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Task Load Modelling for LTE Baseband Signal Processing with Artificial Neural Network Approach
KTH, School of Electrical Engineering (EES), Signal Processing.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis gives a research on developing an automatic or guided-automatic

tool to predict the hardware (HW) resource occupation, namely task load, with

respect to the software (SW) application algorithm parameters in an LTE base

station. For the signal processing in an LTE base station it is important to

get knowledge of how many HW resources will be used when applying a SW

algorithm on a specic platform. The information is valuable for one to know

the system and platform better, which can facilitate a reasonable use of the

available resources.

The process of developing the tool is considered to be the process of building

a mathematical model between HW task load and SW parameters, where

the process is dened as function approximation. According to the universal

approximation theorem, the problem can be solved by an intelligent method

called articial neural networks (ANNs). The theorem indicates that any

function can be approximated with a two-layered neural network as long as

the activation function and number of hidden neurons are proper. The thesis

documents a work ow on building the model with the ANN method, as well

as some research on data subset selection with mathematical methods, such as

Partial Correlation and Sequential Searching as a data pre-processing step for

the ANN approach. In order to make the data selection method suitable for

ANNs, a modication has been made on Sequential Searching method, which

gives a better result.

The results show that it is possible to develop such a guided-automatic

tool for prediction purposes in LTE baseband signal processing under specic

precision constraints. Compared to other approaches, this model tool with

intelligent approach has a higher precision level and a better adaptivity, meaning

that it can be used in any part of the platform even though the transmission

channels are dierent.

Abstract [sv]

Denna avhandling utvecklar ett automatiskt eller ett guidat automatiskt verktyg

for att forutsaga behov av hardvaruresurser, ocksa kallat uppgiftsbelastning,

med avseende pa programvarans algoritmparametrar i en LTE basstation. I

signalbehandling i en LTE basstation, ar det viktigt att fa kunskap om hur

mycket av hardvarans resurser som kommer att tas i bruk nar en programvara

ska koras pa en viss plattform. Informationen ar vardefull for nagon att forsta

systemet och plattformen battre, vilket kan mojliggora en rimlig anvandning av

tillgangliga resurser.

Processen att utveckla verktyget anses vara processen att bygga en matematisk

modell mellan hardvarans belastning och programvaruparametrarna, dar

processen denieras som approximation av en funktion. Enligt den universella

approximationssatsen, kan problemet losas genom en intelligent metod som

kallas articiella neuronnat (ANN). Satsen visar att en godtycklig funktion kan

approximeras med ett tva-skiktS neuralt natverk sa lange aktiveringsfunktionen

och antalet dolda neuroner ar korrekt. Avhandlingen dokumenterar ett arbets-

ode for att bygga modellen med ANN-metoden, samt studerar matematiska

metoder for val av delmangder av data, sasom Partiell korrelation och sekventiell

sokning som dataforbehandlingssteg for ANN. For att gora valet av uppgifter

som lampar sig for ANN har en andring gjorts i den sekventiella sokmetoden,

som ger battre resultat.

Resultaten visar att det ar mojligt att utveckla ett sadant guidat automatiskt

verktyg for prediktionsandamal i LTE basbandssignalbehandling under specika

precisions begransningar. Jamfort med andra metoder, har dessa modellverktyg

med intelligent tillvagagangssatt en hogre precisionsniva och battre adaptivitet,

vilket innebar att den kan anvandas i godtycklig del av plattformen aven om

overforingskanalerna ar olika.

Place, publisher, year, edition, pages
2014. , 75 p.
EES Examensarbete / Master Thesis, XR-EE-SB 2014:013
Keyword [en]
Automatic tool, Signal Processing, Function Approximation, Prediction, ANNs, Data Pre-processing, Task Load Prediction
Keyword [sv]
Automatiskt verktyg, signalbehandling, Funktionsanpassning, Prediktion, Articiella Neuronnat, Dataforbehandling
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-160947OAI: diva2:792776
Educational program
Master of Science - Wireless Systems
Available from: 2015-03-05 Created: 2015-03-05 Last updated: 2015-03-05Bibliographically approved

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