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<span style="color:black"><img width="900" height="257" style="width:9.375in;height:2.677in" id="Picture_x0020_1" src="cid:image001.png@01D9BE25.97EADFC0" alt="/var/folders/ct/b7_z22rd4gvbd0r0bvwgf94xl9cxbw/T/com.microsoft.Outlook/WebArchiveCopyPasteTempFiles/cid137626b1-85e2-4c72-92d9-3628b57cd790"><o:p></o:p></span></p>
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<span class="contentpasted0"><span style="font-size:18.0pt;color:#C8102E">Reconfigurable Intelligent Surfaces: Channel Estimation and Applications in Future Wireless Networks</span></span><span style="font-size:18.0pt;color:#C8102E"><o:p></o:p></span></p>
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<strong><span style="font-size:13.5pt;font-family:"Calibri",sans-serif">Eyad Mahmoud Shtaiwi</span></strong><span style="font-size:13.5pt"><o:p></o:p></span></p>
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<span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Arial",sans-serif">July 31, 2023; 9:00 AM - 11:00 AM (CST)</span></span><br>
<span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Location: D N328 Eng Bld 1</span></span><br>
<span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Zoom: </span></span><a href="https://urldefense.com/v3/__https:/uh-edu-cougarnet.zoom.us/j/91275322202?pwd=akhuU1hncVFsZWFYUVRWTEZXOTZZQT09__;!!LkSTlj0I!HCUEaH6P5ReDmxMlJuGVF6GyVnGfOHNKsbF7G3CAKaD_JIwQC3Z69hETHjg0sFfI_GCNJD6cGh4JH7MbcFz3$" target="_blank"><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#C8102E">https://uh-edu-cougarnet.zoom.us/j/99146061274</span></a><br>
<span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Meeting ID: 991 4606 1274</span></span><o:p></o:p></p>
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<strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Committee Chair:</span></strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif"><br>
Ahmed Abdelhadi, Ph.D.<o:p></o:p></span></p>
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<strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Committee Members:</span></strong><span style="font-size:10.5pt;font-family:"Arial",sans-serif"><br>
Zhu Han, Ph.D. | Miao Pan, Ph.D. | Saurabh Prasad, Ph.D. | Naofal Al-Dhahir, Ph.D. | Mérouane Debbah, Ph.D.<o:p></o:p></span></p>
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<b><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E">Abstract:</span></b><b><span style="font-family:"Arial",sans-serif;color:#C8102E"><br>
</span></b><span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Arial",sans-serif">Reconfigurable Intelligent Surfaces (RISs) are planar structures comprising passive, low-cost reflecting elements that efficiently manipulate the electromagnetic
(EM) propagation environment through phase and amplitude adjustments, enhancing wireless system performance. RIS passive beamforming eliminates the need for complex signal processing or RF chains, offering benefits like improved signal-to-interference noise
ratio (SINR), enhanced link quality-of-service (QoS), and reduced energy consumption. However, the limited signal processing capability of RIS-assisted wireless networks presents design challenges. </span></span><span style="font-family:"Arial",sans-serif"><o:p></o:p></span></p>
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<span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Arial",sans-serif">This dissertation comprises two main parts, addressing channel estimation schemes for RIS-assisted MIMO systems and practical RIS implementation to optimize MIMO Radar
and communication coexistence systems, respectively. In the first part, we introduce an innovative algorithm for estimating the composite channel, separate </span></span>RIS-based<span class="contentpasted0"><span style="font-size:10.5pt;font-family:"Arial",sans-serif"> channels,
and the direct channel within the RIS-assisted system. By dividing the RIS surface into smaller sub-RIS units and controlling the phase shifts, we successfully estimate the overall channel. Additionally, we propose a straightforward passive pilot sequence
scheduling scheme to jointly adjust phase shift coefficients. Next, we present a two-stage channel estimation approach for RIS-assisted mmWave communication systems. The first stage identifies active users and efficiently estimates their channel parameters
using sparsity. The second stage utilizes partial Channel State Information (CSI) and spatial-temporal correlation to estimate inactive users' channel coefficients, adopting a spatial-temporal-spectral (STS) framework based on convolutional neural networks
(CNNs). The second part addresses enhancing a Multiple-Input Multiple-Output (MIMO) Radar and Communication Coexistence (RCC) system using RIS deployment. To mitigate interference in a Multi-User (MU)-MIMO RCC setup, we propose a strategic RIS deployment scheme,
leveraging their passive beamforming capabilities. This dissertation provides insights into RIS-assisted MIMO channel estimation and practical RIS implementation for wireless system design, advancing RIS-assisted wireless communication across various applications.</span></span><o:p></o:p></p>
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