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dc.contributor.advisorCarrasco Álvarez, Roberto
dc.contributor.authorCalderón Rico, Rodrigo
dc.date.accessioned2020-07-26T18:50:01Z-
dc.date.available2020-07-26T18:50:01Z-
dc.date.issued2018-01-24
dc.identifier.urihttps://hdl.handle.net/20.500.12104/81221-
dc.identifier.urihttps://wdg.biblio.udg.mx
dc.description.abstractContemporary wireless communications systems that furnish services to multiple usersatthesame timearecomplex. Theenvironmentwherethey operatebecomesdifficulttoadministerwiththeaggregatednoise,anduser mobility while balancing the system’s limited resources, i.e., the electromagnetic spectrum space. Asnewmanifoldcommunicationstandardsarisetocopewiththeuser’s and industry’s needs such as high mobility, long battery duration, multirateapplications,andmulti-standardmanagementtheneedtomakeuseof more intelligent systems is inevitable. Hence, developing highly flexible communication algorithms across the whole transceiver design is desired to fulfill those needs. The Cognitive Radios (CR’s) can help to alleviate these problems with its high capacity to adapt and handle access to licensed and unlicensed (secondary) users. To be efficient in the adaptation to different transmission scenarios, sensing the spectrum together with channel estimation are the cornerstones to build up valuable communicationslinks. Furthermore,theCR’sheavilydependsontheobservation of the channel state quality to decide, learn and act. Both, sensing and channel estimation are some of the most challenging areas and some of the most important to be investigated. Traditional approaches to performing spectrum sensing and channel estimation on quasi-static scenarios have been developed throughout digital communications history ending-up on good results to improve the recovery accuracy of the original signal at the receiver end, over several scenario conditions. Interestingly, in the case of CR’s radios, they have to be aware of their location and supervise their electromagnetic environment to be able to adapt the CR’s transmission techniques optimally. In thisfashion,thechannelstateinformation(CSI)feedbackhasasubstantial impact on the complete communications system. With the next step in radio evolution on the horizon, CR’s will create devices to help to make the highest efficient use of the radio spectrum, therefore designing and implementing the best in class in channel estimation algorithms that contribute to the cognitive radio’s optimal operative state will be needed. This thesis presents a proposal on the design of the pilot allocation for the discrete channel estimation over transmission blocks on a multipleuser and frequency selective channel. The adopted the OFDM modulation scheme allows to cope with signal errors due to the high mobility, inter-symbol interference (ISI) and inter-carrier interference (ICI) effects. Furthermore,theproposedmethoddesignsthepilotpatternsequencewith the use of wavelets as a technique to decompose the spectrum and identify most affected bands (by noise and interference) and place more pilots tonesinthosesubcarrierstoimproveaccuracyontheCSI.Therefore,minimizingtheerrorontheestimatedchannel. Havingtheimprovedpilottone sequence information at the receiver a less compute demanding method can be devised. It is demonstrated throughout statistical simulations the effectiveness of the proposed algorithm, such that the SNR loss on BER is minimum when compared with a system that has complete knowledge of the CSI.
dc.description.tableofcontents1 Introduction. 1 General Goals . 2 Specific Goals. 3 Hypothesis and Motivation. 3.1 Hypothesis. 2 WirelessCommunicationsSystemsBackground. 1 Background On Electromagnetic Waves’s Nature. 1.1 The aftermath of Maxwell’s Epoch: 1890 Onward. 1.2 The use of Physics to establish communication. 2 The Birth of Wireless Communications. 3 A New Understanding of Information. 4 Modeling Communications Systems. 4.1 Communications Viewed as Adaptive Systems. 4.2 Adverse Effects On Wireless Communications. 4.2.1 Additive White Gaussian Noise (AWGN). 4.2.2 Channel Fading. 4.2.3 Frequency Selectivity. 4.2.4 Time Variance. 3 CognitiveRadio. 1 Software Defined Radio. 1.1 Definition. 1.2 SDR Architecture. 1.2.1 Software Communications Architecture (SCA). 1.2.2 SDR Platforms. 2 Cognitive Radio: Scene. 2.1 Definition. 2.2 Motivations and Applications. 2.3 Foundations. 2.4 Architecture. 2.4.1 Flexible Secondary Use of Radio Spectrum. 2.4.2 Radio and User Knowledge in the Architecture. 2.4.3 The Architecture Plot, Cognition Components and the Cognition Cycle. 4.4 Flexible Functions of the Component Architecture. 2.4.5 Inference on Icr. 2.5 Building an iCR. 2.5.1 Moving SDR Design to iCR Realization. 2.5.2 iCR Conception Dictums. 2.5.3 Ideal SDR Architecture. 2.5.4 Software Tunable Analog Radio Components. 2.6 Challenges on Cognitive Radio. 2.6.1 Dynamic Spectrum Access. 2.6.2 The Side Effects On Accessing Fragmented Spectrum Resources. 4 SpectrumManagementandChannelEstimation. 1 Use of Codes for Dynamic Spectrum Access. 2 Medium Access Channel Strategies. 2.0.1 Cognitive MAC System Model. 2.0.2 Cognitive Radio Spectral Utilization. 2.1 Markov Modeling, Random Access Modeling. 2.1.1 Accessing Model plus Etiquette. 2.1.2 Markov and Random Access Modeling. 5 OFDM-BasedSystemForCognitiveRadios. 1 Multicarrier Transmission. 1.1 Principles. 1.1.1 Transmission-Reception Concept. 1.1.2 Interference. 2 OFDM System Model. 2.1 OFDM Transmitter. 2.2 OFDM Receiver. 2.3 OFDM Cyclic Prefix. 3 OFDM Merits for iCR. 4 OFDM Challenges for iCR. 6 ChannelEstimationOnOFDM-BasediCRSystems. 1 Channel Estimation for OFDM. 1.1 System Model. 1.2 The LS Channel Estimation Method. 2 The Complexity of Optimal Solution. 3 Initial Attempts. 7 LowComplexityTrade-OffviaWaveletAnalysis. 1 Wavelet Transform Analysis. 1.1 Non-Stationary Signal Analysis. 1.2 Continuous Wavelet Transform. 1.2.1 Wavelet Reasoning and Synthesis. 1.3 Discrete Wavelet Transform. 1.4 Spectrogram vs Scalogram. 2 CSI Decomposition with DWT for Pilot Allocation. 8 Results 79 1 Remarks. 9 Conclusions 87 1 Future Research On Channel Estimation. 1.1 Cooperative Channel Estimation and Adaptive Equalization. 1.2 Beyond Pilot-Based Channel Estimation. 1.2.1 ChaosSequencesOnCognitiveCommunicationsSystems. 1.3 Compressed Sensing On Cognitive Communications Systems.
dc.formatapplication/PDF
dc.language.isospa
dc.publisherBiblioteca Digital wdg.biblio
dc.publisherUniversidad de Guadalajara
dc.rights.urihttps://www.riudg.udg.mx/info/politicas.jsp
dc.subjectPatrones De Pilotos
dc.subjectRadios Cognitivos.
dc.titleEL DISEÑO DE PATRONES DE PILOTOS PARA LA ESTIMACIÓN DE CANAL PARA RADIOS COGNITIVOS
dc.typeTesis de Doctorado
dc.rights.holderUniversidad de Guadalajara
dc.rights.holderCalderón Rico, Rodrigo
dc.coverageGUADALAJARA, JALISCO
dc.type.conacytDoctoralThesis-
dc.degree.nameDOCTORADO EN CIENCIAS DE LA ELECTRONICA Y LA COMPUTACION CON ORIENTACIONES-
dc.degree.departmentCUCEI-
dc.degree.grantorUniversidad de Guadalajara-
dc.rights.accessopenAccess-
dc.degree.creatorDOCTOR EN CIENCIAS DE LA ELECTRONICA Y LA COMPUTACION CON ORIENTACIONES-
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