Open Thesis

Optimal and Proactive Communication Resource Allocation

Stichworte:
LiFi, Multipath, Optimization, Task Offloading

Beschreibung

The goal of the thesis would be to build an Anticipatory or Proactive Wireless Resource Allocation Framework to optimize Multi-hop, Multi-path networks.

The approach is to develop and solve an optimization problem to allocate network resources to users by looking into window of time in the future. By knowing the channel quality of the users in the future, a better, more optimal allocation of resources is made possible.

Related Reading:

Dastgheib, Mohammad Amir, et al. "Mobility-aware resource allocation in VLC networks using T-step look-ahead policy." Journal of Lightwave Technology 36.23 (2018): 5358-5370.

If you are interested in this work, please send me an email with a short introduction of yourself along with your CV and grade transcript.

 

Voraussetzungen

  • Strong Python programming skills
  • Strong foundation on wireless communications
  • Experience with optimization problems

 

Kontakt

hansini.vijayaraghavan@tum.de

Betreuer:

Task offloading with Multi-Agent Reinforcement Learning

Stichworte:
LiFi, Multipath, Reinforcement Learning, Task Offloading

Beschreibung

The goal of the thesis would be to build a Wireless Resource Allocation Framework to optimize Multi-hop, Multi-path networks.

The approach is to develop an optimization problem to allocate network resources to users for task offloading and to solve this problem using Multi-agent Reinforcement Learning.

Related Reading:

Z. Cao, P. Zhou, R. Li, S. Huang and D. Wu, "Multiagent Deep Reinforcement Learning for Joint Multichannel Access and Task Offloading of Mobile-Edge Computing in Industry 4.0," in IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6201-6213, July 2020, doi: 10.1109/JIOT.2020.2968951.

If you are interested in this work, please send me an email with a short introduction of yourself along with your CV and grade transcript.

 

Voraussetzungen

  • Strong Python programming skills
  • Strong foundation on wireless communications
  • Experience with Reinforcement Learning

 

Kontakt

hansini.vijayaraghavan@tum.de

Betreuer:

Ongoing Thesis

Masterarbeiten

Reinforcement Learning based Resource Allocation in a multi-hop multi-path heterogeneous LiFi-WiFi Network

Stichworte:
LiFi, Multipath, Reinforcement Learning, Task Offloading

Beschreibung

The goal of the thesis would be to build a Wireless Resource Allocation Framework to optimize Multi-hop, Multi-path networks.

The approach is to develop an optimization problem to allocate wireless and compute resources to users and to solve this problem using Reinforcement Learning.

 

Voraussetzungen

  • Strong Python programming skills
  • Strong foundation on wireless communications
  • Experience with Reinforcement Learning

 

Kontakt

hansini.vijayaraghavan@tum.de

Betreuer:

Interdisziplinäre Projekte

Evaluation of the MPTCP Scheduler LATE for Heterogeneous Wireless Networks

Stichworte:
LiFi, Multipath, Optimization, Scheduling

Beschreibung

In order to fully utilize the capabilities of a LiFi-RF Heterogeneous network, the client devices should be capable of using multiple network interfaces simultaneously. Thanks to multipath solutions like MPTCP, this is possible. 

The challenge in a MPTCP-enabled heterogeneous network lies in designing a policy to schedule data packets onto the multiple paths with heterogeneous characteristics (eg. delay, packet loss).

This work involves

  • Designing an MPTCP scheduler that schedules packets optimally to minimize network delay and handle the dynamicity of heterogeneous links
  • Implementing the scheduler in the Linux kernel
  • Performing extensive evaluations with Mininet and hardware

Related Reading:Yang, Wenjun, et al. "Loss-aware throughput estimation scheduler for multi-path TCP in heterogeneous wireless networks." IEEE Transactions on Wireless Communications 20.5 (2021): 3336-3349.

If you are interested in this work, please send an email with a short introduction of yourself along with your CV and grade transcript.

 

Voraussetzungen

  • Strong Python and C++ programming skills
  • Experience with optimization problems
  • Experience with Linux networking

 

Kontakt

hansini.vijayaraghavan@tum.de

Betreuer:

Forschungspraxis (Research Internships)

Reactive Resource Allocation with Reinforcement Learning to minimize latency in LiFi-WiFi Networks

Stichworte:
LiFi, Reinforcement Learning

Beschreibung

The goal of the thesis would be to build an Anticipatory or Proactive Wireless Resource Allocation Framework to optimize Multi-path networks.

The approach is to develop an optimization problem to allocate network resources to users by looking into window of time in the future and to solve this problem using Reinforcement Learning.

By knowing the channel quality of the users in the future, a better, more optimal allocation of resources is made possible.

Related Reading:

Chen, Weixi, et al. "Proactive 3C Resource Allocation for Wireless Virtual Reality Using Deep Reinforcement Learning." 2021 IEEE Global Communications Conference (GLOBECOM). IEEE, 2021

If you are interested in this work, please send me an email with a short introduction of yourself along with your CV and grade transcript.

 

Voraussetzungen

  • Strong Python programming skills
  • Strong foundation on wireless communications
  • Experience with Reinforcement Learning

 

Kontakt

hansini.vijayaraghavan@tum.de

Betreuer: