Full Text:   <2482>

Summary:  <1443>

CLC number: TP391.4

On-line Access: 2018-10-24

Received: 2018-05-16

Revision Accepted: 2018-09-24

Crosschecked: 2019-10-10

Cited: 0

Clicked: 6832

Citations:  Bibtex RefMan EndNote GB/T7714


Maqsood H. Shah


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.3 P.465-475


An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems

Author(s):  Maqsood H. SHAH, Xiao-yu DANG

Affiliation(s):  College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Corresponding email(s):   maqsood@nuaa.edu.cn, dang@nuaa.edu.cn

Key Words:  Multiple-input multiple-output, Space-time block code, Maximum likelihood, Automatic modulation classification, Zero-forcing

Maqsood H. SHAH; Xiao-yu DANG. An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(3): 465-475.

@article{title="An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems",
author="Maqsood H. SHAH; Xiao-yu DANG",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems
%A Maqsood H. SHAH
%A Xiao-yu DANG
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 3
%P 465-475
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800306

T1 - An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems
A1 - Maqsood H. SHAH
A1 - Xiao-yu DANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 3
SP - 465
EP - 475
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800306

A low-complexity likelihood methodology is proposed for automatic modulation classification of orthogonal space-time block code (STBC) based multiple-input multiple-output (MIMO) systems. We exploit the zero-forcing equalization technique to modify the typical average likelihood ratio test (ALRT) function. The proposed ALRT function has a low computational complexity compared to existing ALRT functions for MIMO systems classification. The proposed approach is analyzed for blind channel scenarios when the receiver has imperfect channel state information (CSI). Performance analysis is carried out for scenarios with different numbers of antennas. Alamouti-STBC systems with 22 and 21 and space-time transmit diversity with a 44 transmit and receive antenna configuration are considered to verify the proposed approach. Some popular modulation schemes are used as the modulation test pool. Monte-Carlo simulations are performed to evaluate the proposed methodology, using the probability of correct classification as the criterion. Simulation results show that the proposed approach has high classification accuracy at low signal-to-noise ratios and exhibits robust behavior against high CSI estimation error variance.


Maqsood H.SHAH,党小宇



Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Alamouti SM, 1998. A simple transmit diversity technique for wireless communication. IEEE J Sel Areas Commun, 16(8):1451-1458.

[2]Ali A, Fan YY, 2017. Unsupervised feature learning and automatic modulation classification using deep learning model. Phys Commun, 25:75-84.

[3]Aslam MW, Zhu ZC, Nandi AK, 2012. {Automatic modulation classification using combination of genetic programming and KNN}. IEEE Trans Wirel Commun, 11(8):2742-2750.

[4]Bahloul MR, Yusoff MZ, Abdel-Aty AH, et al., 2016. Modulation classification for MIMO systems: state of the art and research directions. Chaos Sol Fract, 89:497-505.

[5]Ben-Israel A, Greville TNE, 2003. Generalized Inverses: Theory and Applications. Springer, New York, USA.

[6]Beres E, Adve R, 2007. Blind channel estimation for orthogonal STBC in MISO systems. IEEE Trans Veh Technol, 56(4):2042-2050.

[7]Choqueuse V, Azou S, Yao K, et al., 2009. Modulation recognition for MIMO communications. Milit Tech Acad Rev, 19(2):183-196.

[8]Choqueuse V, Marazin M, Collin L, et al., 2010. Blind recognition of linear space-time block codes: a likelihood-based approach. IEEE Trans Signal Process, 58(3): 1290-1299.

[9]Cormen TH, Leiserson CE, Rivest RL, et al., 2009. Introduction to Algorithms. MIT Press, Massachusettes, USA.

[10]Courrieu P, 2008. Fast computation of Moore-Penrose inverse matrices. Neur Inform Process Lett Rev, 8(2):25-29.

[11]Dobre OA, Abdi A, Bar-Ness Y, et al., 2007. Survey of automatic modulation classification techniques: classical approaches and new trends. IET Commun, 1(2):137-156.

[12]Eldemerdash YA, Marey M, Dobre OA, et al., 2013. Fourth-order statistics for blind classification of spatial multiplexing and Alamouti space-time block code signals. IEEE Trans Commun, 61(6):2420-2431.

[13]Hassan K, Nsiala Nzéza C, Berbineau M, et al., 2010. Blind modulation identification for MIMO systems. IEEE Global Telecommunications Conf, p.1-5.

[14]Hassan K, Dayoub I, Hamouda W, et al., 2012. Blind digital modulation identification for spatially-correlated MIMO systems. IEEE Trans Wirel Commun, 11(2):683-693.

[15]Huang CY, Polydoros A, 1995. Likelihood methods for MPSK modulation classification. IEEE Trans Commun, 43(2-4):1493-1504.

[16]Jalloul LMA, Rohani K, Kuchi K, et al., 1999. Performance analysis of CDMA transmit diversity methods. 50th Vehicular Technology Conf, p.1326-1330.

[17]Solving Laplacian systems in logarithmic space. https://arxiv.org/abs/1608.01426

[18]Luo MG, Li LP, Tang B, 2012. A blind modulation recognition algorithm suitable for MIMO-STBC systems. IEEE 12th Int Conf on Computer and Information Technology, p.271-276.

[19]Marey M, Dobre OA, 2015. Blind modulation classification for Alamouti STBC system with transmission impairments. IEEE Wirel Commun Lett, 4(5):521-524.

[20]Mühlhaus MS, Öner M, Dobre OA, et al., 2012. Automatic modulation classification for MIMO systems using fourth-order cumulants. IEEE Vehicular Technology Conf, p.1-5.

[21]Nandi AK, Azzouz EE, 1997. Modulation recognition using artificial neural networks. Signal Process, 56(2):165-175.

[22]Nandi AK, Azzouz EE, 1998. Algorithms for automatic modulation recognition of communication signals. IEEE Trans Commun, 46(4):431-436.

[23]Niu MB, Cheng JL, Holzman JF, 2014. Alamouti-type STBC for atmospheric optical communication using coherent detection. IEEE Photon J, 6(1):7900217.

[24]Quan Z, Ribeiro MV, 2011. A low cost STBC-OFDM system with improved reliability for power line communications. IEEE Int Symp on Power Line Communications and Its Applications, p.261-266.

[25]Salam AOA, Sheriff RE, Al-Araji SR, et al., 2015. Automatic modulation classification in cognitive radio using multiple antennas and maximum-likelihood techniques. 15th Int Conf on Computer and Information Technology, p.1-5.

[26]Saurabh N, 2015. Improving the Performance of Moore-Penrose Pseudo-Inverse for a Graph’s Laplacian Using GPU. MS Thesis, Amsterdam, the Netherlands.

[27]Shi M, Bar-Ness Y, Su W, 2007. STC and BLAST MIMO modulation recognition. IEEE Global Telecommunications Conf, p.3034-3039.

[28]Sills JA, 1999. Maximum-likelihood modulation classification for PSK/QAM. Military Communications Conf, p.217-220.

[29]Swami A, Sadler BM, 2000. Hierarchical digital modulation classification using cumulants. IEEE Trans Commun, 48(3):416-429.

[30]Tarokh V, Jafarkhani H, Calderbank AR, 1999. Space-time block codes from orthogonal designs. IEEE Trans Inform Theory, 45(5):1456-1467.

[31]Thao PC, Le Khoa D, Tu NT, et al., 2016. Optical MIMO DCO-OFDM wireless communication systems using STBC in diffuse fading channels. Proc 3rd> National Foundation for Science and Technology Development Conf on Information and Computer Science, p.141-146.

[32]Tseng SM, Liao CY, 2014. Distributed orthogonal and quasi-orthogonal space-time block code with embedded AAF/DAF matrix elements in wireless relay networks with four relays. Wirel Pers Commun, 75(2):1187-1198.

[33]Tseng SM, Lee TL, Ho YC, et al., 2017. Distributed space-time block codes with embedded adaptive AAF/DAF elements and opportunistic listening for multihop power line communication networks. Int J Commun Syst, 30(1):e2950.

[34]Turan M, Öner M, Çırpan HA, 2016. Joint modulation classification and antenna number detection for MIMO systems. IEEE Commun Lett, 20(1):193-196.

[35]Veljovic Z, Urosevic U, 2017. New solutions for cooperative relaying implementation of OSTBC with 3/4 code rate. Wirel Pers Commun, 92(1):51-61.

[36]Wei W, Mendel JM, 2000. Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Trans Commun, 48(2):189-193.

[37]Zhu ZC, Aslam MW, Nandi AK, 2011. Support vector machine assisted genetic programming for MQAM classification. Int Symp on Signals, Circuits and Systems, p.1-6.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE