Singular Value Decomposition: Principles and Applications in Multiple Input Multiple Output Communication System

Shehab, Wael Abu; Al-qudah, Zouhair

doi:10.5121/ijcnc.2017.9102

Cited by 11 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The singular value decomposition (SVD) is an effective mathematical tool for evaluating the MIMO capacity [39], [40]. In this section, we use the SVD and assume that the channel state information is available at the D-OFEs and MUs.…”

Section: Throughput Evaluationmentioning

confidence: 99%

All-Optical Distributed MIMO for LiFi: Spatial Diversity Versus Spatial Multiplexing

et al. 2022

View full text Add to dashboard Cite

LiFi or networked optical wireless communication is likely to play an important role in offloading mobile data traffic from radio into the optical spectrum. As the number of Internet of Things (IoT) devices is growing, the RF spectrum becomes a rare resource. Imaging IoT sensors like cameras, ultrasonic devices, and Lidars have real-time requirements, need a high-capacity uplink, and operate in environments that cause or are sensitive against electromagnetic interference. In this paper, for the first time, we present realtime communication over an all-optical fixed-wireless LiFi link based on the distributed multiple-input multiple-output concept. For distributing the wireless signals, plastic optical fibers are used as an analog front-haul. We study the operation of the distributed multiple-input multiple-output link in two modes, i.e., spatial diversity and spatial multiplexing. For the diversity mode, a new combiner is presented, which can support equal gain as well as selection combining. We demonstrate that selection combining is highly effective and enables a similar LiFi performance in up-and downlink, as it is desirable for industrial applications. For the spatial multiplexing mode, we observe that the channel rank and the achievable throughput depend strongly on the user location. As effective solutions, we study the benefits of angular diversity and multiple-input multiple-output mode switching together with multi-user multiplexing and conclude that a dynamic switching between spatial diversity and spatial multiplexing is a practical approach.

show abstract

Section: Throughput Evaluationmentioning

confidence: 99%

All-Optical Distributed MIMO for LiFi: Spatial Diversity Versus Spatial Multiplexing

et al. 2022

View full text Add to dashboard Cite

show abstract

“…where ε is estimation error and H  is attained from singular value decomposition (SVD) [46], that is,…”

Section: By Applying Convergence Of   H H H Mmentioning

confidence: 99%

Impact of Circuit Intake Power on the Spectral Energy Efficiency of Massive MIMO With Channel Estimation Errors

Kibona

Liu

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The influence of circuit intake power on Spectral Energy Efficiency (S.E.E) of massive Multiple-Input-Multiple-Output (MIMO) under the existence of channel estimation errors is examined using Zero-Forcing (ZF) linear precoding scheme. The system model which includes new defined total intake power involving the transmit power of amplifiers and the circuit power intake of the analog devices is developed. To analyze the impact of circuit intake power on S.E.E, the S.E.E with and without circuit intake power is investigated and presented after formulating a closed-form expression of S.E.E that incorporates new defined power model. Our investigation reveals that, the impact of circuit intake power is very significant when Base Station (BS) is arrayed with large number of antennas. Using the new S.E.E closed-form formula, derived from our new defined power intake model, the S.E.E is evaluated when the number of BS antennas (M) rises and it is observed that, S.E.E behaves like a concave function. The same results for S.E.E are also observed when transmit power increases. We conclude that, it is easy to get maximum S.E.E using a small number of BS antennas and optimal transmit power, when circuit power intake is included in the power consumption model. When transmit power totally dominates the circuit intake power, the maximum S.E.E is obtained only when all antennas are used (meaning maximum S.E.E is obtained when the number of BS antennas becomes very large). The numerical results reveal the importance of circuit intake power on optimizing S.E.E of massive MIMO under the presence of channel estimation errors.

show abstract

“…SVD is one of the most popular unsupervised data-mining algorithms and one of the most appropriate mapping tools used for mapping a high-dimensionality data space or vector space to other dimensions. Mathematically, let X be an m × n matrix and let the rank of X be r. The matrix rank is the largest number of rows (or columns) where no nonzero linear combination of rows is the zero vector (a set of such rows or columns is independent) [26]. Then, matrices U, , and V are calculated as shown in Fig.…”

Section: (B)mentioning

confidence: 99%

A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition

Al-Saffar

Yıldırım

2020

IEEE Access

View full text Add to dashboard Cite

Brain image classification is one of the most useful and widely needed processes in the medical system, and it is a highly challenging field. This paper presents a new method for selecting a significant subset of features as the input to the classifier, called mutual information-accelerated singular value decomposition (MI-ASVD). This novel algorithm is exploited to design an intelligent system for classifying MRI brain images into three classes: healthy, high-grade glioma, and low-grade glioma. The proposed system has six stages: pre-processing, clustering, tumour localization, feature extraction, MI-ASVD and classification. First, the MR images are smoothed by using enhancement techniques such as Gaussian kernel filters. Then, local difference in intensity-means (LDI-Means) clustering is employed to segment and detect suspicious regions. The grey-level run-length matrix (GLRLM), texture, and colour intensity features are used for tumour feature extraction. Later, a special method including a summation of feature selection and dimensionality reduction, MI-ASVD, is applied to select the most useful features for the classification process. Finally, the simplified residual neural network technique is implemented to classify the MR brain images. Using MI-ASVD provided accurate and more efficacious results in classification compared with the original feature space and with two other standard dimensionality reduction methods, principal component analysis (PCA) and singular value decomposition (SVD). It achieved a classification accuracy of 94.91%, which is better than the two state-of-the-art techniques as well as methods from similar published studies. INDEX TERMS Brain image classification, clustering, image processing, machine learning, mutual information, PCA, residual neural network (RNN), SVD. TÜLAY YILDIRIM (Member, IEEE) received the B.Sc. and M.Sc. degrees in electronics and communication engineering from Yildiz Technical University, Istanbul, Turkey, in 1990 and 1992, respectively, and the Ph.D. degree in electrical and electronics engineering from the University of Liverpool, U.K., in 1997. She is currently a Full Professor with the Department of Electronics and Communications Engineering, Yildiz Technical University. She has authored over 200 publications in journals, conferences, and books. Her research interests include artificial intelligence, intelligent systems, cyber-physical systems, biomedical instrumentation, biometric person identification and verification systems, electronic circuits and systems, and artificial neural networks.

show abstract

Singular Value Decomposition: Principles and Applications in Multiple Input Multiple Output Communication System

Abstract: The authors discuss the importance of using the singular value decomposition (SVD) in computing the capacity of multiple input multiple output (MIMO)

Cited by 11 publications

References 11 publications

All-Optical Distributed MIMO for LiFi: Spatial Diversity Versus Spatial Multiplexing

All-Optical Distributed MIMO for LiFi: Spatial Diversity Versus Spatial Multiplexing

Impact of Circuit Intake Power on the Spectral Energy Efficiency of Massive MIMO With Channel Estimation Errors

A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition

Contact Info

Product

Resources

About