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- All Subjects: Bayesian statistical decision theory
- All Subjects: MIMO
- Genre: Masters Thesis
- Creators: Zhang, Junshan
- Creators: McCarville, Daniel R.
- Member of: Theses and Dissertations
- Status: Published
The performance of this protection is dependent upon numerous factors, including channel estimation accuracy, which is the focus of this thesis. In particular, the concentration is on estimating the self-interference channel. A novel approach of simultaneous signaling to estimate the self-interference channel in MIMO full-duplex relays is proposed. To achieve this simultaneous communications
and channel estimation, a full-rank pilot signal at a reduced relative power is transmitted simultaneously with a low rank communication waveform. The self-interference mitigation is investigated in the context of eigenvalue spread of spatial relay receive co-variance matrix. Performance is demonstrated by using simulations,
in which orthogonal-frequency division-multiplexing communications and pilot sequences are employed.
In this paper, a literature review is presented on the application of Bayesian networks applied in system reliability analysis. It is shown that Bayesian networks have become a popular modeling framework for system reliability analysis due to the benefits that Bayesian networks have the capability and flexibility to model complex systems, update the probability according to evidences and give a straightforward and compact graphical representation. Research on approaches for Bayesian network learning and inference are summarized. Two groups of models with multistate nodes were developed for scenarios from constant to continuous time to apply and contrast Bayesian networks with classical fault tree method. The expanded model discretized the continuous variables and provided failure related probability distribution over time.