000079363 001__ 79363
000079363 005__ 20200716101435.0
000079363 0247_ $$2doi$$a10.1109/TIE.2018.2842780
000079363 0248_ $$2sideral$$a109687
000079363 037__ $$aART-2019-109687
000079363 041__ $$aeng
000079363 100__ $$aGracia Saez, R.
000079363 245__ $$aRF Power Amplifier Linearization in Professional Mobile Radio Communications Using Artificial Neural Networks
000079363 260__ $$c2019
000079363 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079363 5203_ $$aThis paper is focused on the linearization of the radio frequency power amplifier of a professional digital handheld by means of an artificial neural network. The simplicity of the neural network that is used, together with the fact that a feedback path is unnecessary, makes this solution ideal to reduce both the cost of a handheld and its hardware complexity, while fully maintaining its performance. A compensation system is also needed to keep the linearization characteristics of the neural network stable against frequency, temperature, and voltage variations. The whole solution that comprises both the neural network and the compensation system has been implemented in the digital signal processor of a real handheld and afterward fully tested. It has proved to be satisfactory to meet the telecommunication standard requirements in all frequency, temperature, and voltage ranges under consideration while efficient to lower the computational cost of the handheld and to make its internal hardware simpler in comparison with other traditional linearization techniques. The results obtained demonstrate that a neural network can be used to linearize the power amplifiers that are used in transmitters of telecommunication equipment, leading to a significant reduction of both their hardware cost and complexity.
000079363 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/TEC2015-65750-R
000079363 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000079363 590__ $$a7.515$$b2019
000079363 592__ $$a2.911$$b2019
000079363 591__ $$aAUTOMATION & CONTROL SYSTEMS$$b4 / 63 = 0.063$$c2019$$dQ1$$eT1
000079363 593__ $$aComputer Science Applications$$c2019$$dQ1
000079363 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b1 / 64 = 0.016$$c2019$$dQ1$$eT1
000079363 593__ $$aElectrical and Electronic Engineering$$c2019$$dQ1
000079363 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b16 / 266 = 0.06$$c2019$$dQ1$$eT1
000079363 593__ $$aControl and Systems Engineering$$c2019$$dQ1
000079363 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000079363 700__ $$0(orcid)0000-0002-5380-3013$$aMedrano Marques, N.$$uUniversidad de Zaragoza
000079363 7102_ $$15008$$2250$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Electrónica
000079363 773__ $$g66, 4 (2019), 3060-3070$$pIEEE trans. ind. electron.$$tIEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS$$x0278-0046
000079363 8564_ $$s803534$$uhttps://zaguan.unizar.es/record/79363/files/texto_completo.pdf$$yPostprint
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000079363 909CO $$ooai:zaguan.unizar.es:79363$$particulos$$pdriver
000079363 951__ $$a2020-07-16-08:54:39
000079363 980__ $$aARTICLE