Abstract
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 2 × 2 and 2 × 1 and space-time transmit diversity with a 4 × 4 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.
Similar content being viewed by others
References
Alamouti SM, 1998. A simple transmit diversity technique for wireless communication. IEEE J Sel Areas Commun, 16(8): 1451–1458. https://doi.org/10.1109/49.730453
Ali A, Fan YY, 2017. Unsupervised feature learning and automatic modulation classification using deep learning model. Phys Commun, 25: 75–84. https://doi.org/10.1016/j.phycom.2017.09.004
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. https://doi.org/10.1109/TWC.2012.060412.110460
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. https://doi.org/10.1016/jxhaos.2016.02.029
Ben-Israel A, Greville TNE, 2003. Generalized Inverses: Theory and Applications. Springer, New York, USA.
Beres E, Adve R, 2007. Blind channel estimation for orthogonal STBC in MISO systems. IEEE Trans Veh Technol, 56(4): 2042–2050. https://doi.org/10.1109/TVT.2007.897639
Choqueuse V, Azou S, Yao K, et al., 2009. Modulation recognition for MIMO communications. Milit Tech Acad Rev, 19(2): 183–196.
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. https://doi.org/10.1109/TSP.2009.2036062
Cormen TH, Leiserson CE, Rivest RL, et al., 2009. Introduction to Algorithms. MIT Press, Massachusettes, USA.
Courrieu P, 2008. Fast computation of Moore-Penrose inverse matrices. Neur Inform Process Lett Rev, 8(2): 25–29.
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. https://doi.org/10.1049/iet-com:20050176
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. https://doi.org/10.1109/TCOMM.2013.042313.120629
Hassan K, Nsiala Nzáza C, Berbineau M, et al., 2010. Blind modulation identification for MIMO systems. IEEE Global Telecommunications Conf, p.1–5.
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.
Huang CY, Polydoros A, 1995. Likelihood methods for MPSK modulation classification. IEEE Trans Commun, 43(2-4): 1493–1504. https://doi.org/10.1109/26.380199
Jalloul LMA, Rohani K, Kuchi K, et al., 1999. Performance analysis of CDMA transmit diversity methods. 50th Vehicular Technology Conf, p.1326–1330. https://doi.org/10.1109/VETECF.1999.801478
Le Gall F, 2016. Solving Laplacian systems in logarithmic space. https://doi.org/1608.01426
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. https://doi.org/10.1109/CIT.2012.77
Marey M, Dobre OA, 2015. Blind modulation classification for Alamouti STBC system with transmission impairments. IEEE Wirel Commun Lett, 4(5): 521–524. https://doi.org/10.1109/LWC.2015.2451174
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. https://doi.org/10.1109/VTCFall.2012.6399061
Nandi AK, Azzouz EE, 1997. Modulation recognition using artificial neural networks. Signal Process, 56(2): 165–175. https://doi.org/10.1016/S0165-1684(96)00165-X
Nandi AK, Azzouz EE, 1998. Algorithms for automatic modulation recognition of communication signals. IEEE Trans Commun, 46(4): 431–436. https://doi.org/10.1109/26.664294
Niu MB, Cheng JL, Holzman JF, 2014. Alamouti-type STBC for atmospheric optical communication using coherent detection. IEEE Photon J, 6(1): 7900217. https://doi.org/10.1109/JPHOT.2014.2302807
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. https://doi.org/10.1109/ISPLC.2011.5764404
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. https://doi.org/10.1109/cit/iucc/dasc/picom.2015.3
Saurabh N, 2015. Improving the Performance of Moore-Penrose Pseudo-Inverse for a Graph’s Laplacian Using GPU. MS Thesis, Amsterdam, the Netherlands. https://doi.org/10.13140/2.1.4457.5048
Shi M, Bar-Ness Y, Su W, 2007. STC and BLAST MIMO modulation recognition. IEEE Global Telecommunications Conf, p.3034–3039. https://doi.org/10.1109/GLOCOM.2007.575
Sills JA, 1999. Maximum-likelihood modulation classification for PSK/QAM. Military Communications Conf, p.217–220. https://doi.org/10.1109/MILCOM.1999.822675
Swami A, Sadler BM, 2000. Hierarchical digital modulation classification using cumulants. IEEE Trans Commun, 48(3): 416–429. https://doi.org/10.1109/26.837045
Tarokh V, Jafarkhani H, Calderbank AR, 1999. Space-time block codes from orthogonal designs. IEEE Trans Inform Theory, 45(5): 1456–1467. https://doi.org/10.1109/18.771146
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. https://doi.org/10.1109/NICS.2016.7725639
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. https://doi.org/10.1007/s11277-013-1415-2
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. https://doi.org/10.1002/dac.2950
Turan M, Öner M, Çirpan HA, 2016. Joint modulation classification and antenna number detection for MIMO systems. IEEE Commun Lett, 20(1): 193–196. https://doi.org/10.1109/LCOMM.2015.2500898
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. https://doi.org/10.1007/s11277-016-3838-z
Wei W, Mendel JM, 2000. Maximum-likelihood classification for digital amplitude-phase modulations. IEEE Trans Commun, 48(2): 189–193. https://doi.org/10.1109/26.823550
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. https://doi.org/10.1109/ISSCS.2011.5978654
Author information
Authors and Affiliations
Corresponding author
Additional information
Compliance with ethics guidelines
Maqsood H. SHAH and Xiao-yu DANG declare that they have no conflict of interest.
Project supported by the National Natural Science Foundation of China (Nos. 61172078, 61571224, and 61571225) and Six Talent Peaks Project in Jiangsu Province, China
Rights and permissions
About this article
Cite this article
Shah, M.H., Dang, Xy. An effective approach for low-complexity maximum likelihood based automatic modulation classification of STBC-MIMO systems. Front Inform Technol Electron Eng 21, 465–475 (2020). https://doi.org/10.1631/FITEE.1800306
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1800306
Key words
- Multiple-input multiple-output
- Space-time block code
- Maximum likelihood
- Automatic modulation classification
- Zero-forcing