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PhD Defense: RAN Resource Slicing Mechanisms for Multiplexing of Multiple Services in 5G Downlink Wireless Networks

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Conférencier : Praveen Kumar Korrai (SigCom group)
Date de l'événement : mardi 30 novembre 2021 09:30 - 11:00

Join the PhD Defense through this link.


Members of the defense committee:

  • Ass. Prof. Dr. Bhavani Shankar MYSORE RAMA RAO, University of Luxembourg, Chairman
  • Prof. Dr. Antonio G. MARQUES, Universida Rey Juan Carlos, Madrid, Spain, Deputy Chairman
  • Prof. Dr. Symeon CHATZINOTAS, University of Luxembourg, Supervisor
  • Prof. Dr. Nikolaos PAPPAS, Linkoping University, Linköping, Sweden, Member
  • Prof. Dr. Björn OTTERSTEN, University of Luxembourg, Member


The fifth-generation (5G) of wireless networks majorly supports three categories of services, namely, enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive machine-type communications. Every service has its own set of requirements such as higher data rates, lower latency in packet delivery, high reliability, and network energy-efficiency(EE) to support applications including ultra-high definition (UHD) video streaming, virtual reality (VR), autonomous vehicles, vehicular communications, smart farming, and remote-surgery, respectively. The existing one-size-fits-all services network model is not a viable option to support these services with stringent requirements. Therefore, accommodation of these different services on the same physical network while ensuring their distinct QoS requirements is a major challenge. To address this problem, a new concept called network slicing (NS) has emerged as a promising solution for the dynamic allocation of resources to wireless services with different QoS demands. The NS can be performed in both the radio access network (RAN) and core network (CN) parts. In this thesis, we concentrate on RAN resources slicing, and more specifically on challenges that reside in the assignment of limited radio resources to manage the distinct traffic demands occurring from a wide variety of users belonging to heterogeneous services. Specifically, we address the RAN optimization method for joint allocation of time, frequency, and space resources to the eMBB, URLLC, and mMTC users according to their traffic demands (i.e., queue status). Our work in this thesis can be broadly classified into three parts based on the objective function considered in the resource optimization problem: 1) sum-rate maximization, 2) power minimization, 3) EE maximization.   The resulting problems  are mixed-integer non-linear programming problems (MINLPs) which are intractable to solve. To provide solutions to these problems, we first transform the problems into more tractable using the AMC approximation functions, probabilistic to non-probabilistic conversion functions, Big-M theory, and difference of convex (DC) programming. Later, these transformed problems are solved using the successive convex approximation (SCA) based iterative algorithm. In addition, we consider Dinkelbach iterative method to solve the EE maximization problem.

Finally, we compare the performance of the proposed method against baseline schemes through simulation results. In particular, we show the performance of RAN slicing mechanisms with the mixed and fixed-numerology-based RBs grid models in terms of achievable EE, packet latencies, data rates, total sum-rate, and com[1]putational complexity. The proposed algorithm outperforms the baseline schemes in terms of achieving higher data rates for eMBB users. The results also show the trade-off between the total achievable sum rate and EE of the network. The proposed method with mixed numerology grid delivers 20% of higher URLLC packets within 1 ms of latency. Besides, it achieves the lowest computation time than that with the fixed numerology grid.