Efficient High-level Synthesis Implementation of massive MIMO Processing on RFSoC

Abstract

Massive multiple-input multiple-output (MIMO) refers to a wireless access technology that equips base station (BS) with hundreds to thousands of antennas to serve tens of user equipment (UE) in the same time-frequency resource. These extensive antennas improve spectral and energy efficiency, but the detection algorithms tend to be more complex with operations, multiplications, and inversions on larger size matrix.
The traditional register transfer level (RTL) design process is time-consuming and risks starting over if the proposed architecture does not meet the requirements. High-level synthesis (HLS) addresses this issue by employing a higher level of abstraction and providing an error-less path to generate the RTL code from user-defined architecture. However, more attention is needed during implementation as coding at a too high level might deteriorate the design quality, leading to area overhead and down the throughput.
In this thesis, an efficient HLS implementation of massive MIMO processing is demonstrated and optimized for higher throughput and less area occupation. The design is written in C++ and synthesized by Mentor Catapult HLS. Firstly, the baseline implementation with all default settings is synthesized and simulated, and then loop and memory optimization is applied. The result shows that correct coding style and well-designed constraints improve the performance to a large extent.

Popular Abstract

To meet the demanding user expectation on network capability in the 5G era, engineers adopted a fundamentally new approach to communicate between users and their base station. Compared with the multiple-input multiple-output (MIMO) technology employed in 4G, the new solution utilizes much more antennas in the base station, and that's why it's named "massive MIMO".
Why does the number of antennas on the base station side increase from 4G to 5G? The designer needs to enhance the receiving antenna power for more reliable transmission. You may think of increasing transmitted power. But it is regrettable that due to technical limitations and related regulations, designers cannot increase the transmitting power infinitely; also, the antenna gain is limited by current technology. You may also suggest placing the transmitter and receiver closer. Mobile communication carriers won't want to do this because it will cost more money to build new base stations. Thanks to talented engineers who came up with the "beamforming" concept. It is a solution that adaptively adjusts the radiation graph of the antenna array according to a specific scene. Metaphorically speaking, a signal antenna transmission is like an electric bulb that lights up the whole room, while beamforming is like a flashlight where the light can be intelligently converged to the target location. And also, the number of flashlights can be constructed according to the number of targets. The more antennas in a communication system, the more obvious beamforming can play.
However, this improvement does not come without a price. For the hardware engineer, more base station antennas mean more register-level operations. Hence, the frequently used register transfer level (RTL) language programming is time-consuming and complex. This issue is solved with the help of high-level synthesis (HLS) that can transform the C++ code to RTL code. In this thesis, a massive MIMO processing system is implemented with HLS and further optimized to have a faster and smaller design.

• author: Cheng, Sijia
• supervisor: Liang Liu; Steffen Malkowsky
• organization: Department of Electrical and Information Technology
• course: EITM02 20221
• year: 2022
• type: H2 - Master's Degree (Two Years)
• subject: Technology and Engineering
• report number: LU/LTH-EIT 2022-860
• language: English
• id: 9077530
• date added to LUP: 2022-03-25 14:52:41
• date last changed: 2022-03-25 14:52:41