1,273 research outputs found
Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems
Hybrid massive MIMO structures with lower hardware complexity and power
consumption have been considered as a potential candidate for millimeter wave
(mmWave) communications. Channel covariance information can be used for
designing transmitter precoders, receiver combiners, channel estimators, etc.
However, hybrid structures allow only a lower-dimensional signal to be
observed, which adds difficulties for channel covariance matrix estimation. In
this paper, we formulate the channel covariance estimation as a structured
low-rank matrix sensing problem via Kronecker product expansion and use a
low-complexity algorithm to solve this problem. Numerical results with uniform
linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to
demonstrate the effectiveness of our proposed method
Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments
Hybrid massive MIMO structures with reduced hardware complexity and power
consumption have been widely studied as a potential candidate for millimeter
wave (mmWave) communications. Channel estimators that require knowledge of the
array response, such as those using compressive sensing (CS) methods, may
suffer from performance degradation when array-inherent impairments bring
unknown phase errors and gain errors to the antenna elements. In this paper, we
design matrix completion (MC)-based channel estimation schemes which are robust
against the array-inherent impairments. We first design an open-loop training
scheme that can sample entries from the effective channel matrix randomly and
is compatible with the phase shifter-based hybrid system. Leveraging the
low-rank property of the effective channel matrix, we then design a channel
estimator based on the generalized conditional gradient (GCG) framework and the
alternating minimization (AltMin) approach. The resulting estimator is immune
to array-inherent impairments and can be implemented to systems with any array
shapes for its independence of the array response. In addition, we extend our
design to sample a transformed channel matrix following the concept of
inductive matrix completion (IMC), which can be solved efficiently using our
proposed estimator and achieve similar performance with a lower requirement of
the dynamic range of the transmission power per antenna. Numerical results
demonstrate the advantages of our proposed MC-based channel estimators in terms
of estimation performance, computational complexity and robustness against
array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS
channel estimator.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
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How transformation expectation leads consumers to immediate gratification - A PLS-SEM approach
This study explores the mechanism which triggers consumer's immediate gratification
behavior. It is proposed that consumer's expectation of meaningful life transformation by
acquisition of a product causes her perception of product hedonic and utilitarian value, which
can further predict immediate gratification. The positive impact of perception of hedonic
value on immediate gratification can be mediated by price sensitivity and moderated by
materialism level. The structural model is established for further empirical analysis with PLSSEM
approach. The model suggests different domain of transformation expectation may have
conflicting impact on immediate gratificatio
Mixed Perspectives and Thematic Analysis in Design Education
This research explores the design of products based on users’ emotional requirements and how students can be stimulated to generate novel ideas in design education. In order to achieve these aims, multiple methods were taught to students during an online course. In the first step, the students utilised interviews, questionnaires, and mixed perspectives to design hill censers according to the users’ emotional requirements. In the second step, the researcher conducted a qualitative thematic analysis to study the students’ collected survey reports. The analytic results were then shared with students to help them quickly obtain better novel design ideas. And then, an emotional design appraisal model was built in the third step. The two main findings are as follows: first, creation in light of the stakeholder’s perspective enabled the students to come up with better design ideas quickly. Second, the ‘design method’ and ‘emotional experience’ themes obtained by the thematic analysis were found to be vital for the designers/students. Notably, the ‘design method’ theme can help students generate novel design ideas, and the students can learn the users’ needs from the ‘emotional experience’ theme
Principles of microRNA regulation of a human cellular signaling network
MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs, which suppress gene
expression by selectively binding to the 3-noncoding region of specific message
RNAs through base-pairing. Given the diversity and abundance of miRNA targets,
miRNAs appear to functionally interact with various components of many cellular
networks. By analyzing the interactions between miRNAs and a human cellular
signaling network, we found that miRNAs predominantly target positive
regulatory motifs, highly connected scaffolds and most downstream network
components such as signaling transcription factors, but less frequently target
negative regulatory motifs, common components of basic cellular machines and
most upstream network components such as ligands. In addition, when an adaptor
has potential to recruit more downstream components, these components are more
frequently targeted by miRNAs. This work uncovers the principles of miRNA
regulation of signal transduction networks and implies a potential function of
miRNAs for facilitating robust transitions of cellular response to
extracellular signals and maintaining cellular homeostasis
A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients
In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes
Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications
This work concerns receiver design for light emitting diode (LED)
communications where the LED nonlinearity can severely degrade the performance
of communications. We propose extreme learning machine (ELM) based
non-iterative receivers and iterative receivers to effectively handle the LED
nonlinearity and memory effects. For the iterative receiver design, we also
develop a data-aided receiver, where data is used as virtual training sequence
in ELM training. It is shown that the ELM based receivers significantly
outperform conventional polynomial based receivers; iterative receivers can
achieve huge performance gain compared to non-iterative receivers; and the
data-aided receiver can reduce training overhead considerably. This work can
also be extended to radio frequency communications, e.g., to deal with the
nonlinearity of power amplifiers
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