Mendeley
TY  _  JOUR
ID  -  2022021934192
TI  -  A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems 
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -   
AU  -  Akhavan Amir
AD  -  دانشگاه صنعتی اصفهان 
AD  -  Tehran university
Y1  -  2022
PY  -  2022
VL  -  40
IS  -  
SP  -  249
EP  -  256
KW  -  Massive MIMO
KW  -   MMSE Detector
KW  -   Error Recovery
KW  -   Compressive Sensing
KW  -   Iteratively Reweighted Least Squares (IRLS) Method.
DO  -  
N2  -  Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods.
UR  - rimag.ir/en/Article/34192
L1  - rimag.ir/en/Article/Download/34192
ER  -  
