Project Topics from Integrated Communication Systems · PDF fileIntegrated Communication...

Research Topics

Prof. Dr.-Ing. habil. Andreas Mitschele-ThielIntegrated Communication Systems Groupwww.tu-ilmenau.de/ics

Project Topics from Integrated Communication Systems Group

Prof. Dr.-Ing. Andreas Mitschele-Thiel

Research Topics


Investigation and Comparison of State-of-the-Art Approaches in Underwater Localization

• Description:Underwater Networks become very interesting for research in the last time.

• Tasks– Literature study on robust algorithms for underwater localization– Selection of two approaches for the comparison– Simulation of the selected approaches (PHP or C++)– Extension of the approaches with the improvement scheme

designed by Dr. Artemenko (optional)– Simulation of the improved scheme (optional)

• References[1] Diba Mirza, Curt Schurgers, and Ryan Kastner. 2012. Real-time collaborative tracking for underwater networked systems. In Proceedings of the Seventh ACM International Conference on Underwater Networks and Systems (WUWNet '12). ACM [2] Diba Mirza , Curt Schurgers, Collaborative tracking in mobile underwater networks, Proceedings of the Fourth ACM International Workshop on UnderWater Networks, p.1-8, November 03-03, 2009, Berkeley, California[3] Artemenko, O.; Mitschele-Thiel, A.; Schorcht, G., "Improvement of localization results in wireless networks using estimation of distances between unknown nodes: Simulation and real testbed evaluation," Personal Indoor and Mobile Radio Communications (PIMRC), 2012 IEEE 23rd International Symposium on , vol., no., pp.693,697, 9-12 Sept. 2012

• Focus2 students theory / programming / hardware / measurements

Responsible Professor:Supervisor:

Prof. Andreas Mitschele-ThielDr.-Ing. Oleksandr Artemenko

Research Topics


Robust Reference Selection Algorithms for Localization in Wireless Networks

• Description:In case of redundant reference data (anchors) available for the localization, smart selection strategy can be applied to provide more accurate localization results. The main idea of this project is to compare different approaches using measurement data collected from UAV missions.

• Tasks– Literature study on approaches for anchor selection– Selection of two approaches for the implementation– Conduct experiments using UAV flying outdoors and netbooks located

indoors (supported by supervisor)– Comparison to the results obtained previously by Dr. Artemenko– Extension of algorithms (optional)

• References[1] Artemenko, O.; Simon, T.; Mitschele-Thiel, A.; Schulz, D.; Ta, R.S., "Comparison of Anchor Selection algorithms for improvement of position estimation during the Wi-Fi localization process in disaster scenario," Local Computer Networks (LCN), 2012 IEEE 37th Conference on , vol., no., pp.44,49, 22-25 Oct. 2012




Research Topics


Bootstrapping Approaches in Cognitive Radio Networks

• Description:The performance of Cognitive Radio Ad Hoc Networks, distributed and cooperative in its nature, is tightly coupled with the existence of a signaling layer, or common control channel (CCC), that is established between neighboring Cognitive Radios (CRs) during the bootstrapping process.

• Tasks– Literature study on bootstrapping approaches– Selection of two approaches for the comparison– Simulation of the selected approaches (C++ under OMNeT++)– Comparison with the results obtained in [1]

• References[1] P. M. R. dos Santos, M. A. Kalil, O. Artemenko, A. Lavrenko, A. Mitschele-Thiel: Self-Organized Common Control Channel Design for Cognitive Radio Ad Hoc Networks , 2013 IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications: Mobile and Wireless Networks (IEEE PIMRC2013), London, UK, September 2013




Research Topics


Analyzing Binary Consensus Algorithm in OMNet++ for Cognitive Radio


Prof. Dr.-Ing. Habil. Andreas Mitschele-ThielMS.CSE. Shah Nawaz Khan

• Description:When multiple nodes operate and learn independently in a distributed manner, they may learn different things about the environment. A distributed consensus algorithm can be used to reduce such differences.

• Tasks– Getting an understanding of crSimulator– Implement and analyse a Binary Consensus Algorithm.

• References– Abdaoui, A.; Elfouly, T.M.; Draief, M., "Distributed binary consensus algorithm and wireless

sensor network," Wireless Communications and Mobile Computing Conference (IWCMC), 2013 9th International , vol., no., pp.1331,1336, 1-5 July 2013

– Online: www.omnetpp.org• Focus

1/2 students Theory & C++ Programming

Research Topics


Simulating Primary User Activity Models in OMNeT++


Prof. Dr.-Ing. Habil. Andreas Mitschele-ThielMS.CSE. Shah Nawaz Khan

• Description:Several generalized/specific models exist in literature for simulating the activities of Primary Users in Cognitive Radio context. The implementation of these models accurately is important for CR networks performance evaluation.

• Tasks– Quick overview of the Primary Users activity models– Implementation and analysis in OMNeT++.

• References– Csurgai-Horvath, L.; Bito, J., "Primary and secondary user activity models for cognitive

wireless network," Telecommunications (ConTEL), Proceedings of the 2011 11th International Conference on , vol., no., pp.301,306, 15-17 June 2011



Research Topics


Implementation ofStackelberg Equlibria in Dynamic Networks

• Description:Game theory is a field of applied mathematics that allows you to formulate an optimisation problem with focus on a a per nodes profit/payoff/utility.

• Tasks– Literature study on formulation of optimsation

as a game– Literature study on mechanisms to compute/

approximate and distribute solutions in thenetwork

– Implementation (knowledge on linear programming necessary (primal/dual))

• References[1] Aote, S. S., and Kharat, M. U. A game-theoretic model for dynamic load balancing in distributed systems. In Proceedings of the International Conference on Advances in Computing, Communication and Control (New York, NY, USA, 2009),

ICAC3 ’09, ACM, pp. 235–238.[2] Roughgarden, T. Stackelberg scheduling strategies. In Proceedings of the Thirty-third Annual ACM Symposium on Theory

of Computing (New York, NY, USA, 2001), STOC ’01, ACM, pp. 104–113.

• Focus1 student theory / programming / discrete mathematics / hardware / measurements


Prof. Andreas Mitschele-ThielManuel Osdoba

Research Topics


Modelling Directional Antennas in OMNeT++ for Cognitive Radio-Enabled Unmanned Aerial Vehicle


Prof. Dr.-Ing. Habil. Andreas Mitschele-ThielM.Sc. Oleksandr Andryeyev

• Description:Directive antenna provides a possibility to transmit a signal in a desired direction, creating no disturbance to neighbour nodes. Therefore, it is promising to use this feature in Cognitive Radio (CR) networks to improve the spectrum efficiency. Our crSimulator (based on OMNeT++) needs to be extended to support directive antennas.

• Tasks– Make a survey on existing directive antenna models– Implement and analyse several models

• References– Christian Doerr, Douglas C. Sicker, and Dirk Grunwald: "What a Cognitive Radio Network

Could Learn From a School of Fish" In Proceeding WICON '07 Proceedings of the 3rd international conference on Wireless internet, Article No. 8 , 22-24 October, 2007.



Research Topics


Q-Learning for combined MRO & MLB

• Description: A Q-learning algorithm learns to concurrently optimize Handover (HO) performance and minimize overload

• Strategy: use the Q-Learning (QL) framework for self-optimization to learn required ‘actions’ to undertake when certain mobility ‘states’ are observed. Then adjust the actions in consideration of the load states and learn the best adjustment

• Major task: Implement MRO+MLB using QL framework. Specific tasks: NOTE: An implementation for MRO exists so should be revised for combined MRO/MLB– A literature survey on HO Optimization and MLB challenges and required actions for optimality – Mapping MRO+MLB (and related optimization) to QL framework– Re-implementing the proposed mapping in the C++ LTE simulator– Undertaking simulations and analysis of the results

• References[1] S. Mwanje, A. Mitschele-Thiel, “Minimizing Handover Performance Degradation due to LTE Self-Organized Load Balancing”, Proceeding of VTC spring 2013, Dresden, Germany.[2] S. Mwanje, A. Mitschele-Thiel, “Q-Learning for LTE Self-Organized Mobility Load balancing”, Proceedings of PIMRC 2013 London, UK

• Focus 1 student theory / programming


Prof. Andreas Mitschele-ThielStephen Mwanje

Research Topics


A demonstrator for SON

• Description: Design and develop an interface for demonstrating SON use cases using as much open source tools as possible

• Major task: Given existing C++ based Implementations of SON functions for Load Balancing, Handover optimization, and Interference management, develop a means of demonstrating their operation preferably in an online manner

• Specific Tasks: – Any required literature survey on SON and related activities – Study existing implementations of the use cases in the C++ LTE simulator– Propose and implement demonstrator – UEs as they move around, radio quality characteristics, network

events e.g. cell overload, SON activity like removal of overload, etc

• References[1] S. Mwanje, A. Mitschele-Thiel, “Minimizing Handover Performance Degradation due to LTE Self-Organized Load Balancing”, Proceeding of VTC spring 2013, Dresden, Germany.[2] S. Mwanje, A. Mitschele-Thiel, “Q-Learning for LTE Self-Organized Mobility Load balancing”, Proceedings of PIMRC 2013 London, UK

• Focus 1/2 student(s) theory / programming


Prof. Andreas Mitschele-ThielStephen Mwanje

Research Topics


Improving Spectrum Access by using Distributed Link Adaptation Algorithms

• Description:One of the main challenges in Cognitive Radio Networks (CRNs) is how to manage the multiple access of different Cognitive Radio (CR) peers into the spectrum band in a distributed way. Another issue is how to adapt the link configuration of each peer in a distributed way without selfish behaviour of spectrum access


Prof. Andreas Mitschele-ThielM.Sc. Ali Haider Mahdi

• Task‐ Literature study of MAC protocol principles, ADPSO[1] , crSimulator[2]‐ Define the radio environment, Primary Users activities, QoS requirements‐ Implement multiple CR peers with ADPSO algorithm on each CR node‐ Implement MAC protocol for multi‐access‐ Investigate the resource access for selfish and unselfish behaviors‐ Study the self‐adaptation of each CR peer to avoid selfish behavior scenario

• Requirements‐ Knowledge of C++, OMNeT++

• Focus 2 students Simulation

[1] http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6364817&tag=1[2] http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6549029&queryText%3Dcrsimulator

Research Topics


Implementing Game Theory Cognitive Radio Networks

• Description:Applying Game Theory in Cognitive Radio Networks (CRNs) has been proposed in literatures. However, no works has been done to manage the spectrum access in MAC layer and link configuration in Physical layer for different CRNs in certain area



• Task‐ Literature study of MAC protocol principles, Game Theory[1] , crSimulator[2]‐ Define the radio environment, QoS requirements‐ Implement Game Theory on each CR node‐ Implement MAC protocol ‐ Investigate the resource access for selfish and unselfish behaviors‐ Study the self‐adaptation of each CR peer to avoid selfish behavior scenario

• Requirements‐ Knowledge of C++, OMNeT++

• Focus 2 students Simulation

[1]http://koti.mbnet.fi/~laitinej/jouni/School/artikkelit/A%20survey%20on%20game%20theory%20applications%20in%20wireless%20networks.pdf[2] http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6549029&queryText%3Dcrsimulator

Research Topics


Learning the Shadowing Effects on Cognitive Radio Networks

• Description:The shadowing has influence on the signal propagation. This influence can be useful in Cognitive Radios (CRs) when it decrease the influence of interference to Primary Users. CRs needs to learn the position and the loss of shadowing, in order to add degree of freedom to the decision process



• Task‐ Literature study of shadowing, propagation models, learning algorithm, and link budget.‐ Define the radio environment, PU activities, and QoS requirements‐ Implement learning algorithm on each CR node‐ Determine the interference on PUs‐ Study the self‐adaptation of each CR peer to avoid selfish behavior scenario

• Requirements‐ Good knowledge of MATLAB

• students Simulation

Research Topics


Implementing AODV Routing Protocol in a Cognitive Radio Ad-hoc Network Using OMNeT++

• Description:Implementation of a simple routing protocol based on the existing cognitive radio simulator in OMNeT++ (Cr-simulator)

• Tasks– A short study on the

• Existing AODV model in INET framework• Cognitive radio simulator (Cr-Simulator)

– Implementing AODV in network layer of Cr-Simulator – Giving Weekly report about the progress of the work– Preparing a document about the implementation

• References- [1] http://www.omnetpp.org/documentation- [2] Khan, S.N., Kalil, M.A., Mitschele-Thiel, A, “crSimulator: A discrete simulation model for cognitive radio ad hoc networks in OMNeT ++”

• Focus2 student Programming


Prof. Andreas Mitschele-ThielMehdi Harounabadi

Research Topics


A Survey on The Application of Machine Learning in Ad-hoc Networks Routing

• Description:Using learning methods in wireless ad-hoc networks routing can improve the performance of the routing protocol. Existing research project is exploring different learning based routing protocols in wireless ad-hoc networks.

• Tasks– A short study on the different classes of routings in wireless ad-hoc networks and machine learning

methods – Literature study on existing learning based routing protocols in different ad-hoc networks

• Mobile ad-hoc networks• Cognitive radio ad-hoc networks• Wireless sensor networks

– Giving Weekly report about the progress of the work– Preparing an structured survey paper

• References- IEEE explore,…

Focus1 student Theory


Prof. Andreas Mitschele-ThielMehdi Harounabadi

Research Topics


• Description:Developing and simulating popular MAC protocols in network simulator.

• Tasks– Literature study on typical traffic scenarios and load patterns

for traffic generation– Simulate 802.11 CSMA protocol under different network scenarios (i.e. #users, traffic)– Develop simple TDMA-MAC component for OMNeT++

• Requirements– Packet-based network simulation– Object-oriented programming in C++

Focus2 students theory / programming / hardware / measurements


Prof. Mitschele-ThielAndre Puschmann

Simulation of MAC Protocol Performance Under Varying Network Conditions using OMNeT++/INET

Research Topics


• Description:Designing and implementing a network simulator using SimPy, a framework for discrete-event simulation in Python programming language.

• Tasks– Literature study/tutorial study on SimPy– Designing appropriate system model for simulator– Build simple CSMA-MAC component + wireless channel + Tx/Rx component– Performance measurements

• Requirements– Packet-based network simulation– Object-oriented programming in Python




Implementation of a Simple Packet-basedNetwork Simulator using SimPy

Research Topics


• Description:Implementing and evaluating spectrum mobility strategies for dynamic spectrum access networks using SimPy, a framework for discrete-event simulation in Python.

• Tasks– Literature study/tutorial study on SimPy– Designing appropriate system model for simulator

•Primary User activity (with varying activity patterns)•Secondary user data transmission Rx->Tx

– Evaluation of spectrum mobility including overhead of negotiation, switching time, etc.

• Requirements– Networking protocols– Object-oriented programming in Python




Simulation of Spectrum Mobility Strategies in Python

Research Topics


• Description:Implementing a distributed protocol for time synchronisation in wireless communication networks.

• Tasks– Literature study time synchronisation protocols (focus on simplicity)– Picking and implementing suitable candidate protocol– No network simulation → Targeted for real Software-Defined-Radio testbed– Evaluation through component tests/unit tests

• Requirements– Networking protocols– Object-oriented programming in C++ (using Boost libraries)




Implementation of a Distributed Time Synchronisation Protocol in C++

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