Ibrahim AbuAlhaol
PhD | PEng | SMIEEE
Principal Data Scientist @ Huawei Technologies
Adjunct Research Professor @ Carleton University
Ottawa, ON, Canada
H +1 (613) 216-9474
B ibrahimee@ieee.org
Í alhaol.com
Canadian Citizen
Summary
Principal Data Scientist
@ Huawei Technologies and
Adjunct Research Professor
@ Carleton University in Ottawa,
Canada. Holds a BSc, an MSc, and a PhD in Electrical and Computer Engineering. Holds an MEng in Technology
Innovation Management. A senior member of IEEE and Professional Engineer (PEng) in Ontario, Canada. Interests
in machine learning and big-data analytics applications in Cybersecurity and Wireless Communications. Hands-on
experience in machine learning modeling, natural language processing, and real-time analytics. Excellent hands-on
experience in (i) Python programming (Data analysis and mining with pandas, visualization with matplotlib/seaborn,
machine learning with Scikit-learn/Spark-MLlib/TensorFlow, and natural language processing with NLTK/Gensim),
(ii) Databases (SQL, MongoDB, and Cassandra), (iii) Real-time analytics (Spark streaming and Kafka), (iv) DevOps
(Docker on AWS and Azure).
Education
2014–2015 MEng in Technology Innovation Management, Carleton University, Canada, GPA: 11/12.
2005–2008 PhD in Electrical and Computer Engineering, University of Mississippi, USA, GPA: 4.0/4.0.
2002–2004 MSc in Electrical Engineering, Jordan University of Science & Technology, Jordan, GPA: 84.4/100.
1995–2000 BSc in Electrical Engineering, Jordan University of Science & Technology, Jordan, GPA: 84/100.
Employment
2019-Present Principal Data Scientist, Huawei Technologies, Ottawa, Canada.
Project Lead | Team Builder | Neural Networks (DNN,CNN,RNN) | Malware Detection and Classification
2017-Present Adjunct Research Professor, Carleton University , Ottawa, Canada.
Graduate Students Supervision| Machine Learning | Big Data Analytics | 5G Wireless Network Personalization
2017-2019 Senior Data Scientist, Larus Technologies, Ottawa, Canada.
Business Intelligence Forecasting | LSTM Recurrent Neural Networks | Natural Language Processing | Multi-
objective Optimization (MOO) | Real-time Big Data Analytics.
2015-2017 CyberSecurity Research Scientist, VENUS Cybersecurity Corporation, Ottawa, Canada.
Collective Intelligence | Machine Learning | Big Data Analytics | BGP Hijacking | DDOS Attacks.
2014-2015 CyberSecurity Researcher, Carleton University and Xahive.com, Ottawa, Canada.
Device-to-Device Wireless Security | Internet-based Communications Privacy | RSA Key Exchange | AES Encryption.
2009-2014 Assistant Professor, Khalifa University of Science & Technology, United Arab Emirates.
Supervising Graduate Students | Probability and Statistics | Wireless Communications | Modulation and Coding.
2008-2009 Wireless System Engineer, Broadcom Corporation, San Diego, USA.
3G/4G System Design | RF Compliance | 3GPP Standards | Bluetooth | WLAN.
2008-2008 System Engineer Intern, Qualcomm Incorporation, San Diego, USA.
4G Wireless Network 3GPP Standards | Analysis | Simulation | WiMAX | LTE.
2005-2008 PhD Student/Research Assistant, University of Mississippi, Oxford, USA.
Cooperative (UAV) Networks | MIMO/OFDM Systems | Formulation | Optimization | Modeling | Simulation.
Publications ( http://bit.ly/ibrahimscholar )
4 Book chapters | 10 Journals | 5 Patents | 39 Conferences | 367 Google Scholar citations.
1/ 9
Experience
May 2019
–Present
Principal Data Scientist, Huawei Technologies, Ottawa, Canada.
Objective: Enhance endpoint security systems and firewall threat detection.
Challenge: Leading a team to build ai-enabled cybersecurity solutions.
Tools/Software:
Big Data mining (PySpark and Kafka), Databases (MongoDB), Data Mining (Python-
Pandas and Spark SQL), Data visualization (Python-Seaborn), Natural Language Processing (Python-
NLTK and Python-Gensim), DevOps (Docker on Ubuntu).
Responsibilities: [1]
Lead (build) a team of data scientists, data engineers, and cybersecurity analysts
to develop new capabilities and generate actionable system and network insights through advanced
analytics.
[2]
Plan and prioritize project milestones and guide a team to productize and scale high impact
prototype analytical models developed to support R&D programs.
[3]
File patents of the inventions
after building the proof of concepts.
[4]
Direct collaboration with academic institutes and co-authoring
conference/ journal papers and patents.
[5]
Iteratively, review and design test cases of the developed
data products.
[6]
Direct the team to any breakthrough in cybersecurity and machine learning research
that could enhance our data products.
JAN 2017
–Present
Adjunct Research Professor, Carleton University, Ottawa, Canada.
Objective:
Modeling User satisfaction and zone of tolerance with Machine Learning and big data
analytics and proactively direct the scheduler to micro-manage resource allocations in 5G Networks.
Challenge:
Harness a massive amount of contextual and performance data and model the features that
are correlated to the user satisfaction and zone of tolerance in spatial, temporal, and social contexts.
Tools/Software: MATLAB, KNIME, Python (Scikit-learn, Pandas, PySpark, and Matplotlib).
Responsibilities: [1]
Co-supervise PhD students to apply machine learning and big data analytics on
wireless communication problems.
[2]
Direct the students on designing, developing, and evaluating
machine learning models and techniques and apply them to personalize the distribution of resources in
5G networks.
[3]
Direct the effort to synthesize and model user data and tune the models to generate
both usage and contextual data.
[4]
Co-author conference and journal papers.
[5]
Direct the work-flow
of generating processes, models, and data to provide open-source tools and data that can be utilized by
other members in the research community.
AUG 2018
–May-2019
Senior Data Scientist, Larus Technologies, Ottawa, Canada.
Objective: Mining big data to develop predictive analytics models and machine learning algorithms to
improve both the internal and collaborative processes of the clients.
Challenge:
Generate Actionable Intelligence for Decision Support System using real-time analytics. Ac-
tionable Intelligence includes anomalies, alerts, threats, potential response generation, process refinement
and other types of knowledge that improve the efficiency of clients’ operations and processes.
Tools/Software:
MOEA Framework-Java (Multi-objective Optimization), DEAP-Python (Evolutionary
Algorithms), Big Data mining (PySpark and Kafka), Databases (Cassandra, PostgreSQL, and MongoDB),
Automatic Identification System (AIS) Data Mining (Python-Pandas and Spark SQL), Data visualization
(Python-Seaborn), Natural Language Processing (Python-NLTK and Python-Gensim).
Responsibilities:
[1]
Design, optimize, develop, and deliver machine learning-enabled solutions for Decision Support
Systems (DSS).
[2]
Develop efficient and automated processes to collect, analyze and provide compiled
data to be utilized by Decision Support Systems.
[3]
Build scalable processes to collect, manipulate,
present, and analyze big datasets in a production environment.
2/ 9
[4]
Work on data acquisition, investigation, visualization, feature engineering, experimentation with
machine learning algorithms, and deploying tuned/validated/tested models.
[5]
Develop functioning
prototypes of algorithms and evaluate and compare metrics based on the real-world big datasets.
[6]
Work on pattern learning, outlier detection, and identification of appropriate analytic and statistical
methodology.
[7]
Develop real-time predictive models with Spark-streaming and Kafka in a production
environment.
[8]
Data-driven vessel service time forecasting using Long Short-Term Memory (LSTM)
Recurrent Neural Networks (RNN). [9] Machine learning-enabled retailer promotion planning process.
[10]
Textual feature extraction with word-to-vector (W2V) embedding and multi-label classification with
deep neural network (DNN) models.
JAN 2017
–JUL 2018
Data Scientist, Larus Technologies, Ottawa, Canada.
Objective:
Exploit massive amount of sensor data emitted by many maritime actors to improve both
internal and collaborative processes for maritime Internet-of-Things related organizations.
Challenge:
Generate actionable intelligence for Decision Support systems using real-time Big Data
analytics. The intelligence includes anomalies, alerts, threats, potential response generation, process
refinement and other types of knowledge that improve the efficiency of maritime-related organizations.
Tools/Software:
Big Data (PySpark), Databases (Cassandra, PostgreSQL, and MongoDB), Automatic
Identification System (AIS) Data Mining (Python-Pandas), Data visualization (Python-Seaborn).
Responsibilities: [1]
Prepare literature reviews on supply chain management and optimization and
identify maritime-related problems (pain points) to be solved (optimized) by real-time Big Data analytics.
[2]
Design, develop, and evaluate data mining processes to extract global speed statistics and outliers
for all sea vessel categories at a specific period of interest (POI) and time of interest (TOI).
[3]
Harness
publicly available maritime ports meta-data and establish efficient automated processes to collect, analyze
and provide compiled data to be utilized by other DS systems optimizers.
[4]
Mining port congestion
indicators from big AIS data.
[5]
Co-authoring publications in the area of meta-heuristics multi-objective
optimization enabled by big data analytics and machine learning.
OCT 2015
DEC 2016
CyberSecurity Research Scientist, VENUS Cybersecurity Corporation, Ottawa, Canada.
Objective:
Data mining and exploratory analysis of live and historical Border Gateway Protocol (BGP)
data to enable Cybersecurity operational monitoring, post-event analysis, and real-time anomaly detection.
Challenge:
Real-time mining with unsupervised machine learning capabilities to tune supervised machine
learning predictors. This includes feature engineering, cleaning, averaging, filtering, and time-series
aggregation.
Tools/Software:
KNIME, MATLAB, Python (Skit-learn, Pandas, and PySpark), Databases (MongoDB),
Real-time streaming platform (Apache Kafka), Javascript visualization (D3, Crossfilter, DC).
Responsibilities: [1]
Prepare a prior art review on Exploratory Data Analysis (EDA), Border Gateway
Protocol (BGP) anomaly detection, and real-time analytics frameworks and software architecture.
[2]
Design, prototype, and evaluate BGP anomaly indicators and build machine learning models (supervised
and unsupervised) to predict BGP IP prefix hijacking and Distributed Denial of Service (DDOS) attacks.
[3]
Build a visual interactive dashboard on the data being ingested from a BGP stream. This includes
displaying recent alerts and providing interactive visualizations of the context of a given alert using
timelines, histograms, and moving averages of several types of indicators that are being monitored.
3/ 9
[4]
Collaborate with the team to integrate the prototype as a real-time analytics engine that ingests
BGP real-time stream, and push it to Apache Kafka, and store the processed indicators in a MongoDB
Database and utilize Apache Spark MLLIB to predict the anomalies. The generated intelligence is
pushed to a web-based dashboard that enables the analyst to drill down and explore the incident.
[5]
Documentation and publish original research in peer-reviewed conferences and journals.
[6]
Testing
and validating the developed processes in large-scale and real-life BGP IP prefix hijacking and DDOS
incidents.
FEB 2014
SEP 2015
CyberSecurity Researcher, Carleton University and Xahive.com, Ottawa, Canada.
Objective: Investigate the security and privacy differentiation in end-to-end encryption services.
Challenge: Improve the value proposition through usability in end-to-end encryption services.
Tools/Software:
MATLAB and open-source Javascript libraries (AES-JS: AES-256 encryp-
tion/decryption, JSENCRYPT: RSA key-exchange).
Responsibilities: [1]
Prepare a prior art review on solutions that achieves communication security
and privacy.
[2]
Identify the limitations of the currently used communication encryption/decryption
standards.
[3]
Conduct a prio art review to identify communication security standards to overcome the
identified limitations.
[1]
Propose a multi-layer-based approach to enable an agile defensive response
for a system under attack by shifting Device-to-Device (D2D) communication to a new combination
of encryption implementation, routing protocol, and media access technique and frequency band.
[4]
Provide guidance on the chosen standards and how to customize/improve them.
[5]
Conduct MATLAB
system-level simulations to prove the feasibility of chosen algorithms/standards.
[6]
The applicable
algorithms/standards are recommended to the development team for integration and testing.
OCT 2009
JAN 2014
Assistant Professor, Khalifa University of Science & Technology, United Arab Emirates.
Objective: Teach and research wireless communications.
Challenge: Teach, supervise graduate students, and attract research funds.
Tools/Software:
MATLAB and Latex.
Responsibilities: [1]
Teaching: Introduction to Professional Engineering (ENGR 110), Probability and
Statistics (MATH 215), Wireless Communications (CMME 400), Communication Networks (CMME
320), Modulation and Coding Techniques (CMME 404), Digital Communications I (CMME 302),
Communication Engineering Project Laboratory (CMME 395) and Digital Communication Laboratory
(CMME 300).
[2]
Advise students on academic matters and career decisions.
[3]
Supervised the
following undergraduate projects: Mobile Bluetooth-Based Parking System, Multi-Sources Patient
Localization System for Emergency Response, Wireless Control of Self-Sustained Solar Power Generation
System, Evaluation of Spectrum Sensing Techniques in Cognitive Radio Networks, Solar Thermal Power
Generation System, Performance Evaluation of MIMO-OFDM System over Fading Channels, Simulation
of Interference Mitigation for OFDM Multi-hop LTE Networks, Single Carrier Frequency Division Multiple
Access Air Interface for LTE, Wireless Device to Alert Drivers to Keep a Safe Distance, Downlink Power
Control Techniques in CDMA Systems, Universal Mobile Telecommunications System (UMTS) Physical
Layer, Orthogonal Frequency Division Multiplexing (OFDM) Synchronization Techniques.
[4]
Electrical
and Computer Engineering (ECE) Committee Member: Responsible for establishing a Master of Science
in Electrical and Computer Engineering (M.Sc. in ECE) program.
[5]
Resources Committee Member:
Assessed current and future material requirements for the academic programs and propose new equipment
and resources.
[6]
External Relations Committee Member: Identified best practices for interaction with
all-important external constituencies and defined means that facilitated strong relationships with the
University.
4/ 9
SEP 2008
OCT 2009
Wireless System Engineer, Broadcom Corporation, San Diego, USA.
Responsibilities: [1]
Perform system-level test plan, execution, troubleshooting, optimization and
problem resolution on 2G/3G mobile devices.
[2]
Work with a multi-discipline team to test and
commercialize UMTS (Single SIM/Dual SIM) solutions.
[3]
Actively involved in producing requirements
for tools and system simulators and work closely with development teams and test equipment vendors
to come up with the appropriate test setups and associated automation.
[4]
Ensure close interaction
with the modem stack, multi-media, RF, drivers, 3GPP standard representatives, and System Design
Engineers to ensure proper test coverage of the features.
MAY 2008
–AUG 2008
System Engineer Intern, Qualcomm Incorporation, San Diego, USA.
Responsibilities: [1]
Analyze and simulate fourth-generation (4G) wireless network core components.
[2]
Characterize and improve the performance of RF/RX front in WiMax and LTE wireless systems.
[3]
Identify RF/RX front functionality for which minimum standard system performance requirements
should exist (Digital filtering (down-sampling & jammer rejection), DC offset removal (inner loop and
outer loop), automatic gain control (AGC), digitally controlled variable gain amplifier (DVGA), and
IQ-imbalance.)
[4]
Generate and consolidated performance requirements data and conducted analysis and
evaluation of critical performance metrics for 4G using MATLAB and C++.
[5]
Compare system-level
performance findings with other wireless standards (i.e., GSM, WCDMA, Bluetooth, WLAN).
MOOC Courses
2019 Structuring Machine Learning Projects | Andrew Ng | www.coursera.org/specializations/deep-learning
2019 Convolutional Neural Networks | Andrew Ng | www.coursera.org/specializations/deep-learning
2019 Sequence Models | Andrew Ng | www.coursera.org/specializations/deep-learning
2018 Neural Networks and Deep Learning | Andrew Ng | www.coursera.org/specializations/deep-learning
2018 Improving Deep Neural Networks | Andrew Ng | www.coursera.org/specializations/deep-learning
2017 Docker for DevOps : From development to production | Nick Janetakis | www.udemy.com
2017 Taming Big Data with Apache Spark and Python - Hands On! | Frank Kane | www.udemy.com
2017 Deep Learning for Natural Language Processing (NLP) | Jon Krohn| www.learning.oreilly.com
2016 Learning Python for Data Analysis and Visualization | Jose Portilla | www.udemy.com
2016 Machine Learning, Data Science and Deep Learning with Python | Frank Kane | www.udemy.com
Software & Tools
OS Unix, mac-os, Windows.
Programming Python, Java, C++, MATLAB.
Databases MySQL, PostgreSQL, MongoDB, Cassandra.
Optimization Java (MOEA-Framework), Python (DEAP).
Visualization Python (Matplotlib, Seaborn, Bokeh, Folium), JavaScript (D3.js, DC.js, Crossfilter.js).
Data Mining Python-Pandas, Python-Orange, MATLAB, KNIME.
ML Python (Scikit-learn, TensorFlow, PySaprk-MLLIB).
NLP Python (NLTK, spaCy, Gensim).
BI Power BI, Python-Superset.
Cloud Google Cloud Platform (GCP).
Big Data Apache Spark.
DevOps Docker.
Cloud AWS and Azure
Real-time Kafka and spark-streaming.
5/ 9
Publications [ J: Journal, C: Conference, B: Book Chapter, P: Patent ]
Cyber Security
P 2020 I. Abualhaol
, "Size-agnostic representation of portable executable (PE) files using convolutional
auto-encoder (AE) deep neural network (DNN)," to be filed by March 2020.
P 2020
R. Giagone and
I. Abualhaol
, "Sections header information with deep neural network (DNN) parallel
model architecture to predict malware," to be filed by April 2020.
P 2020
Y. Zhou,
I. Abualhaol
, and R. Giagone, "String-based feature representation with deep neural network
(DNN) to predict malicious portable executable (PE) files," to be filed by April 2020.
P 2020 I. Abualhaol
, R. Giagone, and Y. Zhou, "Multivariate ensemble deep neural network (DNN) based
model to detect malicious portable executable (PE) files," to be filed by May 2020.
B 2017
M. Gad and
I. Abualhaol
, "Securing Smart Cities Systems and Services: A Risk-Based Analytics-Driven
Approach," in Transportation and Power Grid in Smart Cities: Communication Networks and Services,
John Wiley, UK.
J 2017
A. Shah,
I. Abualhaol
, M. Gad, and M. Weiss, "Combining Exploratory Analysis and Automated
Analysis for Anomaly Detection in Real-Time Data Streams", Technology Innovation Management
Review, 7(4): 25-31, 2017.
C 2016 I. Abualhaol
and S. Muegge "Securing D2D Wireless Links by Continuous Authenticity with Legitimacy
Patterns," 49th Hawaii International Conference on System Sciences (HICSS), pp. 5763-5771, Jan 2016.
C 2016
M. Weiss,
I. Abualhaol
, and M. Amin, "A Leader-Driven Open Collaboration Platform for Exploring
New Domains," OpenSym conference, Berlin, Germany, August 17-19, 2016.
J 2016
A. Shah, S. Selman, and
I. Abualhaol
, "License Compliance in Open Source Cybersecurity Projects,"
Technology Innovation Management Review, 6(2): 28-35, 2016.
B 2014
C. Han, S. Muhaidat,
I. Abualhaol
, M. Dianati, and R. Tafazolli, "Intrusion Detection in Vehicular
Ad-Hoc Networks on Lower Layers," Security, Privacy, Trust, and Resource Management in Mobile and
Wireless Communications, pp. 148–173, IGI Global, PA, USA, 2014.
B 2013
Y. Abu Haeyeh,
I. Abualhaol
, Y. Iraqi, and S. Muhaidat, "Intrusion Detection in Vehicular Ad-Hoc
Networks: A Physical Layer Approach," Communication Systems: New Research, pp. 133–152, Nova
publishers, 2013.
Machine Learning | Data Mining | Big Data Analytics
J 2020
R. Alkurd,
I. Abualhaol
, and H. Yanikomeroglu, “Data-Driven Evolutionary Multi-objective Optimization
of Personalized Wireless Networks Resources,” submitted to IEEE transaction on communications, FEB
2020.
.
C 2020
R. Alkurd,
I. Abualhaol
, and H.Yanikomeroglu, “Data-driven user persona prediction framework for
personalized wireless networks,” submitted to IEEE International Conference on Communications (ICC),
Dublin, Irland, June 2020.
C 2020
R. Alkurd,
I. Abualhaol
, and H.Yanikomeroglu, “Deep neural network to predict user satisfaction in
personalized wireless networks,” submitted to IEEE International Conference on Communications (ICC),
Dublin, Irland, June 2020.
J 2019
R. Alkurd,
I. Abualhaol
, and H. Yanikomeroglu, “Big data-driven AI-based framework to enable
personalization in wireless networks,” Under review, IEEE Communications Magazine, DEC 2019
6/ 9
P 2019
R. Alkurd,
I. Abualhaol
, and H. Yanikomeroglu, ”Enabling wireless network personalization using Zone
of Tolerance modeling and predictive analytics”, W.O. Pat. Ser. No. PCT/CA2019/051197, filed on
AUG 29, 2019.
C 2019
R. Alkurd,
I. Abualhaol
, and H. Yanikomeroglu, “A Synthetic Dataset Modeling for Data-Driven AI-
Based Personalized Wireless Networks,” in Proc. 2019 IEEE International Conference on Communications
Workshops (ICC Workshops), Shanghai, China, May 2019.
C 2019
R. Alkurd,
I. Abualhaol
, and H. Yanikomeroglu, “Dataset modeling for data-driven AI-based personalized
wireless networks,” in Proc. 2019 IEEE International Conference on Communications (ICC), Shanghai,
China, May 2019.
C 2018 I. Abualhaol
, R. Falcon, R. Abielmona, and E. Petriu, "Data-Driven Vessel Service Time Forecasting
using Long Short-Term Memory Recurrent Neural Networks,” in IEEE International Conference on Big
Data, Seattle, United States, 10-13 DEC, 2018.
C 2018 I. Abualhaol
, R. Falcon, R. Abielmona, and E. Petriu, "Mining port congestion indicators from big AIS
data,” in IEEE World Congress on Computational Intelligence (IEEE WCCI), Rio de Janeiro, Brazi, 8-13
July., pp. 3743–3750, 2018.
C 2017
I. Al Ridhawi, N. Mostafa, Y. Kotb, M. Aloqaily, and
I. Abualhaol
, "Data Caching and Selection in 5G
Networks Using F2F Communication," in 2017 IEEE International Symposium on Personal, Indoor and
Mobile Radio Communications (PIMRC 2017), Montreal, QC, Canada.
Modeling | Optimization
C 2020
F. Cheraghchib,
I. Abualhaol
R. Falcon, R. Abielmona, B. Raahemi, and E. Petriu, "Distributed
Multi-Objective Cooperative Coevolution Algorithm for Big-Data-Enabled Vessel Schedule Recovery
Problem," accepted for publication in IEEE Conference on Cognitive and Computational Aspects of
Situation Management (CogSIMA), University of Victoria, British Columbia, Canada, May 4-7, 2020.
J 2018
F. Cheraghchib,
I. Abualhaol
R. Falcon, R. Abielmona, B. Raahemi, and E. Petriu, "Modeling the Speed-
based Vessel Schedule Recovery Problem using Evolutionary Multiobjective Optimization”, Information
Sciences 448 (2018): 53-74.
C 2017
F. Cheraghchib,
I. Abualhaol
, R. Falcon, R. Abielmona, B. Raahemi, and E. Petriu, "Big-Data-Enabled
Modelling and Optimization of Granular Speed-based Vessel Schedule Recovery Problem”, in 2017 IEEE
International Conference on Big Data, Boston, MA, USA, Dec 11-14, 2017.
C 2016
Rawan Alkurd,
I. Abualhaol
, Raed Shubair, and Muriel Medard, " Optimum HDAF Relay-Assisted
Combining Scheme with Relay Decision Information," IEEE 84th Vehicular Technology Conference,
Montréal, Canada, September 18-21, 2016.
C 2015
R. Alkurd, R. Shubair, and
I. Abualhaol
, "Optimum Decode-and-Forward Relay-Assisted Combining
Scheme with Relay Decision Information," IEEE International Conference on Communications (ICC), pp.
2331–2337, June 2015.
C 2015
R. Alkurd, R. Shubair, and
I. Abualhaol
, "Modeling Conditional Error Probability for Hybrid Decode-
Amplify-Forward Cooperative System," IEEE Wireless Communications and Networking Conference
(WCNC), 7-12, March 2015.
J 2011 I. Abualhaol
and M. Matalgah "Unified Analysis of Optimized Relay-based Wireless Systems," Journal
of Selected Areas in Telecommunications (JSAT), July Edition, 2011.
J 2009 I. Abualhaol
and M. Matalgah, "Throughput Optimization of Cooperative Teleoperated UGV Network,"
International Journal of Mobile Computing and Multimedia Communications (IJMCMC), pp. 32–46,
2009.
7/ 9
C 2006 I. Abualhaol
and M. Matalgah "Throughput Optimization of Cooperative UAVs Using Adaptive Channel
Assignment," IEEE Wireless Communications and Networking Conference (WCNC), vol. 4, pp. 2279–
2284, 3 -6 April, 2006.
Statistical and Performance Analysis
C 2014
R. Alkurd,
I. Abualhaol
, and S. Muhaidat, "Error Rate Performance Analysis of Cooperative SCR in
VANETs over Generalized Fading Channels," IEEE Wireless Communications and Networking Conference
(WCNC), pp. 3184–3189, 6-9 April, 2014.
C 2013
R. Alkurd, R. Shubair,
I. Abualhaol
, "Error rate performance analysis of cooperative MRC receivers over
generalized fading channels," IEEE 20th International Conference on Electronics, Circuits, and Systems
(ICECS), pp. 201–204, 8-11 Dec, 2013.
C 2013
E. Salahat and
I. Abualhaol
, "General BER analysis over Nakagami-m fading channels," in 6th Wireless
and Mobile Networking Conference (WMNC), pp. 1–4, 23-25 April, 2013.
J 2011 I. Abualhaol
and M. Matalgah "Performance analysis of cooperative multi-carrier relay-based UAV
networks over generalized fading channels," International Journal of Communication Systems (IJCS),
Jan, 2011.
C 2011 I. Abualhaol
, "Symbol Error Rate Analysis of Relay-based Wireless Systems", IEEE 22nd International
Symposium on Personal Indoor and Mobile Radio Communications, pp. 1894–898, 11-14 Sep, 2011.
C 2011 I. Abualhaol
and M. Bawa’aneh "Capacity analysis of cooperative relay-based communication system,"
IEEE GCC Conference and Exhibition, pp. 21–24, 19-22 Feb, 2011.
C 2007 I. Abualhaol
and M. Matalgah "End-to-End Performance Analysis of Cooperative Relay-Based Wireless
System Over Generalized Gaussian-Finite-Mixture Fading Channels," IEE 50th IEEE Global Communica-
tions Conference (GLOBECOM), pp. 3942–3947, 26-30 Nov 2007.
C 2007 I. Abualhaol
and M. Matalgah "Capacity Analysis of MIMO System Over Identically Independent
Distributed Weibull Fading Channels," IEEE International Conference on Communications (ICC), pp.
5003-5008, 24-28 June 2007.
C 2006 I. Abualhaol
and M. Matalgah "Outage Probability Analysis in a Cooperative UAVs Network Over
Nakagami-m Fading Channels," IEEE 64th Semiannual Vehicular Technology Conference (VTC), pp.
1–4, 25-28 Sep 2006.
Wireless and Cooper ative Communications
C 2014
R. Alkurd, R. Shubair, and
I. Abualhaol
, "Survey on device-to-device communications: challenges and
design issues," 12th IEEE International New Circuits and Systems Conference (NEWCAS), pp. 361–364,
22-25 June 2014.
C 2013
S. Al Maeeni, S. Muhaidat, and
I. Abualhaol
, "Non-coherent detection for cooperative OFDM-based
system over time-varying fading channels," 20th IEEE International Conference on Electronics, Circuits,
and Systems (ICECS), pp. 197–200, 8-11 Dec 2013.
C 2013
R. Alkurd,
I. Abualhaol
, and S. Muhaidat, "An efficient approximation of
Q
(
x
) function and general
BER performance analysis," IEEE 7th GCC Conference and Exhibition, pp. 367–371, 17-20 Nov 2013.
C 2013
E. Salahat and
I. Abualhaol
, "Generalized average BER expression for SC and MRC receiver over
Nakagami-m fading channels," IEEE 24th International Symposium on Personal Indoor and Mobile Radio
Communications (PIMRC), pp. 3360–3365, 8-11 Sep 2013.
C 2013
H. Eghbali,
I. Abualhaol
, S. Muhaidat, and Y. Iraqi, "Random-based Fair Allocation Algorithm with
Fuzzy Comprehensive Evaluation for Single Carrier Multi-Relay Cooperative Networks," 19th European
Wireless Conference (EW), pp. 1–5, 16-18 April 2013.
8/ 9
J 2012
M. Ahmed, S. Jimaa, and
I. Abualhaol
, "Performance Enhancements of MIMO-OFDM system using
Various Adaptive Receiver Structures," International Journal of Computer and Information Technology
(IJCIT), pp. 99–106, vol. 1, 2012.
C 2012
M.A Ahmed, S.A Jimaa, and
I. Abualhaol
, "Enhanced channel estimation technique in MIMO-OFDM
system," IEEE 8th International Conference on Wireless and Mobile Computing, Networking and
Communications (WiMob), pp. 545–549, 8-10 Oct 2012.
C 2012
M. Ahmed, S. Jimaa, and
I. Abualhaol
, "BER Enhancement of MIMO-OFDM Using an Optimized
NLMS Receiver," 6th Asia Modeling Symposium (AMS), pp. 211–214, 29-31 May 2012.
C 2012
H. Eghbali,
I. Abualhaol
, S. Muhaidat, and Y. Iraqi, "Cluster-Based Fair Allocation Algorithm for
Multi-Relay Single Carrier Distributed Networks," IEEE 75th Vehicular Technology Conference (VTC
Spring), pp. 1–5, 6-9 May 2012.
C 2012
H. Eghbali, S. Muhaidat, and
I. Abualhaol
, "Enhanced ZP-OFDM receiver in multi-relay cooperative
networks," 25th IEEE Canadian Conference on Electrical & Computer Engineering (CCECE), pp. 1–6,
29 April-2 May 2012.
C 2012
Y. Iraqi,
I. Abualhaol
, and S. Muhaidat, "Lifetime Evaluation of Cooperative OFDM WSNs," IEEE
Wireless Communications and Networking Conference (WCNC), pp. 2054–2058, 1-4 April 2012.
C 2011
H. Eghbali, S. Muhaidat, and
I. Abualhaol
, "Distributed single-carrier frequency-domain equalization
for multi-relay cooperative networks over frequency-selective Rician channels," 45th Asilomar Conference
on Signals, Systems and Computers(ASILOMAR), pp. 1115–1120, 6-9 Nov 2011.
J 2011
H. Eghbali,
I. Abualhaol
, and S. Muhaidat, "Enhanced Iterative-based ZP-OFDM Receiver in Multi-
Relay Cooperative Networks," Journal of Selected Areas in Telecommunications (JSAT), September
Edition, 2011.
B 2010 I. Abualhaol
and M. Matalgah "Resource Allocation for a Cooperative Broadband MIMO-OFDM
System," Cooperative Communications for Improved Wireless Network Transmission: Frameworks for
Virtual Antenna Array Applications, pp. 382–398, IGI Global, PA, USA, 2010.
C 2010 I. Abualhaol
and M. Matalgah "Performance analysis of multi-carrier relay-based UAV network over
fading channels", IEEE GLOBECOM Workshops, pp.1811–1815, 6-10 Dec 2010.
C 2010 I. Abualhaol
and Y. Iraqi, "Random-based fair allocation in Multi-Relay cooperative OFDM system,"
IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications
(WiMob), pp. 596–599, 11-13 Oct 2010.
C 2010 I. Abualhaol
, M. Matalgah, and A. Abu-Abed "Enhanced cooperative coding for relay-based MIMO-
OFDM systems," IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communica-
tions (PIMRC), pp. 2299–2303, 26-30 Sep 2010.
C 2008 I. Abualhaol
and M. Matalgah "Subchannel-Division Adaptive Resource Allocation Technique for Coop-
erative Relay-Based MIMO-OFDM Wireless Systems," IEEE Wireless Communications and Networking
Conference (WCNC), pp. 1002–1007, March 31- April 3, 2008.
C 2008 I. Abualhaol
and M. Matalgah "Capacity Analysis of MIMO System Over Nakagami-
m
Fading Channels
Using Finite Mixture with Expectation-Maximization Algorithm," IEEE International Conference on
Computer Systems and Applications (AICCSA), pp. 309–316, March 31- April 4, 2008.
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