antenna beamwidth and height is compared. The analysis is extended to multiple UAVs and a new packing scheme is proposed for multiple UAVs coverage that offers several advantages compared to prior approaches.

eess.SP↗

arXiv2020

Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks

Mobile cellular network operators spend nearly a quarter of their revenue on network maintenance and management. A significant portion of that budget is spent on resolving faults diagnosed in the system that disrupt or degrade cellular services. Historically, the operations to detect, diagnose and resolve issues were carried out by human experts. However, with diversifying cell types, increased complexity and growing cell density, this methodology is becoming less viable, both technically and financially. To cope with this problem, in recent years, research on self-healing solutions has gained significant momentum. One of the most desirable features of the self-healing paradigm is automated fault diagnosis. While several fault detection and diagnosis machine learning models have been proposed recently, these schemes have one common tenancy of relying on human expert contribution for fault diagnosis and prediction in one way or another. In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input. The proposed solution leverages Random Forests classifier, Convolutional Neural Network and neuromorphic based deep learning model which uses RSRP map images of faults generated. We compare the performance of the proposed solution against state-of-the-art solution in literature that mostly use Naive Bayes models, while considering seven different fault types. Results show that neuromorphic computing model achieves high classification accuracy as compared to the other models even with relatively small training data

cs.NI↗

arXiv2020

A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE). With 5G in view, the number of these COPs are expected to reach 2000 per site, making their manual tuning for finding the optimal combination of these parameters, an impossible fleet. Alongside these thousands of COPs is the anticipated network densification in emerging networks which exacerbates the burden of the network operators in managing and optimizing the network. Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users. The first part of the framework leverages the power of machine learning to predict the KPI of interest given several different combinations of CIO and HOM. The resulting predictions are then fed into Genetic Algorithm (GA) which searches for the best combination of the two mentioned parameters that yield the maximum mean SINR for all users. Performance of the framework is also evaluated using several machine learning techniques, with CatBoost algorithm yielding the best prediction performance. Meanwhile, GA is able to reveal the optimal parameter setting combination more efficiently and with three orders of magnitude faster convergence time in comparison to brute force approach.

cs.NI↗

arXiv2020

Statistical Modelling of Dynamic Interference Threshold and Its Effect on Network Capacity

In this paper, we present the case of utilizing interference temperature (IT) as a dynamic quantity rather than as a fixed quantity in an orthogonal frequency division multiple access (OFDMA) based spectrum sharing systems. The fundamental idea here is to reflect the changing capacity demand of primary user (PU) over time in setting the interference power threshold for secondary user (SU). This type of dynamic IT will allow the SU to opportunistically have higher transmit power during relaxed IT period, thereby resulting in higher network capacity. The cognitive radio network (CRN) considered in this paper has an underlay network configuration in which the available spectrum of the PU is accessed concurrently by SU provided that the interference power at the PU receiver from SU is under a certain power threshold. This power threshold is set to maintain and guarantee a certain level of quality of service (QoS) for PU network. Theoretical expressions for outage probability and mean capacity for SU network are derived, and validated with simulation results, and it is observed that utilizing dynamic IT results in high network performance gains as compared to utilizing a fixed IT in cognitive radio system.

eess.SP↗

arXiv2019

Modelling and Simulation of of high efficiency GaAs PIN-Solar Cell

Solar energy is the most convenient and reliable energy source among all renewable energy resources and an efficient photovoltaic device is required to convert this energy into utilizable energy. Different types of solar cells (SC) are commercially available. However, various parameters need to be optimized to get maximum efficiency from a SC. In this study we have presented a SC model in which dependence of quantum efficiency (QE) on various parameters has been investigated. The mobility of the carriers has been varied with wide range along with the carrier life time (LT). Results show that maximum efficiencies can be achieved up to 11.10% and 10.81% keeping the electron and hole mobility to be 1500 cm2V-1s-1 and 300 cm2V-1s-1 respectively with electron and hole carrier LT to be 3ns and 7ns respectively. The effect of surface recombination velocity (SRV) has also been brought under observation and the maximum efficiency is found to be 13.75% at electron and hole SRV equal to be 103ms-1. Results shows that the higher photovoltaic efficiencies can be achieved by increasing the mobility and carrier LT while decreasing the surface recombination velocities.

physics.app-ph↗

arXiv2019

On the Impact of Mode Selection on Effective Capacity of Device-to-Device Communication

Consider a device to device (D2D) link which utilizes the mode selection to decide between the direct mode and cellular mode. This paper investigates the impact of mode selection on effective capacity (EC) (the maximum sustainable constant arrival rate at a transmitters queue under statistical quality of service constraints) of a D2D link for both overlay and underlay scenarios. Due to lack of channel state information, the transmit device sends data at a fixed rate and fixed power; this fact combined with mode selection makes the D2D channel a Markov service process. Thus, the EC is obtained by calculating the entries of the transition probability matrix corresponding to the Markov D2D channel. Numerical results show that the EC decays exponentially (and the gain of overlay D2D over underlay D2D diminishes) with the increase in estimation error of the pathloss measurements utilized by the mode selection.

eess.SP↗

arXiv2018

User Transmit Power Minimization through Uplink Resource Allocation and User Association in HetNets

The popularity of cellular internet of things (IoT) is increasing day by day and billions of IoT devices will be connected to the internet. Many of these devices have limited battery life with constraints on transmit power. High user power consumption in cellular networks restricts the deployment of many IoT devices in 5G. To enable the inclusion of these devices, 5G should be supplemented with strategies and schemes to reduce user power consumption. Therefore, we present a novel joint uplink user association and resource allocation scheme for minimizing user transmit power while meeting the quality of service. We analyze our scheme for two-tier heterogeneous network (HetNet) and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms compared to 20 dBm in state-of-the-art Max reference signal received power (RSRP) and channel individual offset (CIO) based association schemes.

cs.NI↗

arXiv2018

Proactive Load Balancing in Heterogeneous Cellular Networks

Recent exponential growth of data over cellular networks has cause the progression from conventional mobile communication networks to heterogeneous cellular networks (HetNets). Quality of experience (QoE)-aware traffic load balancing in such dense HetNets is considered a major problem. Current HetNets exploit reactive load balancing schemes that hinder in achieving desired QoE gain. In this paper, we propose a novel proactive load balancing framework which leverages content caching and mobility prediction to improve user QoE. We use Semi-Markov model to predict users' future cells and a novel proactive caching algorithm is proposed to pre-fetch user future demands at these cells. This allows us to reduce cell congestion and offers better average downlink rates. To validate the effectiveness of the proposed framework, system level simulations are performed and compared with state of the art approaches. Encouragingly, our system achieves up-to $98.7\%$ cell load balancing in terms of Jain's fairness index, reduces $20\%$ backhaul load, and provides $31\%$ improved downlink rates at congested cells in contrast to reactive approaches.

cs.NI↗

arXiv2018

Outage Probability of Millimeter Wave Cellular Uplink with Truncated Power Control

In this paper, using the stochastic geometry, we develop a tractable uplink modeling framework for the outage probability of both the single and multi-tier millimeter wave (mmWave) cellular networks. Each tier's mmWave base stations (BSs) are randomly located and they have particular spatial density, antenna gain, receiver sensitivity, blockage parameter and pathloss exponents. Our model takes account of the maximum power limitation and the per-user power control. More specifically, each user, which could be in line-of-sight (LOS) or non-LOS to its serving mmWave BS, controls its transmit power such that the received signal power at its serving BS is equal to a predefined threshold. Hence, a truncated channel inversion power control scheme is implemented for the uplink of mmWave cellular networks. We derive closed-form expressions for the signal-to-interference-and-noise-ratio (SINR) outage probability for the uplink of both the single and multi-tier mmWave cellular networks. Furthermore, we analyze the case with a dense network by utilizing the simplified model, where the LOS region is approximated as a fixed LOS disc. The results show that imposing a maximum power constraint on the user significantly affects the SINR outage probability in the uplink of mmWave cellular networks.

eess.SP↗

arXiv2016

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.

cs.NI↗

arXiv2016

Coverage gain and Device-to-Device user Density: Stochastic Geometry Modeling and Analysis

Device-to-device (D2D) communication has huge potential for capacity and coverage enhancements for next generation cellular networks. The number of potential nodes for D2D communication is an important parameter that directly impacts the system capacity. In this paper, we derive analytic expression for average coverage probability of cellular user and corresponding number of potential D2D users. In this context, mature framework of stochastic geometry and Poisson point process has been used. The retention probability has been incorporated in Laplace functional to capture reduced path-loss and shortest distance criterion based D2D pairing. The numerical results show a close match between analytic expression and simulation setup.

cs.IT↗