Acceleration of Massive MIMO algorithms for Beyond 5G Baseband processing
2023 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE credits
Student thesis
Abstract [en]
As the world becomes more globalised, user equipment such as smartphones and Internet of Things devices require increasingly more data, which increases the demand for wireless data traffic. Hence, the acceleration of next-generational networks (5G and beyond) focuses mainly on increasing the bitrate and decreasing the latency. A crucial technology for 5G and beyond is the massive MIMO. In a massive MIMO system, a detector processes the received signals from multiple antennas to decode the transmitted data and extract useful information. This has been implemented in many ways, and one of the most used algorithms is the Zero Forcing (ZF) algorithm. This thesis presents a novel parallel design to accelerate the ZF algorithm using the Cholesky decomposition. This is implemented on a GPU, written in the CUDA programming language, and compared to the existing state-of-the-art implementations regarding latency and throughput. The implementation is also validated from a MATLAB implementation. This research demonstrates promising performance using GPUs for massive MIMO detection algorithms. Our approach achieves a significant speedup factor of 350 in comparison to a serial version of the implementation. The throughput achieved is 160 times greater than a comparable GPU-based approach. Despite this, our approach reaches a 2.4 times lower throughput than a solution that employed application-specific hardware. Given the promising results, we advocate for continued research in this area to further optimise detection algorithms and enhance their performance on GPUs, to potentially achieve even higher throughput and lower latency.
Place, publisher, year, edition, pages
2023. , p. 39
Keywords [en]
embedded systems, massive MIMO, GPU, massive MIMO detection, Zero-Forcing algorithm, Cholesky decomposition, CUDA, 5G and Beyond
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:hh:diva-51184OAI: oai:DiVA.org:hh-51184DiVA, id: diva2:1778903
Subject / course
Computer science and engineering
Educational program
Computer Science and Engineering, 300 credits
Presentation
2023-05-31, D315, Kristian IV:s väg 3, Halmstad, 13:25 (English)
Supervisors
Examiners
Note
Our examiner Mahdi wants to wait six months before the thesis is published.
2023-07-032023-07-032024-01-03Bibliographically approved