Optimizing Near-ML MIMO Detector for SDR Baseband on Parallel Programmable Architectures
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.
Additional Information
© 2008 EDAA.Additional details
- Eprint ID
- 72261
- Resolver ID
- CaltechAUTHORS:20161122-150927663
- Created
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2016-11-22Created from EPrint's datestamp field
- Updated
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2021-11-11Created from EPrint's last_modified field