Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

Alghorani, Yahia; Chekkouri, Ahmad; Chekired, Djabir; Pierre, Samuel

doi:10.2139/ssrn.3557599

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…In recent years, as deep learning has been further developed, it is applied in industry field. Deep learning is used to solve problems encountered in the production process, such as the application of machine learning to channel modelling, through which the transmission efficiency of wireless communication systems is significantly improved [11,12]. Meanwhile, more and more scholars have combined machine vision and deep learning and used them in the field of intelligent manufacturing, such as waste classification [13] and defect localization in parts [14].…”

Section: Introductionmentioning

confidence: 99%

A DenseNet‐based feature weighting convolutional network recognition model and its application in industrial part classification

An,

Sun,

Song

et al. 2023

IET Image Processing

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Traditional warehousing typically needs machine learning or manual tagging to classify objects. However, this method is less robust and consumes a lot of labour and material resources. Based on DenseNet, this work proposes a feature weighting convolutional network recognition model and designs a set of software and hardware for data acquisition, which is applied to the efficient classification of industrial parts in warehouse management. Firstly, this work modifies DenseNet by embedding SE‐Block, and replaces the cross‐entropy loss function with the focus loss function to optimize the model structure. Secondly, a multi‐view hardware and software acquisition system is designed to complete the functions of part image acquisition, image preprocessing, model training and part recognition. Finally, an industrial parts sorting experiment was designed. Compared with the original DenseNet model, the proposed weighted convolutional network identification model showed that the accuracy of the modified model was increased by 3.09% and the convergence rate was significantly improved. The modified model proposed in this work aims to improve the recognition accuracy of industrial parts in modern warehouse management, so as to modify the classification efficiency of warehouse parts in production.

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Section: Introductionmentioning

confidence: 99%

A DenseNet‐based feature weighting convolutional network recognition model and its application in industrial part classification

An,

Sun,

Song

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

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Exact analysis of the multihop multibranch hybrid AF/DF relaying networks

Forghani

Hassan

Mabrouk

et al. 2020

Int J Communication

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Summary In this paper, the exact analysis of a multihop multibranch (MHMB) relaying network is investigated wherein each relay can operate in amplify‐and‐forward (AF) or decode‐and‐forward (DF) modes depending upon the decoding result of its received signal. If a relay decodes the received signal correctly, it works in DF mode; otherwise, the relay operates in AF mode. Therefore, we name such relaying network as hybrid amplify‐and‐forward and decode‐and‐forward (hybrid AF/DF) relaying network. We first investigate the signal transmission from source to destination node via n number of relays in a hybrid AF/DF MHMB mode. Then, we obtained the statistical features and analyze the end‐to‐end signal‐to‐noise ratio (SNR). Finally, a comprehensive performance analysis is conducted by using maximal ratio combining (MRC) scheme at the destination node. For comparison, we also obtained the results using selection combining (SC) scheme at the destination node. To the best of our knowledge, very few works in the literature have considered a general system model of MHMB relaying network wherein each relay can operate in AF or DF modes, that is, a hybrid AF/DF relaying network. Accordingly, the analysis of our system model is not only novel and exact, but also is comprehensive and can be employed in the future works.

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Cross-Layer Inference in WSN: From Methods to Experimental Validation

Dey¹

2021

Wireless Sensor Networks - Design, Deployment and Applications

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In this chapter, the fundamentals of distributed inference problem in wireless sensor networks (WSN) is addressed and the statistical theoretical foundations to several applications is provided. The chapter adopts a statistical signal processing perspective and focusses on distributed version of the binary-hypothesis test for detecting an event as correctly as possible. The fusion center is assumed to be equipped with multiple antennas collecting and processing the information. The inference problem that is solved, primarily concerns the robust detection of a phenomenon of interest (for example, environmental hazard, oil/gas leakage, forest fire). The presence of multiple antennas at both transmit and receive sides resembles a multiple-input-multiple-output (MIMO) system and allows for utilization of array processing techniques providing spectral efficiency, fading mitigation and low energy sensor adoption. The problem is referred to as MIMO decision fusion. Subsequently, both design and evaluation (simulated and experimental) of these fusion approaches is presented for this futuristic WSN set-up.

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Improved S-AF and S-DF Relaying Schemes using Machine Learning based Power Allocation over Cascaded Rayleigh Fading Channels

Cited by 3 publications

References 29 publications

A DenseNet‐based feature weighting convolutional network recognition model and its application in industrial part classification

A DenseNet‐based feature weighting convolutional network recognition model and its application in industrial part classification

Exact analysis of the multihop multibranch hybrid AF/DF relaying networks

Cross-Layer Inference in WSN: From Methods to Experimental Validation

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