The M.Tech program in Wireless Networks & Applications is designed to produce highly skilled academic and research professionals in the ever-evolving and essential wireless communication and networking field. The program covers a wide range of subjects, including the latest advancements in wireless communications, computer science, computer networks, mobile computing, sensor networks, embedded systems, internet-of-things, signal processing, multimedia systems, machine learning, big data analysis, and applications related to smart city technologies.
Building on the success of the WINSOC joint project with international partners, this M-Tech program is aimed at strengthening academic and research initiatives in the field of wireless networks and applications. Students who complete this program will be well-equipped to enter a diverse array of industries, including but not limited to computer science, communication networks, the internet of things, earth and environmental sciences, disaster management, healthcare, e-governance, bio and nano-technologies, VLSI, embedded systems, agriculture, chemical industries, and strategic planning.
In addition, this program emphasizes the latest technology trends and tools, including 5G & IoT networks, SDN/NFV, edge computing, cloud computing, artificial intelligence, blockchain, AR/VR, and cybersecurity. Graduates of this program will have the knowledge and skills needed to navigate the rapidly changing landscape of wireless communication and networking. They will be well-positioned to make significant contributions to the field.
Amrita Vishwa Vidyapeetham has not appointed any Agent or Third-Party Client for securing admission in any programme. Students are hereby requested to contact only the toll-free number on our website for any admission related queries.
– Issued in Public Interest by Directorate of Admissions and Academic Outreach
| Course Code | Type | Course | L T P | Credit |
| 23WN601 | FC | Signal Processing for Wireless Communication | 2 0 1 | 3 |
| 23MA602 | FC | Probability and Statistical Inference | 2 0 1 | 3 |
| 23WN602 | FC | Advanced Computer Networks | 2 0 1 | 3 |
| 23WN603 | SC | Principles of Wireless Communication Systems | 3 0 1 | 4 |
| 23WN604 | SC | Embedded System Design | 3 0 1 | 4 |
| 23WN681 | FC | Advanced Computer Programming | 0 0 1 | 1 |
| 22AVP103 | Mastery Over Mind | 1 0 2 | 2 | |
| 22ADM501 | HU | Glimpses of Indian Culture * | 2 0 1 | P/F |
| 23HU601 | HU | Career Competency I* | 0 0 3 | P/F |
| Credits | 20 | |||
| *Non Credit Course | ||||
| Course Code | Type | Course | L T P | Credit |
| 23WN611 | Design and Analysis of Algorithms | 2 0 1 | 3 | |
| 23WN612 | Internet of Things: Architecture and System Design | 3 0 1 | 4 | |
| E | Elective I | 2 0 1 | 3 | |
| E | Elective II | 2 0 1 | 3 | |
| 23WN613 | Mobile Communication Networks | 2 0 1 | 3 | |
| 23RM702 | Research Methodology | 2 0 0 | 2 | |
| 23HU611 | Career Competency II | 0 0 3 | 1 | |
| 23WN682 | P | Live-in-Labs-I Participatory Design | 0 0 0 | 0 |
| Credits | 19 | |||
| Course Code | Type | Course | L T P | Credit |
| E | Elective III | 2 0 1 | 3 | |
| 23WN798 | P | Dissertation-Phase I | 10 | |
| 23WN781 | P | Live-in-Labs-II-Lab-to-Field: People Centred Innovation | 0 0 0 | 1 |
| Credits | 14 | |||
| Course Code | Type | Course | L T P | Credit |
| 23WN799 | P | Dissertation – Phase II | 16 | |
| Credits | 16 | |||
Total Credits: 69
| Course Code | Type | Course | L T P | Credit |
| 23WN601 | FC | Signal Processing for Wireless Communication | 2 0 1 | 3 |
| 23MA602 | FC | Probability and Statistical Inference | 2 0 1 | 3 |
| 23WN602 | FC | Advanced Computer Networks | 3 0 1 | 4 |
| 23WN681 | FC | Advanced Computer Programming | 0 0 1 | 1 |
| 23WN611 | FC | Design and Analysis of Algorithms | 2 0 1 | 3 |
| Course Code | Type | Course | L | T | P | Cr |
| 21WN611 | SC | Principles of Wireless Communication Systems | 3 | 0 | 1 | 4 |
| 21WN612 | SC | Embedded System Design | 3 | 0 | 1 | 4 |
| 21WN613 | SC | Mobile Communication Networks | 2 | 0 | 1 | 3 |
| 21RM622 | SC | Research Methodology | 2 | 0 | 0 | 2 |
| 21WN614 | SC | Internet of Things: Architecture and System Design | 3 | 0 | 1 | 4 |
| Elective I | ||||
| Course Code | Type | Course | L T P | Credit |
| 23MA731 | E | Random Processes and Queueing Models | 2 0 1 | 3 |
| 23MA732 | E | Linear Algebra and its Applications | 2 0 1 | 3 |
| 23MA733 | E | Detection and Estimation Theory | 2 0 1 | 3 |
| 23MA734 | E | Computational Optimization | 2 0 1 | 3 |
| 23MA735 | E | Graph Theory and its Applications in Wireless Networks | 2 0 1 | 3 |
| Elective II | ||||
| 23WN731 | E | Applied Machine Learning | 2 0 1 | 3 |
| 23WN732 | E | Open Source Networking | 2 0 1 | 3 |
| 23WN733 | E | Distributed Network Algorithms | 2 0 1 | 3 |
| 23WN734 | E | Introduction to Platform Technologies and APIs | 2 0 1 | 3 |
| 23WN735 | E | Edge and Fog Computing | 2 0 1 | 3 |
| 23WN736 | E | Network and Application Security | 2 0 1 | 3 |
| 23WN737 | E | Adaptive Signal Processing | 2 0 1 | 3 |
| 23WN738 | E | Emerging Wireless Communication Technologies | 2 0 1 | 3 |
| 23WN739 | E | Big Data and Applications | 2 0 1 | 3 |
| Elective III | ||||
| 23WN741 | E | Advanced Signal Processing | 2 0 1 | 3 |
| 23WN742 | E | Distributed Systems | 2 0 1 | 3 |
| 23WN743 | E | Wireless Local Area Networks | 2 0 1 | 3 |
| 23WN744 | E | Antenna Design and Applications | 2 0 1 | 3 |
| 23WN745 | E | Open RAN Networks | 2 0 1 | 3 |
| 23WN746 | E | Introduction to Block chain and Distributed Ledger Technology | 2 0 1 | 3 |
| 23WN747 | E | Coding and Information Theory | 2 0 1 | 3 |
| 23WN748 | E | Private Cellular Networks | 2 0 1 | 3 |
| 23WN749 | E | Introduction to Deep Learning | 2 0 1 | 3 |
| Course Code | Type | Course | L | T | P | Cr |
| 23WN798 | P | Dissertation – Phase I | 10 | |||
| 23WN799 | P | Dissertation – Phase II | 16 | |||
| 23WN682 | P | Live-in-Labs – I | 0 | |||
| 23WN781 | P | Live-in-Labs – II | 1 |
| Course Code | Type | Course | L | T | P | Cr |
| Specialization I: Wireless Communications | ||||||
| 23WN741 | E | Advanced Signal Processing | 2 | 0 | 1 | 3 |
| 23WN747 | E | Coding and Information Theory | 2 | 0 | 1 | 3 |
| 23WN744 | E | Antenna Design and Applications | 2 | 0 | 1 | 3 |
| 23WN748 | E | Private Cellular Networks | 2 | 0 | 1 | 3 |
| 23WN738 | E | Emerging Wireless Communication Technologies | 2 | 0 | 1 | 3 |
| 23WN737 | E | Adaptive Signal Processing | 2 | 0 | 1 | 3 |
| 23MA732 | E | Linear Algebra and its Applications | 2 | 0 | 1 | 3 |
| 23MA733 | E | Detection and Estimation Theory | 2 | 0 | 1 | 3 |
| 23MA734 | E | Computational Optimization | 2 | 0 | 1 | 3 |
| Specialization II: Mobile Networks | ||||||
| 23WN742 | E | Distributed Systems | 2 | 0 | 1 | 3 |
| 23WN747 | E | Coding and Information Theory | 2 | 0 | 1 | 3 |
| 23WN744 | E | Antenna Design and Applications | 2 | 0 | 1 | 3 |
| 23WN733 | E | Distributed Network Algorithms | 2 | 0 | 1 | 3 |
| 23WN736 | E | Network and Application Security | 2 | 0 | 1 | 3 |
| 23WN745 | E | Open RAN Networks | 2 | 0 | 1 | 3 |
| 23MA731 | E | Random Processes and Queueing Models | 2 | 0 | 1 | 3 |
| 23MA734 | E | Computational Optimization | 2 | 0 | 1 | 3 |
| 23MA735 | E | Graph Theory and its Applications in Wireless Networks | 2 | 0 | 1 | 3 |
| Specialization III: Wireless Systems and Application | ||||||
| 23WN742 | E | Distributed Systems | 2 | 0 | 1 | 3 |
| 23WN743 | E | Wireless Local Area Networks | 2 | 0 | 1 | 3 |
| 23WN751 | E | Advanced Embedded Systems | 2 | 0 | 1 | 3 |
| 23WN731 | E | Applied Machine Learning | 2 | 0 | 1 | 3 |
| 23WN735 | E | Edge And Fog Computing | 2 | 0 | 1 | 3 |
| 23WN732 | E | Open Source Networking | 2 | 0 | 1 | 3 |
| 23WN733 | E | Distributed Network Algorithms | 2 | 0 | 1 | 3 |
| 23WN750 | E | Advanced IoT Protocols | 2 | 0 | 1 | 3 |
| 23WN746 | E | Introduction to Block chain and Distributed Ledger Technology | 2 | 0 | 1 | 3 |
| 23WN739 | E | Big Data and Applications | 2 | 0 | 1 | 3 |
| 23WN749 | E | Introduction to Deep Learning | 2 | 0 | 1 | 3 |
| 23MA731 | E | Random Processes and Queueing Models | 2 | 0 | 1 | 3 |
| 23MA734 | E | Computational Optimization | 2 | 0 | 1 | 3 |
| 23MA735 | E | Graph Theory and its Applications in Wireless Networks | 2 | 0 | 1 | 3 |
Duration: Two years
Note:
| Fee for Working Professionals | |||
| Head | Fee | Term | |
|---|---|---|---|
| Tuition Fees | 53000/- | Per Semester | |
| Caution Deposit (Refundable) | 5000/- | One Time | |
| Total | 58000/- | ||
Students graduating from this M.Tech program will be able to demonstrate proficiency in diverse fields, including computer science, communication networks, earth sciences, environmental sciences, disaster management, healthcare, e-governance, VLSI, embedded systems, agriculture, chemical industries, and strategic planning. They can apply their knowledge to address complex problems in these areas and analyse, evaluate, and design effective, efficient, and sustainable systems and solutions.
See Also
Program outcomes are narrower statements that describe what students are expected to know and be able to do by the time of graduation. These relate to the skills, knowledge, and behaviours that students acquire in their matriculation through the program
Terms and Conditions of Scholarship:
Students must pay the regular semester fees of Rs. 79,500/- at the time of admission.
Admissions PG programs & MTech-Wireless Networks & Applications Program Coordinator
At Amrita, companies vie with each other to be the early birds for hiring,
thanks to the quality of students, past and present.
6.8 LPA
5.37 LPA
6.67 LPA
17.5 LPA
My journey with Amrita Wireless Networks and Applications has always been rewarding. I secured my internship due to the well composed multidisciplinary syllabus and a team of knowledgeable and supportive faculty who encouraged me to evolve into a professional.
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