Course Objective

To provide students with a comprehensive understanding of the principles, technologies, and strategies involved in designing and managing global communication networks.

Target Audience

Graduate students specializing in telecommunications, network engineering, or related fields.

Use of generative AI

in Assignments (which are part of the exam)

Course content from course database

The focus of the course will be on providing the students of a general understanding of the complexity of the core and transport infrastructure. It will contain topics such as network topologies, network layering and stratification, reference models, complex communication systems, transmission and multiplexing protocols and their internal relation, logical vs. physical networks, traffic characterization, network resilience, network control and management, node architecture, energy efficiency, quality of service (QoS) and Quality of Experience, and transition from legacy to next generation networks. Assignments, groupwork, peer-evaluation and presentations are an integral part of the course.

At an appropriate level the course will incorporate topics from ongoing research activities within the department as well as guest lecturers from Danish industry.

An example of a question in an assignment is provided below:

Application of AI in Assignments:

  • Assignment Description: Assign students the task of analyzing and proposing future telecommunications strategies for Denmark. First writing their own strategy, and then ask ChatGBT/CoPilot to do the same.
  • Integration of Generative AI: Instruct students to compare their proposed strategies with those generated by ChatGBT/CoPilot
  • Criteria for Comparison: Encourage students to evaluate the similarities, differences, and potential advantages or limitations of their strategies compared to those generated by ChatGBT.
  • Ethical Guidelines: Emphasize the importance of ethical considerations when utilizing AI technologies, including bias and transparency.

Implementation Steps / Practicalities

  • Access to Generative AI Platform: Provide students with access to a platform or tool equipped with generative AI capabilities. Students mainly use ChatGBT, but DTU now has a license for CoPilot
  • Training and Guidance: Offer tips or tutorials to familiarize students with using the generative AI platform effectively for their assignments. (Most students are however already proficient in the use of these tools)

Evaluation and Assessment

  • Evaluation Criteria: Assess students based on the depth of their analysis, the clarity of their comparisons, and the critical insights gained from comparing their strategies with those generated by ChatGBT/Copilot.
  • Feedback and Reflection: Provide constructive feedback to students on their assignments, highlighting areas of improvement and encouraging reflection on the implications of integrating generative AI in telecommunications strategy development.
  • Research and future learning: Encourage students to pursue research projects or initiatives exploring novel applications of AI in telecommunications infrastructure and design. Students can gain valuable insights into future telecommunications strategies and develop critical thinking skills essential for navigating the complexities of the telecommunications industry.