Digital signal processing

Harnessing Multi-modal AI and Machine Learning for Next-Generation 6G Networks

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B9 L2 R2325

This seminar explores how multi-modal AI and large language models can optimize future 6G wireless networks by integrating diverse data sources to enhance reliability, efficiency, and overall performance.

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Key Enabling Techniques and Practical Considerations for Signal and Information Processing in 5G Cellular Network

Chung-An Shen, Associate Professor, National Taiwan University of Science and Technology

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B9 L2 H2

Abstract The 5th Generation (5G) wireless communication provides considerably enhanced user experiences and offers possibilities of more intriguing applications. In order to fulfil the requirements of 5G like low-latency, energy efficient, and high reliability, many enabling technologies from the physical layer signal processing to the design of network architecture are proposed which all increase the demand for more computing capabilities. This talk gives an overview of the practical considerations, challenges, and techniques for the 5G cellular network. In particular, this talk contains two

Designing, Synthesizing, and Automating Domain-Specific Accelerators

Jian Weng, PhD student, Computer Science Department, UCLA

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KAUST

In this talk, I will first discuss how my research democratizes the accelerator designs and unifies the hardware/software innovations by automating the accelerator design process under a unified programming paradigm. By taking advantage of the compiler’s awareness of the program behaviors that profit from hardware specialization, accelerators can be automatically synthesized by searching through a well-defined design space. These automatically designed accelerators achieve comparable cost/performance efficiency compared with prior handcrafted designs. In the rest of the talk, I will also cover how this work inspires me to develop techniques to accelerate emerging application domains by orders-of-magnitude speedup, including digital signal processing and DNN inference, and how I take advantage of this work to revolutionize the FPGA programming paradigm.

Xinyu Zhou

Xinyu Zhou was a Visiting B.S. Student in the CTL. Then, he joined University of Maryland, USA as a PhD Student. Selected Publications Xinyu Zhou, Zouheir Rezki, Basel Alomairy and Mohamed-Slim Alouini, “Achievable Rates of Secure Transmission in Gaussian MISO Channel with Imperfect Main Channel Estimation” with full paper version submitted to IEEE Transactions on Wireless Communications and conference version submitted to IEEE CNS. Education Research internship in Electrical Engineering department, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (September

Tarig Ballal Khidir Ahmed

Research Scientist Laboratory for Nanomedicine, Computational Bioscience Research Center King Abdullah University of Science and Technology | KAUST

Somayah Shafa-amri

Visiting Student at King Abdullah University of Science and Technology (KAUST)

Mohammad F. Hashem

Mohamed Saadeldin

Postdoctoral Research Fellow, Sensors
Postdoctoral Research Fellow, Sensors Lab at King Abdullah University of Science and Technology (KAUST), Assistant Professor, University College Dublin .

Asmaa Abdallah

Ahmed Hussain

Ahmed Hussain, (Member, IEEE) received his B.E. degree in Avionics Engineering from NUST University, Islamabad, Pakistan, in 2014 and his M.S. in Avionics Engineering from Aerospace and Aviation Campus, Kamra, Pakistan. Currently, he is pursuing a PhD degree at KAUST, Thuwal, KSA. His research interests include deep learning-enabled near-field communication, digital array signal processing, and wireless communication.

Abdulwahab Felemban

Ph.D. Student, Electrical and Computer Engineering CEMSE, King Abdullah University of Science and Technology (KAUST),completed MSc under the supervision of Prof. Tareq Al-Naffouri.