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Bit-Metric Decoding Rate in Multi-User MIMO Systems: Applications
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DOI：10.1109/TWC.2022.3222743.png)
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Abstract
En 中文
This is the second part of a two-part paper that focuses on link-adaptation (LA) and physical layer (PHY) abstraction for multi-user MIMO (MU-MIMO) systems with non-linear receivers. The first part proposes a new metric, called bit-metric decoding rate (BMDR) for a detector, as being the equivalent of post-equalization signal-to-interference-noise ratio (SINR) for non-linear receivers. Since this BMDR does not have a closed form expression, a machine-learning based approach to estimate it effectively is presented. In this part, the concepts developed in the first part are utilized to develop novel algorithms for LA, dynamic detector selection from a list of available detectors, and PHY abstraction in MU-MIMO systems with arbitrary receivers. Extensive simulation results that substantiate the efficacy of the proposed algorithms are presented.
Keywords:
Bit-metric decoding rate (BMDR)
convolutional neural network (CNN)
linear minimum mean square error (LMMSE)
link-adaptation (LA)
K-best detector
multi-user MIMO (MU-MIMO)
orthogonal frequency division multiplexing (OFDM)
physical layer (PHY) abstraction
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W