Resource Scheduling and Cell Association in 5G-V2X

Thesis event information

Date and time of the thesis defence

Place of the thesis defence

IT138, https://oulu.zoom.us/j/62918506825?from=msft

Topic of the dissertation

Resource Scheduling and Cell Association in 5G-V2X

Doctoral candidate

Master of Science Hamza Khan

Faculty and unit

University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Centre for Wireless Communications

Subject of study

Communications Engineering

Opponent

Professor Jussi Kangasharju, University of Helsinki

Custos

Associate Professor Mehdi Bennis, University of Oulu

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Resource Scheduling and Cell Association in 5G-V2X

The fifth-generation (5G) of wireless communication is expected to serve a wide variety of applications with heterogeneous service requirements consisting of enhanced mobile broadband (eMBB), ultra-reliable and low-latency communication (URLLC), and massive machine-type communication (mMTC). Network slicing is instrumental in coping with these diverse set of requirements and service heterogeneity.

The overarching goal of this thesis is to investigate radio resource allocation, focusing on eMBB and URLLC in the context of vehicular networks. This thesis exploits the benefits of network slicing for heterogeneous access in vehicular networks from four perspectives:

(i) development and validation of downlink resource allocation algorithms for vehicular networks with multiple slices and varying quality-of-service (QoS) constraints,

(ii) enhancement of quality-of-experience (QoE) via joint resource allocation and video quality selection in a single-slice vehicular network,

(iii) vehicle cell association and resource allocation for sum rate maximization and signalling overhead minimization in millimeter wave (mmWave) vehicular networks, and

(iv) channel state information inference to reduce the overhead of acquiring channel statistics in vehicular networks and radio resource allocation of multiple slices.

These aspects are studied using analytical tools from stochastic optimization and machine learning, while taking into account vehicular mobility, dynamic network states, and heterogeneous traffic demands. The outcome include resource allocation algorithms in a multisliced vehicular network, QoE enhancement, cell association criterion, and a novel CSI overhead reduction mechanism. The research conducted in this thesis provides key insights into the design and optimization of vehicular communication under the constraints of latency and reliability. The obtained results show significant improvement in terms of QoS/QoE requirements, sum rate improvements, and signaling overhead reductions compared to the current state of the art.
Last updated: 5.10.2020