Massive MIMO perspective: improved sea lion for optimal antenna selection

Rao, Inumula Veeraraghava; Rao, V. Malleswara

doi:10.1007/s12065-020-00457-x

Cited by 6 publications

(5 citation statements)

References 41 publications

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“…Here, the performance of classification was analyzed by analyzing the Type I and Type II measures. Here, “Type I measures were positive measures like Accuracy, Sensitivity, Specificity, Precision, Negative Predictive Value (NPV), F1Score and Mathews correlation coefficient (MCC), and Type II measures were negative measures like False positive rate (FPR), False negative rate (FNR), and False Discovery Rate (FDR).” The experimental analysis was conducted by considering the “number of population as 10 and maximum number of iteration as 100.” The performance of the suggested approach was compared with grey wolf optimization (GWO) (Mirjalili et al , 2014), improved sea lion optimization (ISLO) (Rao and Rao, 2021), COA (Yuan et al , 2020), JA (Venkata Rao, 2016), DNN (Apoorva and Sangeetha, 2021), fuzzy (Ansari et al , 2013), SVM (Manjula Devi and Seenivasagam, 2020) and improved sealion optimization algorithm-attention pyramid-convolutional neural network (BU-SLnO)-AP-CNN (Rao and Rao, 2021; Manne et al , 2020).…”

Section: Resultsmentioning

confidence: 99%

“…The experimental analysis was conducted by considering the "number of population as 10 and maximum number of iteration as 100." The performance of the suggested approach was compared with grey wolf optimization (GWO) (Mirjalili et al, 2014), improved sea lion optimization (ISLO) (Rao and Rao, 2021), COA (Yuan et al, 2020), JA (Venkata Rao, 2016), DNN (Apoorva and Sangeetha, 2021), fuzzy (Ansari et al, 2013), SVM (Manjula Devi and Seenivasagam, 2020) and improved sealion optimization algorithm-attention pyramidconvolutional neural network (BU-SLnO)-AP-CNN (Rao and Rao, 2021;Manne et al, 2020). IJPCC 19,4…”

Section: Results and Discussion 61 Experimental Setupmentioning

confidence: 99%

“…In 2020, Rao and Rao (2021) have introduced an innovative wireless system based on magnetic induction for HAR. The magnetic induction system was integrated with machine learning techniques to detect a wide range of human motions.…”

Section: Literature Surveymentioning

confidence: 99%

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Human activity recognition in WBAN using ensemble model

Boga

2022

IJPCC

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Purpose For achieving the profitable human activity recognition (HAR) method, this paper solves the HAR problem under wireless body area network (WBAN) using a developed ensemble learning approach. The purpose of this study is,to solve the HAR problem under WBAN using a developed ensemble learning approach for achieving the profitable HAR method. There are three data sets used for this HAR in WBAN, namely, human activity recognition using smartphones, wireless sensor data mining and Kaggle. The proposed model undergoes four phases, namely, “pre-processing, feature extraction, feature selection and classification.” Here, the data can be preprocessed by artifacts removal and median filtering techniques. Then, the features are extracted by techniques such as “t-Distributed Stochastic Neighbor Embedding”, “Short-time Fourier transform” and statistical approaches. The weighted optimal feature selection is considered as the next step for selecting the important features based on computing the data variance of each class. This new feature selection is achieved by the hybrid coyote Jaya optimization (HCJO). Finally, the meta-heuristic-based ensemble learning approach is used as a new recognition approach with three classifiers, namely, “support vector machine (SVM), deep neural network (DNN) and fuzzy classifiers.” Experimental analysis is performed. Design/methodology/approach The proposed HCJO algorithm was developed for optimizing the membership function of fuzzy, iteration limit of SVM and hidden neuron count of DNN for getting superior classified outcomes and to enhance the performance of ensemble classification. Findings The accuracy for enhanced HAR model was pretty high in comparison to conventional models, i.e. higher than 6.66% to fuzzy, 4.34% to DNN, 4.34% to SVM, 7.86% to ensemble and 6.66% to Improved Sealion optimization algorithm-Attention Pyramid-Convolutional Neural Network-AP-CNN, respectively. Originality/value The suggested HAR model with WBAN using HCJO algorithm is accurate and improves the effectiveness of the recognition.

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Section: Resultsmentioning

confidence: 99%

Section: Results and Discussion 61 Experimental Setupmentioning

confidence: 99%

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Human activity recognition in WBAN using ensemble model

Boga

2022

IJPCC

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“…The suggested optimum TAS design was implemented in “MATLAB,” and the outcomes were displayed. Accordingly, the supremacy of the presented HS‐WA model was evaluated over existing models, namely, the modified velocity vector‐based gravitational search algorithm (MV‐GSA), 36 the improved sea lion optimization (I‐SLnO), 37 the SLnO, 34 the WOA, 29 and the particle swarm optimization (PSO) 38 . Here, two sets were used for the analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed optimal TAS model was implemented in "MATLAB" and the outcomes were noticed. Accordingly, the supremacy of the presented HS-WA model was evaluated over existing models namely, MV-GSA [27], I-SLnO [28], SLnO [24] and WOA [25]. Here, analysis was done using 2 setups.…”

Section: Simulation Setupmentioning

confidence: 99%

Optimal transmit antenna selection using hybrid algorithm for massive MIMO technology

Singh

Kishoreraja

2022

Int J Communication

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Massive Multiple Input Multiple Output (M-MIMO) systems specifically refers to a practical technique for sending and receiving more than one data signal simultaneously over the same radio channel by exploiting multipath propagation". It depends on several antennas for transferring varied data streams simultaneously. With the increase in count of antennas, the energy or power utilization also gets increased. Thus, it becomes necessary to select optimal transmit antennas that exist as the great challenge in M-MIMO systems. This work introduces a new "Hybrid Sea Lion-Whale Algorithm (HS-WA)" for selecting the optimal transmit antenna by considering the multi-objectives, which increases both capacity and efficiency. The adopted scheme is the combination of both "Whale Optimization Algorithm (WOA) and Sea Lion Optimization Algorithm (SLnO)" that optimizes the antenna's count and moreover, it finds out "which antenna to be selected". At last, the supremacy of presented model is confirmed over existing models in terms EE and capacity analysis.

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Hybrid optimization assisted transmit antenna selection for massive MIMO technology

Singh

Raja

2023

Multimed Tools Appl

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Massive MIMO perspective: improved sea lion for optimal antenna selection

Cited by 6 publications

References 41 publications

Human activity recognition in WBAN using ensemble model

Human activity recognition in WBAN using ensemble model

Optimal transmit antenna selection using hybrid algorithm for massive MIMO technology

Hybrid optimization assisted transmit antenna selection for massive MIMO technology

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