PERFORMANCE ANALYSIS OF FUTURE COMMUNICATIONS SYSTEMS UNDER RESIDUAL HARDWARE IMPAIRMENTS

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dc.contributor.authorTlebaldiyeva, Leila
dc.date.accessioned2021-02-01T05:11:39Z
dc.date.available2021-02-01T05:11:39Z
dc.date.issued2020-04
dc.description.abstractCognitive radio (CR) and millimeter wave (mmWave) communication are two potential technologies for future wireless communication systems to meet ever-increasing consumer data demand. The significant advantage of CR is its ability to improve spectrum utilization by introducing spectrum management paradigms between primary and cognitive users. An even more significant enabling technology for future communications is mmWave communication that offers enormous bandwidth at mmWave frequency bands. Low-grade transceiver hardware is often utilized in modern communication systems to lower the cost of potential networks. The residual hardware distortion noise originating from high rate and low-grade transceiver hardware is a vital parameter to consider while designing reliable systems. This dissertation work pursues to model residual transceiver hardware impairments by using the statistical additive Gaussian model, which is mathematically tractable and can be embedded in complex system configurations. In this thesis, we first develop a system model for a dual-hop decode-and-forward underlay CR relay network operating under residual hardware impairments and derive a closed-form expression for the outage probability performance. Moreover, this work provides useful discussions on the design aspects of wireless communication systems in terms of the outage probability given residual transceiver noise level and fading parameters of channel. Secondly, we study the spectrum sensing technique by employing an improved energy detector (ED) under residual hardware constraints. We present a novel test statistic for improved ED that accounts for residual distortion noise when the fading statistics of the received signal follows the 􀀀 distribution. Moreover, we derive closed-form expressions for the probabilities of detection and false alarm and the area under the receiver operating characteristic curve (AUC) for additive white Gaussian and Nakagami-m fading channels. Our work proposes a new diversity concept of p-order-law combining and p-order-law selecting schemes to combat the adverse effect of residual hardware impairments. Thirdly, our study develops an analytical framework for analog beamforming deviceto- device mmWave communication constrained by residual hardware impairments and other random impairments such as multi-user interference, inter-beam radio frequency (RF) power leakage, and imperfect channel state information (CSI). We perform in-depth outage probability and ergodic capacity analysis for the proposed system model. Finally, we propose to implement a maximum sub-array transmission (MST) scheme built on a hybrid beamforming structure that enables multi-user communication and high outage probability and ergodic capacity performance. The MST diversity suffers from RF power leakage and transceiver distortion noise that are addressed in this work. The hardware impaired communication systems transmit at considerably lower rates than the ideal ones, and, therefore, our research emphasizes the importance of residual distortion modeling.en_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/5277
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectCognitive radioen_US
dc.subjectCRen_US
dc.subjectmillimeter waveen_US
dc.subjectmmWaveen_US
dc.subjectEDen_US
dc.subjectenergy detectoren_US
dc.subjectAUCen_US
dc.subjectchannel state informationen_US
dc.subjectCSIen_US
dc.subjectmaximum sub-array transmissionen_US
dc.subjectMSTen_US
dc.subjectResearch Subject Categories::TECHNOLOGYen_US
dc.titlePERFORMANCE ANALYSIS OF FUTURE COMMUNICATIONS SYSTEMS UNDER RESIDUAL HARDWARE IMPAIRMENTSen_US
dc.typePhD thesisen_US
workflow.import.sourcescience

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