<scp>MC–CDMA</scp> receiver design using recurrent neural networks for eliminating multiple access interference and nonlinear distortion

Kumar, Chandan; Bagadi, Kala Praveen

doi:10.1002/dac.3328

Cited by 19 publications

(3 citation statements)

References 22 publications

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“…The position of a signal at a dumb node is determined by its DOA. One can determine the signal's direction by calculating the receiver's propagation delay with the reference angle, which can be worked out with the help of a direct reference (Ravikumar & Bagadi, 2017). Using this method, the AOA for a dumb node's location is found using at least three beacon nodes.…”

Section: Related Studiesmentioning

confidence: 99%

Enhancement of Precise Underwater Object Localization

Sathish

Anbazhagan

et al. 2023

Radio Science

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Underwater communication applications extensively use localization services for object identification. Because of their significant impact on ocean exploration and monitoring, underwater wireless sensor networks (UWSN) are becoming increasingly popular, and acoustic communications have largely overtaken radio frequency (RF) broadcasts as the dominant means of communication. The two localization methods that are most frequently employed are those that estimate the angle of arrival (AOA) and the time difference of arrival (TDoA). The military and civilian sectors rely heavily on UWSN for object identification in the underwater environment. As a result, there is a need in UWSN for an accurate localization technique that accounts for dynamic nature of the underwater environment. Time and position data are the two key parameters to accurately define the position of an object. Moreover, due to climate change there is now a need to constrain energy consumption by UWSN to limit carbon emission to meet net‐zero target by 2050. To meet these challenges, we have developed an efficient localization algorithm for determining an object position based on the angle and distance of arrival of beacon signals. We have considered the factors like sensor nodes not being in time sync with each other and the fact that the speed of sound varies in water. Our simulation results show that the proposed approach can achieve great localization accuracy while accounting for temporal synchronization inaccuracies. When compared to existing localization approaches, the mean estimation error (MEE) and energy consumption figures, the proposed approach outperforms them. The MEEs is shown to vary between 84.2154m and 93.8275m for four trials, 61.2256m and 92.7956m for eight trials, and 42.6584m and 119.5228m for twelve trials. Comparatively, the distance‐based measurements show higher accuracy than the angle‐based measurements.This article is protected by copyright. All rights reserved.

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Section: Related Studiesmentioning

confidence: 99%

Enhancement of Precise Underwater Object Localization

Sathish

Anbazhagan

et al. 2023

Radio Science

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“…High energy density and lightning-fast response times are hallmarks of modern battery and supercapacitor technologies [24,[26][27][28][29][30]. Because of this, they can handle a surge in power demand and maintain a constant output throughout the change [31][32][33][34][35].…”

Section: Energy Management System In Hybrid Power Systemmentioning

confidence: 99%

Intelligent Energy Management for Distributed Power Plants and Battery Storage

Prakash

Varma

Ravikumar

et al. 2023

International Transactions on Electrical Energy Systems

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A smart energy management controller is required for effective source coordination and load demand management. This work proposes a novel instantaneous current reference technique for use in power management of hybrid power systems (HPS), both autonomous and interconnected with the grid. A grid integrated hybrid power system (GI-HPS) includes both the AC grid and additional sources used in industrial and commercial environments. Solar photovoltaic (SPV) panels, wind turbine generators, proton exchange membrane fuel cells, and batteries are all part of the test system. The suggested energy management system (SEMS) manages power from the hybrid power source and the energy storage components to meet the load needs. The recommended SEMS can transit between 12 different modes of operation to fulfil the load demand requirements. The SEMS employs a scaling factor N to accelerate the rate at which the measured current approaches the reference current. The proposed scaling factor significantly improves the SEMS’s dynamic performance since it can quickly respond to the changes in the source and load characteristics. The dependability of an HPS powered by a variety of renewable energy sources can also be improved.

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“…Zero-forcing (ZF), equal gain combining (EGC), and maximum-ratio combining (MRC) are a few examples of linear detection methods used in practical implementations of MC-CDMA. Te combination of MC-CDMA and index modulation (IM) has led to the development of a recent method called IM-MC-CDMA [13][14][15]. Tis method transmits data bits utilizing the indices of spreading codes.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Nonlinear Distortion and Interference in MC-CDMA Receivers Employing Deep Neural Networks

Ravikumar,

Bagadi,

Rushendrababu

et al. 2023

Mobile Information Systems

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Systems that employ multicarrier code division multiple access, commonly known as MC-CDMA, produce outstanding results in terms of both the performance of the system as a whole and the efficiency with which it uses the spectrum. However, multiple access strategies are susceptible to interference despite their high spectrum efficiency. This work aims to reduce multiple access interference (MAI) by developing an MC-CDMA receiver. When MC-CDMA deteriorates nonlinearly, standard receivers, namely, zero forcing (ZF), maximal ratio combining (MRC), minimum mean square error (MMSE), and equal gain combining (EGC), are unable to cancel MAI. Neural network (NN) receivers are a better option due to their nonlinear nature. Based on the simulation results, the suggested deep neural network- (DNN-)based schemes outperform the current baselines in terms of error handling and usability. This research explores the viability and effectiveness of a DNN-based receiver designed for MC-CDMA with nonlinearity degradations. The focus of this research is on MC-CDMA.

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MC–CDMA receiver design using recurrent neural networks for eliminating multiple access interference and nonlinear distortion

Cited by 19 publications

References 22 publications

Enhancement of Precise Underwater Object Localization

Enhancement of Precise Underwater Object Localization

Intelligent Energy Management for Distributed Power Plants and Battery Storage

Optimizing Nonlinear Distortion and Interference in MC-CDMA Receivers Employing Deep Neural Networks

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