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28th Signal Processing and Communications Applications Conference, SIU (2020; Gaziantep, Turkey), Constant Envelope (CE) precoding is a transmission technique, which provides efficient power amplification and can be realised with a single radio frequency (RF) chain at the multi-antenna transmitter. In this paper, we study the multi-user massive MIMO broadcast channel with CE precoding and with non-orthogonal multiple access (NOMA). The motivation for this is to provide different multi-user interferance (MUI) energy levels to different users with different MUI requirements. Wepair users according to their channel conditions and examine the impact of the NOMA on MUI. Simulations for the independent and identically distributed Rayleigh fading channel show that different MUI energy levels is possible with NOMA under CE precoding., Sabit genlikli önkodlama, çok antenli ileticide tek bir radyo frekans zinciri kullanarak, etkili ve uygulanabilir güç yükseltimi sağlayan bir iletim tekniğidir. Bu bildiride, yoğun çok girdili çok çıktılı ve çok kullanıcıya iletim yapılan sistemlerde sabit genlikli önkodlama kullanımı incelenmektedir. Kullanıcılara iletim yapılırken dikgen olmayan çoklu erişim kullanılmaktadır. Bu yöntemle, farklı ihtiyaçlara sahip kullanıcılar için farklı girişim enerjisi seviyeleri sağlamak hedeflenmiştir. Kullanıcıların kanal durumları gözetilerek kullanıcı eşleştirme tekniği kullanılmış ve dikgen olmayan çoklu erişimin kullanıcılarda elde edilen girişim enerjisi üzerindeki etkisi incelenmiştir. Bağımsız ve eş dağılıma sahip Rayleigh sönümlü kanallar üzerinde yapılan benzetim sonuçlarına göre dikgen olmayan çoklu erişim kullanıldığında farklı seviyelerde çok kullanıcılı girişim ene

Non-negative Tensor Factorization (NTF) has become a prominent tool for analyzing high dimensional multi-way structured data. In this paper we set out to analyze gene expression across brain regions in multiple subjects based on data from the Allen Human Brain Atlas [1] with more than 40 % data missing in our problem. Our analysis is based on the non-negativity constrained Canonical Polyadic (CP) decomposition where we handle the missing data using marginalization considering three prominent alternating least squares procedures; multiplicative updates, column-wise, and row-wise updating of the component matrices. We examine three gene expression prediction scenarios based on data missing at random, whole genes missing and whole areas missing within a subject. We find that the column-wise updating approach also known as HALS performs the most efficient when fitting the model. We further observe that the non-negativity constrained CP model is able to predict gene expressions better than predicting by the subject average when data is missing at random. When whole genes and whole areas are missing it is in general better to predict by subject averages. However, we find that when whole genes are missing from all subjects the model based predictions are useful. When analyzing the structure of the components derived for one of the best predicting model orders the components identified in general constitute localized regions of the brain. Non-negative tensor factorization based on marginalization thus forms a promising framework for imputing missing values and characterizing gene expression in the human brain. However, care also has to be taken in particular when predicting the genetic expression levels at a whole region of the brain missing as our analysis indicates that this requires a substantial amount of subjects with data for this region in order for the model predictions to be reliable.