M.Tech. Wireless Networks & Applications (Specialising in IoT, AI, 5G, Blockchain)

Future-proof your Career with Our MTech Program and Stay Ahead of the Curve

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.

CurriculumYear-wise Curriculum & Syllabus

Semester 1 Semester 2 Semester 3 Semester 4 Foundation Core Subject Core Electives Project Work Specialization

Semester 1

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

Semester 2

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

Semester 3

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

Semester 4

Course Code	Type	Course	L T P	Credit

23WN799	P	Dissertation – Phase II		16
		Credits	16

Total Credits: 69

Foundation Core

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

Subject Core

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

Electives

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

Project Work

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

Specialization

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

Admissions

Duration: Two years

Eligibility Criteria

B.Tech. or B.E. in (Applied Electronics and Instrumentation, Computer Engineering, Computer Science and Engineering, Electrical and Electronics Engineering, Electrical Engineering, Electronics and Communication Engineering, Electronics and Instrumentation Engineering, Electronics Engineering, Information Science and Engineering, Information Technology, Robotics Engineering, Software Engineering, Telecommunication Engineering)
B.Tech. in (Cloud Computing & Virtualization Technology, Computer and Information Science, Electronics and Media Technology, Electronics and Nanotechnology, Electronics and Telecom Engineering, Information and Communication Technology, Mathematics and Computing, Power Engineering, Power System Engineering, Power Electronics)
M.Sc. in (Applications of Mathematics, Applied Mathematics and Computing, Computer Applications, Computer Communication, Computer Science, Computer Technology, Data Mining and Warehousing, Information and Communication Technology, Information Science, Information Security and Cyber Forensics, Information Technology, Information Technology and Management, Instrumentation Technology, Software Engineering, Software System, System Administration & Networking)
MCA

Program Overview

Learning Objectives

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

About Amrita WNA

Amrita WNA Facility

Amrita WNA Faculty

Amrita WNA Alumni

Programme Outcomes

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

PO1: Engineering Knowledge
PO2: Problem Analysis
PO3: Design/Development of Solutions
PO4: Conduct Investigations of complex problems
PO5: Modern tools usage
PO6: Engineer and Society
PO7: Environment and Sustainability
PO8: Ethics
PO9: Individual & Team work
PO10: Communication
PO11: Project management & Finance
PO12: Lifelong learning

PSO-MTech WNA

Gain knowledge on Critical review of research papers & develop technical writing skills
Modern tools usage: Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations.
Capability to think Innovatively and creatively in solving real-life challenges
Communication efficacy: Communicate effectively with the research community, and with society at large.
Develop awareness on social challenges and involve actively in providing affordable, sustainable and environment-friendly solutions for rural communities.
Develop skill sets to work in multidisciplinary areas/projects and diverse groups, national and international mission projects.
Research development opportunities by collaborating with international experts and get exposure to state of the art technologies for ready absorption in core industries and research institution

Thrust Areas in Research

Mobile Communication and Networks
Internet of Things
Machine Learning
Wireless Communications
5G and 6G Communication
Blockchain Technology, Smart Contracts and Distributed Ledger Technology
Augmented Reality and Virtual Reality

Downloads

Curriculum & Syllabus 2023

Curriculum

Scholarship

Terms and Conditions of Scholarship:

Students must pay the regular semester fees of Rs. 79,500/- at the time of admission.

Slab 1 -Students must have secured a CGPA of 8.0 or above in their B Tech course to be considered for this scholarship slab.
Scholarship will be paid monthly @ Rs.5,000 per month for 5 months in a semester. Students will need to pay the full amount of semester fees up front.
From Semester 2 onwards, the scholarship will be based on their M Tech CGPA in the previous semesters.
If the CGPA is between 7.0 and 8.0 for slab 1 students, the scholarship amount will be decreased accordingly going forward.
If the CGPA falls below 7.0 or if there are any arrears, the scholarship will be discontinued.

Slab 2 -Students must have secured a CGPA of 7.0 or above in their B Tech course to be considered for this scholarship slab.
Scholarship will be paid monthly @ Rs.2, 500 per month for 5 months in a semester. Students will need to pay the full amount of semester fees up front.
From Semester 2 onwards, the scholarship will be based on their M Tech CGPA in the previous semesters.
If the CGPA rises to 8.0 or above, the scholarship amount will be increased to slab 1 level.
If the CGPA falls below 7.0 or if there are any arrears, the scholarship will be discontinued.

Scholarship Rules