
Open access
Date
2016Type
- Conference Paper
ETH Bibliography
no
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Abstract
We investigate the performance of mismatched data detection in large multiple-input multiple-output (MIMO) systems, where the prior distribution of the transmit signal used in the data detector differs from the true prior. To minimize the performance loss caused by this prior mismatch, we include a tuning stage into our recently-proposed large MIMO approximate message passing (LAMA) algorithm, which allows us to develop mismatched LAMA algorithms with optimal as well as sub-optimal tuning. We show that carefully-selected priors often enable simpler and computationally more efficient algorithms compared to LAMA with the true prior while achieving near-optimal performance. A performance analysis of our algorithms for a Gaussian prior and a uniform prior within a hypercube covering the QAM constellation recovers classical and recent results on linear and non-linear MIMO data detection, respectively. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000455411Publication status
publishedExternal links
Book title
2016 IEEE International Symposium on Information Theory (ISIT)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
09695 - Studer, Christoph / Studer, Christoph
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ETH Bibliography
no
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