Latency Minimization Techniques in Dense Urban 5G Mobile Networks
Shubhashish GoswamiSchool of Engineering & Computing, Dev Bhoomi Uttarakhand University socse.shubhashish@dbuu.ac.in0000-0002-6129-9822
K. Shashi RajAssistant Professor, Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering shashiraj18@gmail.com0000-0002-5946-9044
Dr. Vinod B DurdiDepartment of Electronics & Telecommunication Engineering, Dayananda Sagar College of Engineering, vinoddurdi-tce@dayanandasagar.edu0000-0001-5788-7341
T. Harisha NaikAssociate Professor, Department of Computer Applications (DCA), Presidency College harishnaik-coll@presidency.edu.in0009-0006-3970-7486
M.P. SunilAssistant Professor, Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University) mp.sunil@jainuniversity.ac.in0000-0002-7737-4145
Kalpana K HarishAsisstant Professor, Department of Computer Science Engineering, Presidency University kalpana.k@presidencyuniversity.in0009-0004-5741-8210
Dr. Shatarupa DashAssistant Professor, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University) shatarupadash@soa.ac.in0000-0003-1605-0777
Keywords: Latency Minimization, 5G Mobile Networks, Dense Urban Environments, Edge Computing, Network Slicing, Ultra-Reliable Low-Latency Communication (URLLC), Small Cells, Wave, Dynamic Resource Allocation, Intelligent Handover.
Abstract
The advent of 5G mobile networks in environments with ultra-high density (e.g. city centres) poses unique challenges related to latency, arising from three main areas: user density, multipath propagation and limitations associated with supporting infrastructure. This paper provides a complete review of advanced latency reduction strategies tailored toward ultra-high density 5G environments. Key strategies that have been examined include; Edge computing, dynamic resource scheduling, slicing and intelligent handover techniques using machine learning. The study evaluated the performance of each latency reduction technique in effectiveness in reducing end-to-end latency considering a range of urban density scenarios); and used simulation-based performance. The contribution made by ultra-dense small cell deployments and millimetre-wave (Wave) communication towards improving latency performance has also been evaluated. Often hybrid methods combining the edge processing with real-time adaptive radio resource management produced overall measurable latency benefits matching ultra-reliable low-latency communication (URLLC) requirements. Finally the paper provides an outline of recommendations and implications to network operatives as well as future research pathways towards the development of real-time adaptive 5G systems within complex urban topologies.