Ongoing Thesis

Masterarbeiten

Exploring Multi-Link Operation (MLO) for Reliable Transmissions in Wi-Fi 7 and Beyond

Beschreibung

How to efficiently allocate industrial automation traffic to multiple links in MLO to meet the latency requirements?

  • Implementation of State-of-the-Art policies for our Industrial Automation Application using NS3.
  • Analyzing their effect and coming up with a novel policy/classification based on our traffic type.

Betreuer:

Alba Jano, Hansini Vijayaraghavan - Ben Schneider (Siemens)

Proactive load-aware wireless resource allocation for sustainable 6G network

Beschreibung

The rapid growth of traffic and the number of connected devices in the 5G and beyond wireless networks focus the attention on sustainability in the radio access network (RAN). Traffic load and status of available wireless resources in the network change rapidly, especially in scenarios with a large number of connections and high mobility. The high connectivity is caused by exponentially increasing Internet of Things (IoT) devices connected to the network for supporting various use cases ranging from Industry 4.0 to healthcare.

IoT devices mainly powered by batteries are characterized by low cost, low complexity, and limited computational resources. Therefore, elongating their lifetime while fulfilling the quality of service (QoS) requirements poses a new research challenge. To tackle this problem, context awareness of devices consisting of device type and mobility; and the network traffic load simultaneously enhance the wireless resource management and the management of the devices states. Moreover, to enable awareness of the neighboring cells, the predicted information on the traffic load can be exchanged among cells. The above affects the decisions of accepting the device or offloading it to the neighboring cell and the device's operating state.

In this thesis, the student will focus on developing and testing a context-aware resource allocation mechanism based on device mobility and traffic load, focusing on decreasing individual devices' energy consumption and reducing processing latency.

Voraussetzungen

  • Good knowledge of Python and Matlab programming.
  • Good mathematical background.
  • Knowledge of mobile networks.

Kontakt

alba.jano@tum.de

Betreuer:

Alba Jano

Interdisziplinäre Projekte

Joint radio and computing resource allocation using artificial intelligence algorithms

Beschreibung

Mobile Edge Computing (MEC) enabled 6G network support low latency applications running in energy-constrained and computational limited devices, especially IoT devices. Using the task offloading concept, the devices offload the incoming tasks fully or partially to MEC depending on the device and network side's communication and computation resource availability.

The 6G networks are oriented towards the Digital Twins (DT); therefore, the resource allocation and offloading decisions are enhanced with the context-awareness of the devices, environment, and network. The device context awareness consists of battery state, power consumption, CPU load, and traffic type. Further, the environmental context-awareness includes the position of the network components, the mobility patterns, and the quality of the wireless channel and the availability of the network resources.

 

In this project, the student will focus on developing and testing an artificial intelligence algorithm for joint allocating of computing and radio resources in a predictive manner, focusing on decreasing individual devices' energy consumption and reducing processing latency.

Tasks

 

  • Work with a 6G radio access network simulator, to generate the database for the scenario with devices having high energy efficiency and low task processing latency requirements.
  • Develop a reinforcement learning algorithm for joint allocation of radio and computing resource allocation.
  • Comparing the developed model with the state-of-the-art approaches.
  • Test and documentation.

 

Voraussetzungen

 

  • Good knowledge of Python programming.
  • Good mathematical background.
  • Good knowledge of deep learning/reinforcement learning.

Betreuer:

Alba Jano

Forschungspraxis (Research Internships)

Evaluation of traffic model impact on a context-aware power consumption model of user equipment

Stichworte:
5G, IIoT, energy, efficiency

Beschreibung

Energy efficiency is one of the key performance requirements in the 5G network to ensure user experience. A portion of devices, especially the Industrial Internet of Things (IIoT), run on limited energy, supported by the batteries not placed over the lifetime.

Therefore, the estimation of the power consumption and battery lifetime has recently received increased attention. Multiple context parameters, such as mobility and traffic arrivals, impact the device's power consumption.

In this thesis, the student shall focus on analysing the impact of different traffic models on the power consumption of user equipment. Different source and aggregated traffic models will be implemented depending on the number of devices n the scenario. The implemented traffic models will be evaluated based on a context-aware power consumption model for the user equipment.

Voraussetzungen

  • Good knowledge of Python and Matlab programming.
  • Good mathematical background.
  • Knowledge mobile networks.

Betreuer:

Alba Jano

Studentische Hilfskräfte

Working Student for Analysis, Modeling and Simulation of Communication Networks SS2023

Beschreibung

The main responsibilities of a working student include assistance to tutors in the correction of the programming assignments, as well as answering the questions in Moodle.  Working time is 6-7 hours per week in the period from May to July.

Voraussetzungen

  • Python knowledge

Kontakt

polina.kutsevol@tum.de

Betreuer:

Alba Jano, Polina Kutsevol

Working Student for Testbed on 5G/6G RAN

Beschreibung

The results expected from this work are the enhancement of the 5G/6G tested setup with additional features on the Radio Access Network (RAN) and Core Network (CN). The work is focused on the OpenAirInterface (OAI) [1] platform, which forms the basis of the testbed setup. The expected outcome is to have improvements in wireless resource scheduling, focused on the uplink (UL), power management, and core network function management. 

[1] N.Nikaein, M.K. Marina, S. Manickam, A.Dawson, R. Knopp and C.Bonnet, “OpenAirInterface: A flexible platform for 5G research,” ACM SIGCOMM Computer Communication Review, vol. 44, no. 5, 2014.

Voraussetzungen

  • Good C/C++ experience
  • Good Python knowledge
  • RAN and CN architecture understanding is a plus

Kontakt

alba.jano@tum.de, yash.deshpande@tum.de

Betreuer:

Alba Jano, Yash Deshpande