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Supervisor: Dr. M Khurram Ehsan

 

Context-Aware Resource Prediction Framework for a Sustainable Urban Radio Environment in NG-HetNetIs

 

Supervisor: Dr. M Khurram Ehsan/Snr. Associate Professor

Campus/School/Dept: BUIC-E8/BSEAS/CS

RAC Approved Supervisor for Research Areas: Wireless Networks, AI

Enabled Networks Information/Signal Processing

Supervisory Record:  

  • PhD Produced:0
  • PhD Enrolled:1
  • MS/MPhil Produced:6
  • MS/MPhil Enrolled:3

Co-Supervisor: Dr. Arshad Farhad/Assistant Professor

 

Topic Brief Description:

The Context-Aware Resource Prediction Framework for a Sustainable Urban Radio Environment in NG-HetNets aims to enhance resource allocation and management in next-generation heterogeneous network infrastructures (NG-HetNetIs) within urban settings. By observing real-time radio data such as user mobility, traffic patterns, and environmental factors, the framework predicts resource demands and optimizes network performance. This approach ensures efficient utilization of network resources, reduces energy consumption, and supports sustainable operations while maintaining high-quality service delivery. It addresses the challenges of scalability and dynamic adaptability inherent in NG-HetNetIs, paving the way for more resilient and eco-friendly urban communication systems.

 

Research Objectives/Deliverables:

  1. Develop a context-aware framework to predict resource demands in NG-HetNetIs by analyzing real-time radio data, including user mobility, traffic patterns, and environmental factors.
  2. Ensure sustainable network operations by reducing energy consumption and promoting eco-friendly practices without compromising service quality.

 

Research Questions: 

  1. How can real-time radio data be leveraged to accurately predict resource demands in NG-HetNetIs?
  2. How can resource management strategies in NG-HetNetIs be designed to balance sustainability and service quality in urban environments?

 

Candidate’s Eligibility Profile:

  1. The applicant must have an MS/MPhil/Equivalent degree in CS/CE/EE with CGPA >0. Besides, applicants must have a strong background in wireless networks, statistic, machine learning and related fields.
  2. Experience with programming languages such as MATLAB, Python or C/C++, is advantageous. Candidates should thrive in an international environment and have excellent communication skills to actively contribute to team research efforts.
  3. Proficiency in spoken and written English is essential. We value independence and responsibility while promoting teamwork and collaboration among colleagues.