Abstract
In this paper, we introduce a new detection algorithm for large-scale wireless systems, referred to as post sparse error detection (PSED) algorithm, that employs a sparse error recovery algorithm to refine the estimate of a symbol vector obtained by the conventional linear detector. The PSED algorithm operates in two steps: 1) sparse transformation converting the original non-sparse system into the sparse system whose input is an error vector caused by the symbol slicing and 2) estimation of the error vector using the sparse recovery algorithm. From the asymptotic mean square error (MSE) analysis and empirical simulations performed on large-scale systems, we show that the PSED algorithm brings significant performance gain over classical linear detectors while imposing relatively small computational overhead.
Original language | English |
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Journal | IEEE Transactions on Signal Processing |
DOIs | |
Publication status | Accepted/In press - 2017 Sep 2 |
Externally published | Yes |
Keywords
- compressive sensing
- error correction
- large-scale systems
- linear minimum mean square error
- orthogonal matching pursuit
- Sparse signal recovery
- sparse transformation
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering