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Communication systems depend on non-linear and non-stationary circuits to implement its function. To successfully harness such circuits its essential to understand its behavior in all working conditions.
This thesis will investigate and devise kernelized density estimation methods for constructing a probabilistic characterization of such circuits. Several circuits of similar type but of increasing non-linear complexity will be considered and furthermore the operation conditions will vary (e.g. changing temperature).
The thesis will consider the following activities:
- Survey literature on maximum mean discrepancy and related kernelized methods for density estimation.
- Implement methods from survey and evaluate on a set of members of the exponential family and products/mixtures thereof.
- Characterize the non-stationary stochastic process of a set non-linear circuits of increasing complexity when applied to an exponential distribution in a set of working conditions.
Thesis work description/Qualifications
This thesis work is suitable for one to two students with background in mathematics / statistics (optimization, random processes, probability theory, machine learning, programming).
The students will study relevant literature, conduct theoretical work and develop training framework.
Extent: 30 hp
Location: The thesis will be conducted in Ericsson’s office in Kista, Sweden.
If you feel interested, please send in your application as soon as possible. The start date can be adjusted to both your and the business needs – the intention is to start in January.
While applying please attach your updated CV, current grades and cover letter written in English into one document (under CV field in the application tool) and clearly define your technical knowledge.
The above presented thesis is not written in stone. Have an idea on how we can make it even better? We would love to hear them.
If you have any questions, please reach out to Sylwia Kwiecień at email@example.com
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Primary country and city: Sweden (SE) || || Stockholm || Stud&YP
Req ID: 305463