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Kwasi Adu-Boahen Opare

Telecommunications Engineering

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About

Dr. Kwasi Adu-Boahen Opare received the BSc. Electrical/Electronic Engineering and MSc. Telecommunication Engineering degrees in 2004 and 2010, respectively, from the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana. He obtained a PhD in Information and Communication Engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, Sichuan, China. His research interests include Wireless Communications and Data Communication Networks.

Research Summary

(inferred from publications by AI)

The researcher has made significant contributions across various advanced fields, focusing on network security, photonics, advanced MIMO systems, communication technologies, and their applications in areas such as anomaly detection, optical networks, massive MIMO, privacy/secure communications, network design, measurement techniques, aerospace engineering, transportation planning, and more.

Research Themes

All Papers

An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers(2021)
Mode Combination in an Ideal Wireless OAM-MIMO Multiplexing System(2015)
Performance of an ideal wireless orbital angular momentum communication system using multiple-input multiple-output techniques(2014)
The Degrees of Freedom in Wireless Line-of-Sight OAM Multiplexing Systems Using a Circular Array of Receiving Antennas(2015)
The Effect of Receiver-Side Circular Antenna Arrays on Bit Error Probability in a Wireless Line-of-Sight OAM Communication System(2015)
A 100 Gbps OFDM-Based 28 GHz Millimeter-Wave Radio over Fiber Fronthaul System for 5G(2021)
NetLogo implementation of an ant colony optimisation solution to the traffic problem(2015)
Mitigating Pilot Contamination in Massive MIMO Using Cell Size Reduction(2017)
The effect of channel estimation errors on the energy efficiency of downlink massive MIMO systems(2015)
A greedy algorithm for pilot contamination mitigation using LAP(2023)
The optimum number of base station antennas in an energy efficient downlink massive MIMO system(2015)
Performance Analysis of SISO and MIMO Communication Systems using Multiple Point Scatter Model(2019)
Stochastic geometry modelling and analysis for cooperative NOMA with large transmit antennas for 5G applications and beyond(2023)
Reconfigurable Intelligent Reflecting Surfaces Enabled Spectrum Access for Beyond 5G Networks(2024)
A Heuristic K-Means-Based Unsupervised Machine Learning Model for Unmanned Aerial Vehicle Mounted Reconfigurable Intelligent Surface for Enhanced 5G and Beyond Networks Performance(2025)
IRS‐Assisted Communication in 3D Stochastic Geometry Utilizing Large Antenna Transmitters With Hardware Impairments(2025)
Heterogeneous Architecture for DSRC/LTE Vehicular Communication Networks Based on the ITS Reference Architecture with Fuzzy Logic for Decision-Making(2023)
An Ant Colony Optimization Solution to the Optimum Travel Path Determination Problem in VANETS: A NetLogo Modelling Approach(2015)
A Distributed Intelligent Traffic System Using Ant Colony Optimization: A NetLogo Modeling Approach(2016)
An Optimized Vertical Handover Decision Model for the Heterogeneous DSRC/LTE Vehicular Networks(2023)
Emergency Call Routing Schemes(2017)
Spam detection through link authorization from neighboring nodes(2015)
Optimization of PHY Layer Protocol for Wireless and Mobile Networks(2014)
Temperature Calibration of a Passive Eight-Port Network for Noise Parameters Measurement(2015)
A 6-Port Network Technique for Extraction of 2-Port DUT Noise Correlation Matrix: A Theoretical Verification Through Modeling and Simulation(2017)
The Method of Context-Dependent Annotation Based on the Spectral Language Model in Information Systems for Monitoring Emergencies(2021)
Optimal Coverage Enhancement for Multiple UAVs Using Multi-agent Learning Technique(2023)

Collaboration Network

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About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.