For students

Study possibility of implementation of device-to-device communication into cellular networks based on LTE. Analyze selected approaches of the management of mobility for devices communicating with each other directly without involvement of a base station. By means of simulations, compare selected approaches for mobility management from the point of view of the impact on user’s and network’s performance. Based on analysis of the simulation results, propose possible enhancements of the mobility management algorithm and compare it with existing approaches.

• A. Asadi, Qing Wang, V. Mancuso, "A Survey on Device-to-Device Communication in Cellular Networks", vol. 16, no. 4, pp. 1801-1819, 2014.
• Min-Hong Han, Byung-Gook Kim, Jang-Won Lee, "Subchannel and Transmission Mode Scheduling for D2D Communication in OFDMA Networks", Vehicular Technology Conference (VTC Fall), 2012 IEEE, pp. 1-5, 2012.

Study possibility of implementation of device-to-device (D2D) communication into cellular networks based on LTE. Analyze capabilities and limitations of D2D communication for high-speed moving users. Design an algorithm for decision on D2D communication considering mobility of users. Define suitable performance metrics and compare efficiency of the proposed solution with state of the art approaches.

• A. Asadi, Qing Wang, V. Mancuso, "A Survey on Device-to-Device Communication in Cellular Networks", vol. 16, no. 4, pp. 1801-1819, 2014.
• Min-Hong Han, Byung-Gook Kim, Jang-Won Lee, "Subchannel and Transmission Mode Scheduling for D2D Communication in OFDMA Networks", Vehicular Technology Conference (VTC Fall), 2012 IEEE, pp. 1-5, 2012.

Study methods for creation and management of a neighbor cell list in mobile networks. Compare selected methods of neighbor cell list management and ways for its distribution among user equipments. This comparison should consider, among others, complexity of implementation of the algorithms to LTE-A networks. Furthermore, evaluate performance of individual approaches by means of simulations.

• Z. Becvar, P. Mach, M. Vondra, "Self-optimizing neighbor cell list with dynamic threshold for handover purposes in networks with small cells", pp. n/a-n/a, 2013.
• Van Minh Nguyen, H. Claussen, "Efficient self-optimization of neighbour cell lists in macrocellular networks", Personal Indoor and Mobile Radio Communications (PIMRC), 2010 IEEE 21st International Symposium on, pp. 1923-1928, 2010.
• Jung-Min Moon, Dong-Ho Cho, "Efficient scanning algorithm for integrated mobile and nomadic systems: a hierarchical approach", vol. 13, no. 4, pp. 265-267, 2009.

Analyze potential improvement of handover decision process in mobile networks with possibility to exploit prediction of the network status and/or users’ behavior. Compare selected prediction algorithms according to computation complexity a required prediction accuracy. Furthermore, propose extension of current prediction approaches to exploit potential of future mobile networks in terms of possibility to acquire and process big amount of data related to network status and user’s behavior.

• Zdenek Becvar, Pavel Mach, "Mitigation of Redundant Handovers to Femtocells by Estimation of Throughput Gain", IOS Press, vol. 9, no. 4, Amsterdam, The Netherlands, The Netherlands, pp. 315-330, 2013.
• N. Samaan, A. Karmouch, "A mobility prediction architecture based on contextual knowledge and spatial conceptual maps", vol. 4, no. 6, pp. 537-551, 2005.

Study the methods of creation and maintenance of neighbor cell list of macro cell. Propose and analyze possibilities of adaptation of neighbor cell list of macro cell to changes in network. Focus especially on cases of adding and removing of small cells inside the coverage of macro cell. Prove and evaluate the efficiency of proposed approach by using MATLAB simulation.

• M. Vondra, Z. Becvar, "Distance-based Neighborhood Scanning for Handover Purposes in Network with Small Cells", vol. PP, no. 99, pp. 1-1, 2015.
• M. Vondra, Z. Becvar, "Self-configured Neighbor Cell List of macro cells in network with Small Cells", Personal Indoor and Mobile Radio Communications (PIMRC), 2013 IEEE 24th International Symposium on, pp. 3044-3049, 2013.

Study methods for prediction of network’s parameters and user’s behavior in mobile networks. Select the most appropriate methods with respect to their exploitation by admission control process in networks with femtocells. Compare those selected methods and propose an optimization targeting maximization of prediction efficiency. During optimization, consider exploitation of these techniques in admission control procedure. Perform comparison of those methods by simulations.

• Zdenek Becvar, Pavel Mach, "Mitigation of Redundant Handovers to Femtocells by Estimation of Throughput Gain", IOS Press, vol. 9, no. 4, Amsterdam, The Netherlands, The Netherlands, pp. 315-330, 2013.
• N. Samaan, A. Karmouch, "A mobility prediction architecture based on contextual knowledge and spatial conceptual maps", vol. 4, no. 6, pp. 537-551, 2005.

Study the femto-cloud concept and path selection algorithms for data delivery. Analyze existing user movement prediction algorithms and propose algorithm for path selection using mobility prediction. Evaluate efficiency of proposal and success rate of prediction using simulation.

• Z. Becvar, J. Plachy, P. Mach, "Path Selection Using Handover in Mobile Networks with Cloud-enabled Small Cells", IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1480 - 1485, 2014.
• A. Nadembega, A. Hafid, T. Taleb, "A path prediction model to support mobile multimedia streaming", Communications (ICC), 2012 IEEE International Conference on, pp. 2001-2005, 2012.

Study the femto-cloud concept and analyze methods for VM migration during request computation. Compare existing methods and propose an optimization for reduction of results delivery. Perform evaluation of optimization by means of simulation.

• Shiqiang Wang, R. Urgaonkar, Ting He, M. Zafer, K. Chan, K.K. Leung, "Mobility-Induced Service Migration in Mobile Micro-clouds", Military Communications Conference (MILCOM), 2014 IEEE, pp. 835-840, 2014.
• Z. Becvar, J. Plachy, P. Mach, "Path Selection Using Handover in Mobile Networks with Cloud-enabled Small Cells", IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1480 - 1485, 2014.
• A. Nadembega, A. Hafid, T. Taleb, "A path prediction model to support mobile multimedia streaming", Communications (ICC), 2012 IEEE International Conference on, pp. 2001-2005, 2012.

Propose mobility model for simulation of VANETs in MATLAB. Consider map of city environment with orthogonal streets as basis of simulation model. Simulate the movement of vehicles in map based on real movement of vehicles. Focus especially on mutual interaction between vehicles and on situation at the intersections.

• N. Akhtar, S.C. Ergen, O. Ozkasap, "Vehicle Mobility and Communication Channel Models for Realistic and Efficient Highway VANET Simulation", vol. 64, no. 1, pp. 248-262, 2015.
• E. Spaho, L. Barolli, G. Mino, F. Xhafa, V. Kolici, "VANET Simulators: A Survey on Mobility and Routing Protocols", Broadband and Wireless Computing, Communication and Applications (BWCCA), 2011 International Conference on, pp. 1-10, 2011.