Dimensioning Packet Based Fronthaul for Radio Base Stations: Network Requirements and Traffic Scenarios

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Examensarbete för masterexamen
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2022
Författare
Abraham, Jimmy
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A radio base station is a wireless communication station which is used for wireless telephone systems such as GSM (2G), WCDMA (3G), LTE (4G) and NR (5G). Today, radio base stations use circuit switched fronthaul and will continue to do so for a foreseeable future, however packet based fronthaul will grow as 5G development does as well. A radio base station for packet based communication consists of three main units: radio, baseband and switch. The aim of the thesis was to implement a simulator for packet based fronthaul and determine optimal network configurations for a given traffic load through statistical methods. First the radio base stations were defined mathematically as a small network through graph theory. Then traffic generation was characterized as a two state Markov chain. Algorithms were designed for both network and traffic generation. Packets inside ethernet networks follow protocols which were simplified and adapted for radio base stations. An algorithm was designed for simulating the packets movement in the network. The simulator was implemented in Python and was evaluated through statistical methods to determine the amount of time steps and iterations that is required to have consistent simulation results. Two-sided one sample t-test showed that five iterations for each simulation was enough to generate samples close enough to the expected mean and 10000 time steps was shown to be enough for a low standard deviation. Finally, data was generated with the simulator for selected combinations of network properties. Selected properties were number of radio units, buffer size, number of switches, and traffic load. A Poisson regression model was used to correlate properties of a given network with the amount of packet loss as response variable. Results show that the D2 score is about 0.8 and prove that the model works with a reasonably good score. This also shows that with more time and development a more advanced analysis can be made, and better machine learning models can be made with the help of the Ethernet Simulator.
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Packets, Ethernet Frames, eCPRI, Telecommunication, Radio Base Station, RAN, 5G, Machine learning, Poisson Regression GLM, Fronthaul
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