Presentation Title

Autonomous Self-Healing and Beamforming in 5G Networks

Faculty Mentor

Thomas Ketseoglou, Tamer Omar

Start Date

23-11-2019 10:00 AM

End Date

23-11-2019 10:45 AM

Location

147

Session

poster 3

Type of Presentation

Poster

Subject Area

engineering_computer_science

Abstract

In order to meet increasing data demands, we will have to upgrade our physical infrastructure with new 5g technologies. A 5g network will be required to provide between 1-10 Gigabits/second with less than 1 millisecond of latency. These networks will be using a variety of new technologies including: MIMO beamforming antennae, which will significantly reduce wasted power and allow for more reliable communication; Millimeter Wavelengths (mmWave), an unused portion of the EM spectrum that will allow for higher data rates; and new network management techniques to ensure network reliability. Notably, it will be important that the network be able to recover autonomously from network failure through a process called Self-Healing. Self-Healing processes are critical for these future networks because they will likely often be comprised with a dense number of smaller cells; where older networks had base stations every few miles or so, 5g coverage will sometimes require many small cells to maintain coverage in dense environments. With the greatly increased number of cells, dealing with network failure in person or even remotely becomes impractical, and thus it will be ideal for the network to recover from failure on its own. Our project aims to be capable of simulating a network comprised of these new technologies in order to test various self-healing and network management strategies in the hopes of implementing a digital network manager. A MATLAB-based simulation of the beamformer performance improvement will be developed. The simulator will accurately model channel conditions and model downlink communications between the Base Stations (BS) and User Equipments (UE). The project centers around determining the optimal algorithms implemented at the management level for achieving this result using the constraints introduced by the environment and technologies simulated. Preliminary beamforming performance results will be shown in the paper.

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Nov 23rd, 10:00 AM Nov 23rd, 10:45 AM

Autonomous Self-Healing and Beamforming in 5G Networks

147

In order to meet increasing data demands, we will have to upgrade our physical infrastructure with new 5g technologies. A 5g network will be required to provide between 1-10 Gigabits/second with less than 1 millisecond of latency. These networks will be using a variety of new technologies including: MIMO beamforming antennae, which will significantly reduce wasted power and allow for more reliable communication; Millimeter Wavelengths (mmWave), an unused portion of the EM spectrum that will allow for higher data rates; and new network management techniques to ensure network reliability. Notably, it will be important that the network be able to recover autonomously from network failure through a process called Self-Healing. Self-Healing processes are critical for these future networks because they will likely often be comprised with a dense number of smaller cells; where older networks had base stations every few miles or so, 5g coverage will sometimes require many small cells to maintain coverage in dense environments. With the greatly increased number of cells, dealing with network failure in person or even remotely becomes impractical, and thus it will be ideal for the network to recover from failure on its own. Our project aims to be capable of simulating a network comprised of these new technologies in order to test various self-healing and network management strategies in the hopes of implementing a digital network manager. A MATLAB-based simulation of the beamformer performance improvement will be developed. The simulator will accurately model channel conditions and model downlink communications between the Base Stations (BS) and User Equipments (UE). The project centers around determining the optimal algorithms implemented at the management level for achieving this result using the constraints introduced by the environment and technologies simulated. Preliminary beamforming performance results will be shown in the paper.