Disaggregated optical access network planning ILP vs ML
ML, ILP, optical access networks, PON, disaggregated networks
Description
The increase in bandwidth requirements triggered by new services and much more terminals force network providers to upgrade their networks constantly. Obviously, the upgrade takes cost into account, but it should also consider bandwidth, delay, reliability, and security.
This master thesis will aim at modeling, implementing, and evaluating different access network architectures with a network planning tool. For that purpose, different tasks will be considered:
• Define different network architectures based on the state of the art: combining PON and aggregation networks, considering dependabilities, etc.
• Learn and select the best data and planning tool: ArcGIS, Gabriel Graphs, etc.
• Implement planning solution: component placement, fiber/cable layout, etc. using ILP
• Implement planning solution: component placement, fiber/cable layout, etc. using ML
• Evaluate and compare the availability and cost of different architectures.
Example of previous work
Shahid, Arslan; Mas Machuca, Carmen: Dimensioning and Assessment of Protected Converged Optical Access Networks. IEEE Communications Magazine Vol. 55, No. 8, 2017
Prerequisites
Python, ML, and ILP formulation.
Contact
PD Dr.-Ing. habil. Carmen Mas Machuca
cmas@tum.de
Supervisor:
Machine-learning-based network planning for the future railway communications
Network Planning, On-Train Data Communications. Machine Learning
Exploration of mechanisms for handling data communications under the influence of mobility in the German long distance railway system.
Description
This work focuses on the exploration of networks enabling train control and on-board data communications under mobility scenarios. Today, low bandwidth networks such as GSM, providing less than 200 Kbps are being used to transmit train control information. Moreover, despite trains may use multiple on-board technologies to provide users with an internet connection (e.g., repeaters, access points), they fail in their attempt as these connections are characterized by having low throughput (less than 2 Mbps) and frequent service interruptions.
This work aims at the development of a network planning solution enabling future applications in train mobility scenarios such as: Automatic Train Operation (ATO) [1,2], leveraging cloud technologies and meeting bandwidth requirements of data-hungry end-users' applications. Here, special attention will be given to the migration of communications services triggered by trains mobility patterns. It is expected of the student to find solutions to the following questions:
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When to trigger service migrations?
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Where to migrate services? (i.e., to which data center)
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How to handle this process? (So that the user does not perceive any interruption)
Given:
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Trains mobility patterns
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Service requirements in terms of bandwidth and delay
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Network topology
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Data center locations
The results from this work can be useful to get an insight on requirements for Smart Transportation Systems, that may in turn be useful for cementing the basis of other scenarios such as: Autonomous Driving and Tele-Operated Driving.
[1] Digitale Schiene Deutschland. Last visit on 13.12.2021 https://digitale-schiene-deutschland.de/FRMCS-5G-Datenkommunikation
[2] 5G-Rail FRMCS. Last visit on 13.12.2021 https://5grail.eu/frmcs/
Prerequisites
Basic knowledge in:
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Integer Linear Programming (ILP), heuristics or Machine Learning (ML).
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Python
Please send your CV and transcript of records.
Contact
Supervisor:
Student Assistant for the Internetkommunikation Lecture in SoSe23
Description
Internetkommunikation offers the opportunity to develop interesting software solutions for technical questions about internet protocols and mechanisms. For the next semester, a position is available to assist the teaching assistants for the tutorials and class project.
Prerequisites
- Knowledge in computer networking
- Good programming skills in Python
Supervisor:
Working Student for Analysis, Modeling and Simulation of Communication Networks SS2023
Description
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.
Prerequisites
- Python knowledge
Contact
polina.kutsevol@tum.de
Supervisor:
Alpha-fair Mobility Management in 5G Networks using Deep Reinforcement Learning
Description
Mobility management in 5G is challenging due to the usage of higher frequencies and blockages of line of sight signal. Moreover, a much higher number of cells is needed at higher frequencies to provide similar coverage as in 4G. As a result, the base stations are placed much more densely, and the users experience frequent handovers, which reduce the network capacity. The 5G baseline handover leads to frequent unnecessary handovers that drastically increase the signaling. Moreover, selecting the serving BS for a user is not trivial since users are often located in the coverage of multiple BSs. Therefore, advanced handover techniques are needed in 5G to perform smooth network operation.
In this thesis, an optimization problem to provide alpha-fairness in data rates among the users and reduce the handover rate will be considered. This is an NP-hard problem, so we will relax it to obtain an upper bound. Then, a Deep Reinforcement Learning (DRL)-based algorithm will be developed, which finds a near-optimal user-to-BS assignment and resource allocation. Finally, extensive simulations with varied parameters should be performed to evaluate the algorithms using a Python-based simulator.
Prerequisites
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Good knowledge of Python
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Interest to learn about mobility management in 5G
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Motivation to produce results that might go into a paper
Supervisor:
Operator-revenue Maximization in Beyond-5G Cellular Networks
Description
Starting with 5G, assigning portions of network resources depending on the use case, where users running the same application/service would be getting resources from the same resource pool, a process known as network slicing, introduced a paradigm shift in how cellular networks operate in general. This brought significant advantages to both users and operators, by easing the resource allocation process and improving performance.
In 5G, users can be categorized, based on the service they are running, into three broad groups: eMBB, URLLC, and mMTC. There would be a separate Radio Access Networks (RAN) slice for each group. So, all eMBB users within the same cell would receive their resources from the same slice. URLLC service type is characterized by the most stringent traffic requirements. So, different amount of resources are needed to enable a satisfying user experience for different types of services. Hence, the problem of proper slice dimensioning arises as a very important issue that needs to be addressed.
Besides splitting RAN resources (slices), given that in-network computing will become an inevitable part of next generation of cellular networks, splitting computing resources would be very important. The computing resources come from edge clouds, which are collocated with base stations. Also, users have different channel conditions, which needs to be taken into account. For each admitted user, the traffic requirement would have to be satisfied.
Therefore, the operator would need to decide how to slice the RAN resources, and given the available computing resources, it would need to decide on the number of users of each service type to admit so that its overall revenue is maximized. The candidate needs to formulate an optimization problem. Then, different static and dynamic policies are to be analyzed in order to determine the best one, depending on the computational complexity introduced and the achievable performance in terms of the objective of the optimization problem.
Prerequisites
A good knowledge of any programming language is required.
Supervisor:
Processing Priorization in the Medical Context
Description
In future communication systems such as 6G, in-network computing will play a crucial role. In particular, processing units within the network enable to run applications such as digital twins close to the end user, leading to lower latencies and overall better performance. However, these processing resources are usually shared among many applications, which potentially leads to worse performance in terms of execution time, throughput, etc. . This is especially critical for applications such as autonomous driving, telemedicine or smart operations. Hence, the processing of more critical applications must be prioritized.
In this thesis, the task is to develop and evaluate a priorization approach for applications. However, not only technical aspects will play a role for the priorization, but also ethical, i.e. in this case medical aspects. This is especially important, if applications are equally critical. For this, suitable real use cases are identified together with our partners at MITI (Hospital "Rechts der Isar"). The priorization approach then leads to a specified distribution of the processing and networking resources, satisfying the minimum needs of critical applications.
The result will be an evaluated priorization approach for applications in the medical environment.
Prerequisites
Motivation, basic networking knowledge, basic programming knowledge
Contact
nicolai.kroeger@tum.de
Supervisor:
Mobility Management for Computation-Intensive Tasks in Cellular Networks with SD-RAN
Description
In the previous generations of cellular networks, both data plane and control plane operations were conducted jointly in Radio Access Networks (RANs). With the emergence of Software Defined Networks (SDNs), and their adaptation in RANs, known as SD-RAN, for the first time the separation of control from data plane operations became possible in 5G RAN, as a paradigm shift on how the assignment of network resources is handled in particular, and how cellular networks operate in general. The control is shifted to centralized units, which are known as SD-RAN controllers. This brings considerable benefits into the cellular network because it detaches the monolithic RAN control and enables co-operation among different RAN components, i.e., Base Stations (BSs), improving this way network performance along multiple dimensions. Depending on the current spread of users (UEs) across BSs, and their channel conditions for which UEs periodically update their serving BSs, and BSs forward that information to the SD-RAN controller, the latter can reallocate resources to BSs accordingly. BSs then perform the resource allocation across their corresponding UEs. Consequently, exploiting the wide network knowledge leads to an overall improved performance as it allows for optimal allocation decisions.
This increased level of flexibility, which arises from having a broader view of the network, can be exploited in improving the mobility management in cellular networks. This comes into play even more with 6G networks in which in-network computing is envisioned to be integral part. Namely, users will be sending computationally-intensive tasks to edge clouds (through their BSs) and would be waiting some results as a response. However, as it will take some time until these tasks are run on the cloud, the user might be changing the serving BS. As a result, handover will have to be managed. However, while the task is being uploaded, performing handovers would not be good as then the task would need to be sent to another edge cloud. Consequently, having a centralized knowledge of all the network (which the SD-RAN controller has), to avoid frequent handovers, the controller has an extra degree of freedom by increasing the number of frequency blocks that can be assigned to a user while uploading the task and while downloading results.
In this thesis the goal would be to increase the overall network utility by deciding which tasks to serve (each task has its own utility), given the limited network resources in terms of the upload bandwidth, download bandwidth, storage in edge clouds, and finite computational capacity. Users besides sending tasks and receiving results are assumed to run other applications, with given service requirements. The student will formulate optimization problems and solve them either analytically or using an optimization solver, like Gurobi, CVX, etc. The other task would be to conductt realistic simulations and showing the advantages the developed algorithms offer against benchmarks.
Prerequisites
Good knowledge of Python and interest to learn about mobility management in 5G
Supervisor:
Optimal resource allocation for utility maximization in 5G networks
Description
The slice dimensioning for the three types of traffic in 5G(eMBB, URLLC, and mMTC) would be the focus of this thesis. Each user, depending on the type of traffic, is characterized by a weight. This could be e.g., the gain of the operator by serving a given user. We assume that the more stringent the traffic requirement is, the higher the gain for the operator is. In this way, mMTC users weight would be the lowest, whereas the URRLC’s the highest. Also, users have different channel conditions, which needs to be taken into account. For each admitted user, the traffic requirement would have to be satisfied. The problem would then reduce to deciding what sizes of network slices are to be allocated to each service type, and the corresponding number of users within the slice, so that the utility for the operator is maximized.
Three policies are to be analyzed. The first is motivated by the finite number of PhysicalResource Blocks (PRBs) each cell has, so that a brute-force solution is found. The computational complexity of this policy has to be obtained as well. The second, less complex policy, is the one in which resources are reserved beforehand (i.e., it is a static policy), based depending on the ratio between the weight and the resources needed for a user of the given service type. Finally, the third policy, which would be based on a heuristic, would decide onthefly on how to dimension RAN slice sizes.
Prerequisites
A good knowledge of any programming language is required.
Contact
fidan.mehmeti@tum.de
Supervisor:
Enhanced Mobility Management in 5G Networks with SD-RAN
Description
In pre-5G networks, both the data plane and control plane operations were performed jointly in Radio Access Networks (RANs). With the emergence of Software Defined Networks (SDNs), and its adaptation in RANs, known as SD-RAN, for the first time the separation of control from data plane became possible 5G RAN, as a paradigm shift on how the assignment of network resources is handled in particular, and how cellular networks operate in general. The control is transferred to centralized units, which are known as SD-RAN controllers. This brings considerable benefits into the mobile network since it detaches the monolithic RAN control and enables co-operation among different RAN components, i.e., Base Stations (BSs), improving the network performance along several dimensions. To that end, depending on the current spread of the users (UEs) across BSs, and their channel conditions for which the UEs periodically update their serving BSs, and BSs send that information to the SD-RAN controller, the latter can reallocate resources to BSs accordingly. BSs then perform the resource allocation across their corresponding UEs. As a consequence, exploiting the wide network knowledge leads to an overall improved performance as it allows for optimal allocation decisions.
This increased level of flexibility, which arises from having a broader view of the network, can be exploited in improving the mobility management in cellular networks. In the previous generations of cellular networks, each BS has its own set of frequencies at which it could transmit. Given that each user would receive service by only one BS, depending on the channel conditions the users would have with the serving BS and the number of users within the same sell, the user would decide on whether she would need a handover or it would be better to remain within the same serving area (i.e., receiving service from the same BS) . Currently, conditional handovers are being the most serious candidate for 5G. However, every handover involves a considerable cost, due to the preparations that need to be performed to hand a user over from one BS to another one. These will unavoidably lead to reductions in data rates and network resources for other users. On the other hand, having a centralized knowledge of all the network (which the SD-RAN controller has), to avoid frequent handovers, the controller has an extra degree of freedom by increasing the number of frequency blocks that can be assigned to a user experiencing bad channel conditions. This of course depends on the topology of the users in that moment.
In this thesis, the focus will be on jointly deciding on the resource allocation policy for each user across the entire area of the controller and when to perform the handover in order to optimize different performance aspects (e.g., provide proportional fairness). To that end, the student will formulate optimization problems and solve them either analytically or using an optimization solver, like Gurobi, CVX, etc. The other part would be conducting realistic simulations and showing the advantages the developed algorithms offer against state of the art.
Prerequisites
Good knowledge of Python and interest to learn about mobility management in 5G
Supervisor:
Joint power and PRB allocation in SD-RAN environments in Beyond-5G networks
5G NR, SD-RAN, joint optimization
Description
In the previous generations of cellular networks, in Radio Access Networks (RANs) both the data plane and control plane operations were performed jointly. With the emergence of Software Defined Networks (SDNs), and its adaptation in RANs, known as SD-RAN, the separation of control from data plane became possible for the first time in RANs of 5G networks, as a paradigm shift on how the assignment of network resources is handled in particular, and how cellular networks operate in general. The control is transferred to centralized units known as SD-RAN controllers. This brings a lot of benefits into the mobile network since it detaches the monolithic RAN control and enables co-operation among RAN components, i.e., Base Stations (BSs), improving the network performance along different dimensions.
This increased level of flexibility arises from having a broader view of the network, which is provided by the centralized SD-RAN approach. In that way, depending on the current spread of the users (UEs) across BSs, and their channel conditions for which the UEs periodically update their serving BSs, and BSs send that information to the SD-RAN controller, the latter can reallocate resources to BSs accordingly. BSs then perform the resource allocation across their corresponding UEs. As a consequence, exploiting the wide network knowledge leads to an overall improved performance as it allows for optimal allocation decisions. As opposed to SD-RAN, in a classical RAN approach, each BS has its own fixed set of resources, and allocates them to the UEs within its operational area.
So far, the research in SD-RAN has focused only on allocating the resource blocks (i.e., frequencies) adaptively to the BSs. In this thesis, the focus will be on the joint allocation of both the resource blocks and transmission power to BSs within the area of the controller, in order to optimize different performance aspects. To that end, the student will formulate optimization problems and solve them either analytically or using an optimization solver, like Gurobi, CVX, etc. The other part would be conducting measurements for different allocation policies in OperAirInterface.
Prerequisites
- Good C/C++ experience
- Knowledge on OFDMA
Contact
serkut.ayvasik@tum.de
fidan.mehmeti@tum.de
Supervisor:
Optimal and Proactive Communication Resource Allocation
LiFi, Multipath, Optimization, Task Offloading
Description
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.
Prerequisites
- Strong Python programming skills
- Strong foundation on wireless communications
- Experience with optimization problems
Contact
hansini.vijayaraghavan@tum.de
Supervisor:
Modeling and implementing a simulator for multi-domain wireless networks
Description
Emerging applications such as telemedicine put stringent requirements on the underlying communication network. Furthermore, communication is expected to happen also across different domains. As this cannot be fulfilled easily and efficiently, the new communication network generation (6G) is currently being researched. 6G follows a holistic networking approach, i.e., not only across individual domains, but also across the entire network, with the focus to provide end-to-end performance guarantees. The overall network consists of several network types, different in the used devices and technologies (such as molecular networks, quantum networks, satellite networks, campus networks, etc.)
In order to develop new concepts and estimate their performance, it is essential to see practical results. However, obtaining measurement results from a complete testbed setup for every case is infeasible. In comparison, using a simulation allows to vary a broad variety of parameters such as the topology or device parameters and still achieve results in a reasonable amount of time. Another important aspect is performing analytic modeling.
The goal in this Master Thesis is to implement a packet-based simulator (preferably) in C/C++ and evaluate the functionality of multi-domain networks. Also, the achievable performance guarantees should be provided.
Supervisor:
Working Student for Testbed on 5G/6G RAN
Description
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.
Prerequisites
- Good C/C++ experience
- Good Python knowledge
- RAN and CN architecture understanding is a plus
Contact
alba.jano@tum.de, yash.deshpande@tum.de
Supervisor:
Dimensioning a German Train IP-Optical Network with Integer Linear Programming
ILP, optical communicaitons
This thesis consists in modeling and solving the dimensioning problem for a train IP-Optical network using Integer Linear Programming (ILP).
Description
Background
Network operators are confronting continuously increasing QoS (Quality of Service) requirements. The need for Internet bandwidth and low latency is getting more demanding. Thus, network operators must make appropriate network equipment upgrades to serve the user traffic. A german train IP-Optical network faces similar challenges. The network operators of a railway company must stand up to the challenge of the rising traffic requested from the passengers. They must develop a mechanism to quantify the nature of the new needed equipment (i.e. dimensioning) while leveraging the current advances in the Coherent Pluggable Transceivers (CPT) modules.
Problem Description
In the context of this thesis, you are called to model and solve the dimensioning problem for a train IP-Optical network using Integer Linear Programming (ILP). More specifically, the thesis consists of the following steps:
- literature research on RMSA (Routing, Modulation and Spectrum Assignment) and CPT market
- adaptation of a given ILP model to the current scenario
- parametric study
- evaluation and visualization
Acquired Knowledge and Skills
In this thesis you will enrich your knowledge of IP-optical networks and ILP, a general methodology to find optimum solutions to linear problems. You will get an insight into core networking, network services, and modern challenges. Finally, you will learn how to run a parametric sweep simulation and evaluate the results.
Prerequisites
Basic knowledge in:
- Communication Networks Architecture and Design
- Programming Experience
- Julia Language
Contact
Please send your CV and transcript of records to:
- cristian.bermudez-serna@tum.de
- filippos.christou@ikr.uni-stuttgart.de
Supervisor:
Working Student for Network Delay Measurements
Description
Communication Networks must fulfill a strict set of requirements in the Industrial Area. The Networks must fulfill strict latency and bandwidth requirements to allow trouble-free operation. Typically, the industry relies on purpose build solutions that can satisfy the requirements.
Recently, the industry is moving towards using Ethernet-based Networks for their use case. This enables us to use common of the shelf hardware to communicate within the network. However, this hardware still will execute industrial applications and therefore has the same strict requirements as the network. In this project, we consider Linux-based hosts that run the industrial applications. We consider different networking hardware and configurations of the system to see how it affects performance. The goal is to investigate the overhead of the host.
Your tasks within the project are :
- Measure the Host Latency with different NICs
- Measure the Host Latency with different Hardware Offloads
- Tune, configure, and measure the Linux Scheduler to improve performance
You will gain:
- Experience with Networking Hardware
- Experience with Hardware Measurements
- Experience with Test Automation
Please send a short intro of yourself with your CV and transcript of records to us. We are looking forward to meeting you.
Prerequisites
- Familiarity with Linux Console
- Python
- C (not required, but a plus)
Contact
philip.diederich@tum.de
Supervisor:
Adaptive regenerator location selection in EON using reinforcement learning
Optical network planning, 3R regeneration, Reinforcement learning
Description
Elastic Optical Networks (EON) provide flexibility in bandwidth allocation, leading to improvement in spectrum utilization. The signal transmission capability is also improved, as lightpaths with better configurations of higher datarate and better modulation schemes can be deployed. The reach of the optical signal is controlled by the receivers' capability to receive the signal successfully, subjected to the received OSNR. This reach can be extended by using regeneration. Existing works use simple heuristics to find the locations for regeneration. The challenge is to update/ increase the regeneration locations based on the current network state. The optimal placement of regenerators and assignment of regeneration locations should aid in improving spectrum efficiency.
In this work, the student is expected to
- Designing and implementing an adaptive regeneration location selection algorithm, which considers the current network state, available spectrum, and available routing scenario, using reinforcement learning.
- Evaluating the performance of the algorithm in realistic topologies.
Interested students, please send an email with a short introduction of yourself along with your CV and grade transcript.
Prerequisites
- Strong Python and Java programming skills
- Experience in ML techniques (including reinforcement learning)
- Knowledge of optimization problems and classical methods is preferable
Contact
Saquib Amjad (saquib.amjad@tum.de)
Supervisor:
Working Student for the Implementation of a Medical Testbed
Communication networks, programming
Your goal is to implement a network for critical medical applications based on an existing open-access 5G networking framework as well as the adaptation of this network according to the needs of our research.
Description
Future medical applications put stringent requirements on the underlying communication networks in terms of highest availability, maximal throughput, minimal latency, etc. Thus, in the context of the 6G-life project, new networking concepts and solutions are being developed.
For the research of using 6G for medical applications, the communication and the medical side have joined forces: While researchers from the MITI group (Minimally invasive Interdisciplinary Therapeutical Intervention), located at the hospital "Rechts der Isar", focus on the requirements of the medical applications and collecting needed parameters of patients, it is the task of the researchers at LKN to optimize the network in order to satisfy the applications' demands. The goal of this joint research work is to have working testbeds for two medical testbeds located in the hospital to demonstrate the impact and benefits of future 6G networks and concepts for medical applications.
Your task during this work is to implement the communcation network for those testbeds. Based on an existing open-access 5G network implementation, you will implement changes according to the progress of the current research. The results of your work, working 6G medical testbeds, will enable researchers to validate their approaches with real-world measurements and allow to demonstrate future 6G concepts to research, industry and politics.
In this project, you will gain a deep insight into how communication networks, especially the Radio Access Network (RAN), work and how different aspects are implemented. Additionally, you will understand the current limitations and weaknesses as well as concepts for improvement. Also, you will get some insights into medical topics if interested. As in such a broad topic there are many open research questions, you additionally have the possibility to also write your thesis or complete an internship.
Prerequisites
- Most important: Motivation and willingness to learn unknown things.
- C/C++ and knowledge about how other programming languages work (Python, etc.) and/or the willingness to work oneself into such languages.
- Preferred: Knowledge about communication networks (exspecially the RAN), 5G concepts, the P4 language, SDN, Linux.
- Initiative to bring in own ideas and solutions.
- Ability to work with various partners (teamwork ability).
Please note: It is not necessary to know about every topic aforementioned, much more it is important to be willing to read oneself in.
Contact
Supervisor:
P4Update Improvement
Description
References:
[1] P4Update: Fast and Locally Verifiable Consistent Network Updates in the P4 Data Plane
Prerequisites
Contact
Zikai Zhou
Supervisor:
Design and evaluation of conditional device paging DRX
5G, IIoT, energy efficiency, DRX
Description
Energy Efficiency (EE) has become a key performance indicator for sustainable 5G networks due to the growth of next-generation mobile devices connected to the network and applications with the requirements to preserve energy resources. The relevance of EE increases for Industrial Internet of Things (IIoT) devices, which run on limited energy supported by the batteries not replaced over the lifetime.
Therefore, the development of methods to increase the energy efficiency on the device side has received the attention of academia and industry research. Reducing the continuous monitoring of the PDCCH channels is considered a key factor in increasing the device energy efficiency, especially considering the limited resources.
In this thesis, the student shall focus on the implementation and evaluation of a conditional device paging DRX mechanism to reduce the PDCCH channel monitoring. The mechanism will be evaluated through a 5G based simulator.
Prerequisites
- Good knowledge of Python and Matlab.
- Knowledge of mobile networks.