TY - JOUR
T1 - Detection of Large-Scale Wireless Systems via Sparse Error Recovery
AU - Choi, Jun Won
AU - Shim, Byonghyo
N1 - Funding Information:
Manuscript received August 8, 2016; revised July 7, 2017; accepted August 22, 2017. Date of publication September 4, 2017; date of current version September 26, 2017. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ami Wiesel. This work was supported in part by the research grant from Qualcomm Incorporated and in part by the Institute for Information & Communications Technology Promotion Grant funded by the Korea government (MSIP) (No. 2015-0-00294) and the Ministry of Education (NRF-2017R1D1A1A09000602). This paper was presented in part at the IEEE Global Telecommunications Conference, Austin, TX, USA, December 2014. (Corresponding author: Byonghyo Shim.) J. W. Choi is with the Department of Electrical Engineering, Hanyang University, Seoul 133-791, South Korea (e-mail: junwchoi@hanyang.ac.kr).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/15
Y1 - 2017/11/15
N2 - In this paper, we propose 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: First, sparse transformation converting the original nonsparse system into the sparse system whose input is an error vector caused by the symbol slicing; and second, the estimation of the error vector using the sparse recovery algorithm. From the asymptotic mean square error analysis and empirical simulations performed on large-scale wireless 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 propose 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: First, sparse transformation converting the original nonsparse system into the sparse system whose input is an error vector caused by the symbol slicing; and second, the estimation of the error vector using the sparse recovery algorithm. From the asymptotic mean square error analysis and empirical simulations performed on large-scale wireless systems, we show that the PSED algorithm brings significant performance gain over classical linear detectors while imposing relatively small computational overhead.
KW - Sparse signal recovery
KW - compressive sensing
KW - error correction
KW - large-scale systems
KW - linear minimum mean square error
KW - orthogonal matching pursuit
KW - sparse transformation
UR - http://www.scopus.com/inward/record.url?scp=85029159112&partnerID=8YFLogxK
U2 - 10.1109/TSP.2017.2749214
DO - 10.1109/TSP.2017.2749214
M3 - Article
AN - SCOPUS:85029159112
VL - 65
SP - 6038
EP - 6052
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 22
M1 - 8025598
ER -