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 |
|---|---|
| Journal | IEEE Transactions on Signal Processing |
| DOIs | |
| Publication status | Accepted/In press - 2017 Sep 2 |
| Externally published | Yes |
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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
Cite this
Detection of Large-Scale Wireless Systems via Sparse Error Recovery. / Choi, Jun Won; Shim, Byonghyo.
In: IEEE Transactions on Signal Processing, 02.09.2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Detection of Large-Scale Wireless Systems via Sparse Error Recovery
AU - Choi, Jun Won
AU - Shim, Byonghyo
PY - 2017/9/2
Y1 - 2017/9/2
N2 - 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.
AB - 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.
KW - compressive sensing
KW - error correction
KW - large-scale systems
KW - linear minimum mean square error
KW - orthogonal matching pursuit
KW - Sparse signal recovery
KW - sparse transformation
UR - http://www.scopus.com/inward/record.url?scp=85029159112&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029159112&partnerID=8YFLogxK
U2 - 10.1109/TSP.2017.2749214
DO - 10.1109/TSP.2017.2749214
M3 - Article
AN - SCOPUS:85029159112
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
ER -