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Imran Khan is a highly accomplished RAN Virtualization Engineer and IEEE Senior Member with over 15 years of experience in 4G, 5G, vRAN, and O-RAN technologies. Currently serving as a RAN Virtualization SME Engineer at Samsung Electronics America, he plays a pivotal role in deploying the world's first cloud-native O-RAN 5G network for Dish, leading an 8-member team in the integration and optimization of Samsung's O-RAN-compliant 5G solutions. His expertise spans Telco Cloud implementations, NFV, Kubernetes, Docker, and microservices, with a deep understanding of 3GPP and O-RAN standards, call flows, KPIs, and network performance analysis. Imran is adept in validating RANGPT solutions, troubleshooting multi-vendor network interworking issues, and ensuring optimal network performance through parameter tuning and RF optimization. His role involves deploying Samsung vRAN 5G software on VMware, Dell x86 COTS servers, and AWS public cloud across various availability zones, significantly accelerating 5G commercial deployments. Throughout his career, he has worked extensively with signaling and log analysis tools such as TM500, XCAL/XCAP, QXDM, and Wireshark to conduct functional, stability, and performance testing. His contributions at Samsung include analyzing 5G mobile logs and gNB logs to enhance network quality, optimizing Dish’s 5G network for superior call quality, and conducting FCC compliance testing while collaborating with Samsung R&D to identify and resolve software issues. Prior to Samsung, Imran was a Senior Member of Customer Engineering at Altiostar Networks, where he spearheaded vRAN/O-RAN (LTE/NR) trial and deployment projects, led 5G/O-RAN proof-of-concepts with global telecom operators like Etisalat and STC, and worked closely with Qualcomm, Nokia, and Dish for IODT testing. His responsibilities included deploying Altiostar’s 5G software on AWS, orchestrating 5G services using Kubernetes, refining performance indicators, and integrating multi-vendor solutions. During his tenure at Ericsson in Thailand, he played a crucial role in optimizing DTAC’s 4G/LTE network by improving coverage, implementing new features, and validating Ericsson’s Massive MIMO technology. Additionally, as an RF Technical Manager at Quadgen Wireless, he set up a global resource center in Bangalore to support AT&T’s LTE/4G network optimization, hiring and training engineers while optimizing AT&T sites across Ohio, Missouri, and Kansas. His earlier career included RF optimization roles at Ericsson Global India, Alcatel-Lucent, and projects with Orange France and Reliance GSM & 3G, where he managed UTRAN network performance and swap site optimizations. Imran's technical proficiency is complemented by numerous industry certifications, including Qualcomm 5G SA Associate Certification and IEEE Senior Membership. Recognized for his outstanding contributions, he was twice awarded the Samsung Rock Star Award for his work on Dish’s 5G deployment milestones. His expertise in RF shakedown and optimization, multi-vendor integrations, cloud-native deployments, and automation scripting makes him a key figure in the advancement of next-generation telecom networks. Holding a Postgraduate Diploma in Data Analytics from IIIT Bangalore and a Bachelor's degree in Information Science from MVJ College of Engineering, India, he is fluent in English and based in Aurora, Colorado, with an H1B visa status in the USA. His LinkedIn profile, showcasing his vast industry experience.

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Skills

Experience

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RAN Virtualization SME Engineer - Professional III

Samsung Electronics America, Inc.

Oct-2022 to Present

Education

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International Institute of Information Technology, Bangalore (IIIT-B)

PG Diploma in Data Analytics

Passout Year: 2018
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MVJ College of Engineering, Bangalore

BE in Information sciences

Passout Year: 2008

Publication

Research Article
  • dott image December, 2020

Performance Tuning of 5G Networks Using AI and Machine Learning Algorithms

Journal : IJRPS - International Journal for Research Publication and Seminar

As the demand for faster and more reliable mobile networks intensifies, the deployment of 5G has emerged as a transformative solution to meet the growing needs of connectivity. However, to f...

Peer-Reviewed Articles

COMPARATIVE ANALYSIS OF REVERSE IMAGE SEARCH ENGINES USING DIVERSE IMAGE SETS

Eight well-known reverse image search engines—Google, Bing, TinEye, Yandex, Baidu, Getty Images, Shutterstock, and Alamy—are compared in this study based on several different factors. Language support, speed, accuracy, facial recognition, geographic coverage, cropping feature, number of images retrieved, ease of use, mobile app availability, privacy measures, input options, supported file formats, search methods, and additional features are some of these requirements. The study outlines each engine's advantages and disadvantages. Both Google and Bing are very user-friendly, fast, and support multiple languages. However, Google is more accurate and has features like facial recognition and SafeSearch. Yandex offers comparable functionality but targets the Russian market. TinEye promotes privacy and collects very little data, however, it has trouble with unique photos and doesn't have many sophisticated capabilities. Baidu offers little privacy and openness and caters mostly to the Chinese market. Although Shutterstock and Getty Images have extensive privacy policies, their accuracy is not as high. Alamy has a reduced precision of retrieval but complies with data standards. According to the analysis, each engine serves a particular purpose. Google or Bing may be preferred by users looking for smart image detection and user-friendliness. TinEye might work for users who are concerned about their privacy. In the end, the decision is based on personal preferences and search objectives.

DESIGN AND IMPLEMENTATION OF Wi-Fi DEAUTHENTICATION SYSTEM USING NODEMCU ESP8266

Network security is seriously threatened by Wi-Fi de-authentication attacks, which frequently lead to data interception, illegal access, and service interruption. The mechanics and ramifications of these assaults are explored in detail in this research study, which highlights how they could jeopardize network availability, secrecy, and integrity. To bridge theoretical understanding with actual experimentation, the paper presents a practical implementation of a Wi-Fi deauther utilizing the NodeMCU ESP8266 microcontroller platform. With the use of programs like the Arduino IDE and NodeMCU Flasher, the Wi-Fi deauther was created and put through testing to identify and stop de-authentication threats instantly. The system's high detection accuracy, quick response times, and little effect on network performance as a whole are demonstrated by the experimental findings. The NodeMCU ESP8266 platform demonstrated good resource management by managing the detection and countermeasures while keeping CPU use below 70% and guaranteeing less than 5% reduction in network performance and latency. This study advances wireless network security by demonstrating a scalable, affordable method of thwarting de-authentication attacks and by suggesting further improvements that would include machine learning integration and wider assault coverage. For network managers, cybersecurity experts, and researchers looking to strengthen wireless network defenses, the findings offer insightful information and useful recommendations.

Blockchain using Virtual TRY-ON

In today’s dynamic retail environment, the shift towards online shopping necessitates innovative solutions that enhance customer engagement and satisfaction. This project introduces a virtual try-on clothing platform designed to revolutionize the online shopping experience by merging cutting-edge augmented reality (AR) and machine learning technologies. The platform enables users to visualize how garments will fit and appear on their unique body shapes without the need to visit a physical store. By offering a user-friendly interface, the website allows customers to upload personal images or utilize real-time video features, facilitating an interactive and personalized shopping experience. Key functionalities include accurate size recommendations tailored to individual measurements, as well as curated fashion suggestions that align with users' personal styles. These enhancements aim to minimize return rates—a significant challenge in e-commerce—while simultaneously boosting customer satisfaction and driving sales. Additionally, the platform fosters social interaction through built-in sharing capabilities, allowing users to solicit feedback from friends and family, thus enriching the decision-making process. This aspect not only enhances the shopping experience but also builds a sense of community around fashion choices. By integrating advanced technology with a seamless and engaging user experience, this virtual try-on website represents a substantial advancement in online fashion retail. It sets the stage for a more personalized and interactive shopping journey, ultimately redefining how consumers engage with fashion in the digital age. As we look to the future, this platform aims to become a cornerstone of online retail, reflecting the evolving needs and preferences of today’s consumers.

The Power of AI and Machine Learning in Cybersecurity: Innovations and Challenges

Networks and sensitive data are no longer adequately protected by traditional security methods due to the ongoing evolution and sophistication of cyber-attacks. Cybersecurity can be enhanced with the exploitation of machine learning and artificial intelligence techniques, which make threat detection more effective and efficient. This article, while giving an outline of the field's present position, discusses the difficulties in adapting machine learning and artificial intelligence (ML) to cybersecurity. The research discusses machine learning methods that are applied to tasks like malware classification, anomaly detection, and network intrusion detection. Lastly, the necessity for sizable labeled datasets, the adversarial attacks on machine learning models, and the adversity of deciphering models of black-box ML are some of the boundaries and challenges that are also covered.

Design of 4-bit ALU for low-power and High-speed Applications.

This paper presents a novel design and optimization of a 4-bit Arithmetic Logic Unit (ALU) utilizing 90nm CMOS technology, specifically addressing the longstanding carry-out issue prevalent in existing architectures. Notably, our proposed 4-bit ALU architecture successfully minimizes delay and power consumption by incorporating an optimized carry-out design employing AND gates. A comprehensive comparison of three logic styles - Pass Transistor Logic (PTL), Complementary Metal-Oxide-Semiconductor (CMOS), and Transmission Gate Logic (TGL) - is conducted, yielding significant improvements in power-delay tradeoffs. Simulation results validate the efficacy of our design in resolving the carry-out issue, making it an attractive solution for low-power, high-speed digital applications.

Honours & Awards

dott image
Rock Star Award
Awarded by:

Samsung Electronics America - Network Business

 Year: 2023

Scholar9 Profile ID

S9-102024-0406195

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Authors

Antara FNU
Antara FNU

PhD Student at Unive...
Archit Joshi
Archit Joshi

Android Engineer
Imran Khan
Imran Khan

RAN Virtualization S...
Om Goel
Om Goel

Founder & CEO of Res...