Mathematical Modelling and Optimization of Programmable 5G Networks

Projektinformationen

MAPNET

ID Finanzhilfevereinbarung: 796378

DOI

10.3030/796378

Projekt abgeschlossen

EK-Unterschriftsdatum 4 April 2018

Startdatum 1 Januar 2019

Enddatum 31 Januar 2021

Finanziert unter

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Gesamtkosten

€ 185 076,00

EU-Beitrag

€ 185 076,00

Koordiniert durch

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
France

Periodic Reporting for period 1 - MAPNET (Mathematical Modelling and Optimization of Programmable 5G Networks)

Berichtszeitraum: 2019-01-01 bis 2020-12-31

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

The exponential increase in mobile data traffic warrants disruptive changes in the design and management of cellular networks. The next generation (5G) networks target to support a variety of applications having diverse requirements in data rates, latency, energy, etc. To address this surge in traffic demand and the associated heterogeneity in service requirements, several candidate technologies are being investigated under the 5G vision, naming a few: 1) Ultra-dense network deployment for high spatial capacity; 2) Network slicing to support heterogeneous service requirements; and 3) Cloud radio access networks (CRAN), etc. An important element of the 5G vision is to achieve a 50% reduction in the total network energy consumption. To ensure that the future networks meet both the service and the sustainability requirements, energy-efficient designs and integration of renewable-energy sources into the network infrastructure are needed. To address the sudden capacity demands, on-demand network deployment is desired, instead of maintaining a permanently over-engineered infrastructure. Inclusion of these approaches result in highly complex and stochastic network topologies. Modeling and optimizing such networks is a challenging problem.

The primary goal of MAPNET is to propose new modelling techniques for 5G-and-beyond networks mainly characterized by a very dense deployment and heterogeneous radio access technologies coupled with the requirements of energy- efficiency and the applications’ quality of service in terms of latency and data rates.

The specific objectives are listed as follows:

(i) Mathematical modelling of ultra-dense networks

(ii )Energy-efficiency maximization of ultra-dense networks

(iii) Network slicing reformulations to satisfy user demands

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

During MAPNET, research has been conducted along four main research axes:

1. Deep reinforcement learning based optimization of ultra-dense Millimeter wave networks
We developed a DRL based framework for power and beamwidth allocation in ultra-dense mmWave deployment.

2. Energy efficiency maximization of ultra-dense Millimeter wave networks
Energy efficiency is a very important metric for ultra-dense mmWave deployments catering for the high data-rate applications. To address this challenge, we defined a novel energy consumption analysis model of mmWave UDNs.

3. Latency and energy Optimization of mobile edge/fog computing systems
We considered a fog/edge computing aided drone communication system where latency and energy consumption optimizations are highly important. We formulated the joint minimization of both service latency and energy consumption as a bi-objective minimization problem.

4. Admission control in radio access network slicing systems
We proposed strategies to avoid frequent switching behavior using the tool of game theory resulting in a more stable tenant behavior and increased profit for the MNOs.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

In MAPNET, the ER got opportunity to work on cutting edge topics in the area of wireless networks and attended many courses on hot-topic related to stochastic geometry, machine learning and artificial intelligence. During the overall stay at the host institute, ER acquired new scientific and transferable skills. The knowledge generated and contribution made towards energy-efficient wireless network will definitely have indirect impact economically and environmentally.

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