High level synthesis strategies for ultra fast and low latency matrix inversion implementation for massive MIMO processing

Sirois, S., Ahmed Ouameur, M. et Massicotte, D. (2022). High level synthesis strategies for ultra fast and low latency matrix inversion implementation for massive MIMO processing. Integration, 82 . pp. 29-36. ISSN 0167-9260 DOI 10.1016/j.vlsi.2021.08.011

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Résumé

In this paper, we discuss the implementation strategies of an explicit matrix inversion technique based on the recursive Gram matrix inversion update (RGMIU) algorithm. These strategies are explicitly chosen to optimize the design for high throughput dictated by the enhanced mobile broad band (eMBB) use case and by the low latency imposed by the ultra-reliable low-latency communications (URLLCs) use case. The RGMIU algorithm is recently proposed to implement the zero forcing (ZF) problem encountered in massive multiple-input multiple-output (MIMO) detection task. We therefore compare and analyse the performance in terms of symbol error rate (SER) against popular implicit and explicit methods such as optimized coordinate descent (OCD), Gauss-Seidel (GS) and Neumann series expansion (NSE) algorithms. To determine the optimal word length, a fixed-point analysis shows that 16 bits are enough to reach a floating-point precision. We, thereafter, use Vivado High-Level Synthesis tools and optimization directives to implement the RGMIU algorithm based on three strategies to infer key insights and design trade-offs from the resource's utilization, latency, throughput and energy efficiency.

Type de document:	Article
Mots-clés libres:	Massive MIMO Recursive gram matrix inversion update (RGMIU) MIMO detection Zero forcing (ZF) Fixed-point analysis High-level synthesis Vivado HLS Throughput Latency
Date de dépôt:	09 mai 2022 20:09
Dernière modification:	31 janv. 2024 05:00
Version du document déposé:	Post-print (version corrigée et acceptée)
URI:	https://depot-e.uqtr.ca/id/eprint/10120

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