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Mobility Management for Computation-Intensive Tasks in Cellular Networks with SD-RAN

Beschreibung

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.

 

 

 

 

 

 

Voraussetzungen

Good knowledge of Python and interest to learn about mobility management in 5G

Betreuer:

Anna Prado, Fidan Mehmeti

Enhanced Mobility Management in 5G Networks with SD-RAN

Beschreibung

 

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.

 

 

Voraussetzungen

Good knowledge of Python and interest to learn about mobility management in 5G

Betreuer:

Anna Prado, Fidan Mehmeti