CaltechAUTHORS
  A Caltech Library Service

Optimizing Near-ML MIMO Detector for SDR Baseband on Parallel Programmable Architectures

Li, Min and Bougard, Bruno and Yu, Weiyu and Novo, David and Van Der Perre, Liesbet and Catthoor, Francky (2008) Optimizing Near-ML MIMO Detector for SDR Baseband on Parallel Programmable Architectures. In: DATE '08 Proceedings of the conference on Design, automation and test in Europe. European Design and Automation Association , Dresden, Germany, pp. 444-449. ISBN 978-3-9810801-3-1. https://resolver.caltech.edu/CaltechAUTHORS:20161122-150927663

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20161122-150927663

Abstract

ML and near-ML MIMO detectors have attracted a lot of interest in recent years. However, almost all the reported implementations are delivered in ASICs or FPGAs. Our contribution is optimizing the near-ML MIMO detector for parallel programmable architectures, such as those with ILP and DLP features. In the proposed SSFE (Selective Spanning with Fast Enumeration), architecture-friendliness is explicitly introduced from the very beginning of the design flow. Importantly, high level algorithmic transformations make the dataflow pattern and structure fit architecture-characteristics very well. We enable abundant vector-parallelism with highly regular and deterministic dataflow in the SSFE; memory rearrangements, shuffling and non-predictable dynamism are all elaborately excluded. Hence, the SSFE can be easily parallelized and efficiently mapped onto ILP and DLP architectures. Furthermore, to fine-tune the SSFE on parallel architectures, extensive pre-compiler transformations are applied with the help of the application-level information. These optimize not only computation-operations but also address-generations and memory-accesses. Experiments show that the SSFE brings very efficient resource-utilizations on real-life VLIW architectures. Specifically, with the SSFE the percentage of NOPs instructions on VLIW is below 1%, even better than that achieved by the software-pipelined FFT. To the best of our knowledge, this is the first reported work about comprehensive optimizations of near-ML MIMO detectors for parallel programmable architectures.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1145/1403375.1403482DOIArticle
http://dl.acm.org/citation.cfm?doid=1403375.1403482PublisherArticle
Additional Information:© 2008 EDAA.
Record Number:CaltechAUTHORS:20161122-150927663
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20161122-150927663
Official Citation:Min Li, Bruno Bougard, Weiyu Xu, David Novo, Liesbet Van Der Perre, and Francky Catthoor. 2008. Optimizing near-ML MIMO detector for SDR baseband on parallel programmable architectures. In Proceedings of the conference on Design, automation and test in Europe (DATE '08). ACM, New York, NY, USA, 444-449. DOI=http://dx.doi.org/10.1145/1403375.1403482
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72261
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:22 Nov 2016 23:40
Last Modified:03 Oct 2019 16:16

Repository Staff Only: item control page