《A Survey on Deep Learning Techniques in Wireless Signal Recognition》思维导图

前三级节点
所有内容

References
[1] X. Hong, J. Wang, C.-X. Wang, and J. Shi, “Cognitive radio in
5G: a perspective on energy-spectral efciency trade-of,” IEEE
Communications Magazine, vol. 52, no. 7, pp. 46–53, 2014.
[2] A. A. Khan, M. H. Rehmani, and A. Rachedi, “Cognitive-radiobased internet of things: applications, architectures, spectrum
related functionalities, and future research directions,” IEEE
Wireless Communications Magazine, vol. 24, no. 3, pp. 17–25,

[3] A. Ali and W. Hamouda, “Advances on spectrum sensing
for cognitive radio networks: theory and applications,” IEEE
Communications Surveys & Tutorials, vol. 19, no. 2, pp. 1277–
1304, 2017.
[4] T. Liu, Y. Guan, and Y. Lin, “Research on modulation recognition with ensemble learning,” EURASIP Journal on Wireless
Communications and Networking, vol. 2017, no. 1, article no. 179,

[5] O. A. Dobre, A. Abdi, Y. Bar-Ness, and W. Su, “Survey
of automatic modulation classifcation techniques: classical
approaches and new trends,” IET Communications, vol. 1, no.
2, pp. 137–156, 2007.
[6] L.-X. Wang and Y.-J. Ren, “Recognition of digital modulation
signals based on high order cumulants and support vector
machines,” in Proceedings of the 2009 Second ISECS International Colloquium on Computing, Communication, Control, and
Management, CCCM 2009, pp. 271–274, China, August 2009.
[7] A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: a brief review,” Computational Intelligence and Neuroscience, vol. 2018, Article
ID 7068349, 13 pages, 2018.
[8] S. P. Singh, A. Kumar, H. Darbari, L. Singh, A. Rastogi, and S.Jain, “Machine translation using deep learning: An overview,” in Proceedings of the 1st International Conference on Computer, Communications and Electronics, COMPTELIX 2017, pp. 162–167, India, July 2017.
[9] T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends
in deep learning based natural language processing,” IEEE
Computational Intelligence Magazine, vol. 13, no. 3, pp. 55–75,

[10] T. J. O’Shea, J. Corgan, and T. C. Clancy, “Convolutional
radio modulation recognition networks,” in Proceedings of
the International Conference on Engineering Applications of
Neural Networks, vol. 629, pp. 213–226, Springer International
Publishing, 2016.
[11] A. Shen, Y. Liu, Y. Zhang et al., “Te method of interference
recognition in mobile communication network based on deep
learning,” in Signal and Information Processing, Networking and
Computers, vol. 494 of Lecture Notes in Electrical Engineering,
pp. 296–306, Springer, Singapore, 2019.
[12] J. Schmidhuber, “Deep learning in neural networks: an
overview,” Neural Networks, vol. 61, pp. 85–117, 2015.

[13] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature,
vol. 521, no. 7553, pp. 436–444, 2015.
[14] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Te American Association for the Advancement of Science: Science, vol. 313, no. 5786,
pp. 504–507, 2006.
[15] M. I. Jordan and T. M. Mitchell, “Machine learning: trends,
perspectives,andprospects,” Science,vol.349,no.6245,pp.255–
260, 2015.
[16] C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen, and L. Hanzo,
“Machine learning paradigms for next-generation wireless networks,” IEEE Wireless Communications Magazine, vol. 24, no. 2,
pp. 98–105, 2017.
[17] Y. Wang, J. Guo, H. Li, L. Li, Z. Wang, and H. Wang, “CNNbased modulation classifcation in the complicated communication channel,” in Proceedings of the 13th IEEE International
Conference on Electronic Measurement and Instruments, ICEMI
2017, pp. 512–516, China, October 2017.
[18] J. Lemley, S. Bazrafan, and P. Corcoran, “Deep learning for
consumer devices and services: pushing the limits for machine
learning, artifcial intelligence, and computer vision,” IEEE
Consumer Electronics Magazine, vol. 6, no. 2, pp. 48–56, 2017.
[19] K.Yashashwi,A.Sethi,andP.Chaporkar,“Alearnabledistortion
correction module for modulation recognition,” IEEE Wireless
Communications Letters, pp. 1-1, 2018.
[20] R. Li, L. Li, S. Yang, and S. Li, “Robust automated VHF
modulation recognition based on deep convolutional neural
networks,” IEEE Communications Letters,vol.22,no.5,pp.946–
949, 2018.
[21] S. Peng, H. Jiang, H. Wang et al., “Modulation classifcation
basedonsignalconstellationdiagramsanddeeplearning,” IEEE
Transactions on Neural Networks and Learning Systems,pp.1–10,

[22] O. A. Dobre and F. Hameed, “Likelihood-based algorithms
for linear digital modulation classifcation in fading channels,”
in Proceedings of the 2006 Canadian Conference on Electrical
and Computer Engineering, CCECE’06, pp. 1347–1350, Canada,

[23] V. G. Chavali and C. R. C. M. Da Silva, “Maximum-likelihood
classifcation of digital amplitude-phase modulated signals
in fat fading non-gaussian channels,” IEEE Transactions on
Communications, vol. 59, no. 8, pp. 2051–2056, 2011.
[24] B. Kim, J. Kim, H. Chae, D. Yoon, and J. W. Choi, “Deep neural
network-based automatic modulation classifcation technique,”
in Proceedings of the 2016 International Conference on Information and Communication Technology Convergence, ICTC 2016,
pp. 579–582, Republic of Korea, October 2016.
[25] E. E. Azzouz and A. K. Nandi, “Procedure for automatic recognition of analogue and digital modulations,” IEE Proceedings
Communications, vol. 143, no. 5, pp. 259–266, 1996.
[26] A. K. Nandi and E. E. Azzouz, “Algorithms for automatic modulation recognition of communication signals,” IEEE Transactions on Communications, vol. 46, no. 4, pp. 431–436, 1998.
[27] F. C.B. F. Muller,C. Cardoso Jr.,andA. Klautau,“Afrontendfor
discriminative learning in automaticmodulation classifcation,”
IEEE Communications Letters, vol. 15, no. 4, pp. 443–445, 2011.
[28] T. O’Shea and J. Hoydis, “An introduction to deep learning for
the physical layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563–575, 2017.
[29] S. Rajendran, W. Meert, D. Giustiniano, V. Lenders, and S.
Pollin, “Deep learning models for wireless signal classifcation
with distributed low-cost spectrum sensors,” IEEE Transactions
on Cognitive Communications and Networking, vol. 4, no. 3, pp.
433–445, 2018.
[30] F. N. Khan, C. H. Teow, S. G. Kiu et al., “Automatic modulation
format/bit-rate classifcation and signal-to-noise ratio estimation using asynchronous delay-tap sampling,” Computers and
Electrical Engineering, vol. 47, pp. 126–133, 2015.
[31] F.N.Khan,C.Lu,andA.P.T.Lau,“Jointmodulationformat/bitrate classifcation and signal-to-noise ratio estimation in multipath fading channels using deep machine learning,” IEEE
Electronics Letters, vol. 52, no. 14, pp. 1272–1274, 2016.
[32] D. C. Chang and P. K. Shih, “Cumulants-based modulation
classifcation technique in multipath fading channels,” IET
Communications, vol. 9, no. 6, pp. 828–835, 2015.
[33] H. Wu, M. Saquib, and Z. Yun, “Novel automatic modulation
classifcation using cumulant features for communications via
multipath channels,” IEEE Transactions on Wireless Communications, vol. 7, no. 8, pp. 3098–3105, 2008.
[34] V. D. Orlic and M. L. Dukic, “Automatic modulation classifcation algorithm using higher-order cumulants under real-world
channel conditions,” IEEE Communications Letters, vol. 13, no.
12, pp. 917–919, 2009.
[35] M. R. Mirarab and M. A. Sobhani, “Robust modulation classifcation for PSK/QAM/ASK using higher-order cumulants,” in
Proceedings of the 6th International Conference on Information,
Communications and Signal Processing, ICICS, pp. 1–4, 2007.
[36] O. A. Dobre, Y. Bar-Ness, and W. Su, “Higher-order cyclic
cumulantsfor highorder modulation classifcation,”in Proceedings of the MILCOM 2003 - 2003 IEEE Military Communications
Conference, vol. 1, pp. 112–117, USA, 2003.
[37] M. L. D. Wong and A. K. Nandi, “Automatic digital modulation
recognition using artifcial neural network and genetic algorithm,” Signal Processing, vol. 84, no. 2, pp. 351–365, 2004.
[38] A. Aubry, A. Bazzoni, V. Carotenuto, A. De Maio, and P. Failla,
“Cumulants-based radar specifc emitter identifcation,” in Proceedings of the 2011 IEEE International Workshop on Information
Forensics and Security, WIFS 2011, pp. 1–6, November 2011.
[39] M.L.Wong,S.K.Ting,andA.K.Nandi,“Naivebayesclassifcation of adaptive broadband wireless modulation schemes with
higher order cumulants,” in Proceedings of the 2008 2nd International Conference on Signal Processing and Communication
Systems (ICSPCS 2008), pp. 1–5, Gold Coast, Australia, 2008.
[40] A. Abdelmutalab, K. Assaleh, and M. El-Tarhuni, “Automatic
modulation classifcation using polynomial classifers,” in Proceedings of the 2014 25th IEEE Annual International Symposium
on Personal, Indoor, and Mobile Radio Communication, IEEE
PIMRC 2014, pp. 806–810, USA, September 2014.
[41] A. Abdelmutalab, K. Assaleh, and M. El-Tarhuni, “Automatic
modulation classifcation based on high order cumulants and
hierarchical polynomial classifers,” Physical Communication,
vol. 21, pp. 10–18, 2016.
[42] W. A. Gardner, A. Napolitano, and L. Paura, “Cyclostationarity:
half a century of research,” Signal Processing, vol. 86, no. 4, pp.
639–697, 2006.
[43] B. Ramkumar, “Automatic modulation classifcation for cognitive radios using cyclic feature detection,” IEEE Circuits and
Systems Magazine, vol. 9, no. 2, pp. 27–45, 2009.
[44] P. D. Sutton, K. E. Nolan, and L. E. Doyle, “Cyclostationary signatures in practical cognitive radio applications,” IEEE Journal
on Selected Areas in Communications, vol. 26, no. 1, pp. 13–24,

[45] A.Fehske,J.Gaeddert,andJ.H.Reed,“A newapproach tosignal
classifcation using spectral correlation and neural networks,”
in Proceedings of the 1st IEEE International Symposium on New
Frontiers in Dynamic Spectrum Access Networks (DySPAN ’05),
pp. 144–150, Baltimore, Md, USA, November 2005.
[46] K. Kim, I. A. Akbar, K. K. Bae, J.-S. Um, C. M. Spooner, and
J. H. Reed, “Cyclostationary approaches to signal detection and
classifcation in cognitive radio,” in Proceedings of the 2007 2nd
IEEE International Symposium on New Frontiers in Dynamic
Spectrum Access Networks, pp. 212–215, Ireland, April 2007.
[47] X. Teng, P. Tian, and H. Yu, “Modulation classifcation based
on spectral correlation and SVM,” in Proceedings of the 2008
4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), pp. 1–4, Dalian,
China, October 2008.
[48] H. Hu, Y. Wang, and J. Song, “Signal classifcation based on
spectral correlation analysis and SVM in cognitive radio,” in
Proceedings of the 22nd International Conference on Advanced
Information Networking and Applications (AINA 2008),pp.883–
887, Gino-wan, Okinawa, Japan, 2008.
[49] G. J. Mendis, J. Wei, and A. Madanayake, “Deep learningbased automated modulation classifcation for cognitive radio,”
in Proceedings of the 2016 IEEE International Conference on
Communication Systems, ICCS 2016, pp. 1–6, China, December

[50] J.Li,L.Qi,andY.Lin,“Research on modulation identifcationof
digital signalsbasedon deeplearning,” in Proceedings of the 2016
IEEE International Conference on Electronic Information and
Communication Technology, ICEICT 2016, pp. 402–405, China,

[51] P. R. U. Lallo, “Signal classifcation by discrete Fourier transform,” in Proceedings of the Conference on Military Communications (MILCOM’99), vol. 1, pp. 197–201, Atlantic City, NJ, USA,

[52] Z. Yu, Y. Q. Shi, and W. Su, “M-ary frequency shif keying
signal classifcation based-on discrete fourier transform,” in
Proceedings of the IEEE Military Communications Conference,

  1. MILCOM 2003, pp. 1167–1172, Boston, MA, USA, 2003.
    [53] L. Zhou, Z. Sun, and W. Wang, “Learning to short-time Fourier
    transform in spectrum sensing,” Physical Communication, vol.
    25, pp. 420–425, 2017.
    [54] Z. Liu, L. Li, H. Xu, and H. Li, “A method for recognition and
    classifcation for hybrid signals based on deep convolutional
    neural network,” in Proceedings of the 2018 International Conference on Electronics Technology, ICET 2018, pp. 325–330, China,
    May 2018.
    [55] K. C. Ho, W. Prokopiw, and Y. T. Chan, “Modulation identifcation of digital signals by the wavelet transform,” IEE Proceedings
  • Radar, Sonar and Navigation, vol. 147, no. 4, pp. 169–176, 2000.
    [56] X. Z. Feng, J. Yang, F. L. Luo, J. Y. Chen, and X. P. Zhong, “Automatic modulation recognition by support vector machines
    using wavelet kernel,” Journal of Physics: Conference Series, vol.
    48, pp. 1264–1267, 2006.
    [57] K. Hassan, I. Dayoub, W. Hamouda, and M. Berbineau, “Automatic modulation recognition using wavelet transform and
    neural networks in wireless systems,” EURASIP Journal on
    Advances in Signal Processing, vol. 2010, Article ID 532898, p.
    42, 2010.
    [58] R. A. Brown, M. L. Lauzon, and R. Frayne, “A general description of linear time-frequency transforms and formulation of
    a fast, invertible transform that samples the continuous stransform spectrum nonredundantly,” IEEE Transactions on
    Signal Processing, vol. 58, no. 1, pp. 281–290, 2010.
    [59] U. Satija, M. Mohanty, and B. Ramkumar, “Automatic modulation classifcationusing S-transform based features,” in Proceedings of the 2nd International Conference on Signal Processing and
    Integrated Networks, SPIN 2015, pp. 708–712, India, 2015.
    [60] B. G. Mobasseri, “Digital modulation classifcation using constellation shape,” Signal Processing, vol. 80, no. 2, pp. 251–277,

[61] A. Ali and F. Yangyu, “Unsupervised feature learning and automatic modulation classifcation using deep learning model,”
Physical Communication, vol. 25, pp. 75–84, 2017.
[62] A. Ali, F. Yangyu, and S. Liu, “Automatic modulation classifcation of digital modulation signals with stacked autoencoders,”
Digital Signal Processing, vol. 71, pp. 108–116, 2017.
[63] F. Wang, Y. Wang, and X. Chen, “Graphic constellations and
DBN based automaticmodulation classifcation,”in Proceedings
of the 2017 IEEE 85th Vehicular Technology Conference (VTC
Spring), pp. 1–5, Sydney, NSW, June 2017.
[64] D. Wang, M. Zhang, J. Li et al., “Intelligent constellation
diagram analyzer using convolutional neural network-based
deep learning,” Optics Express, vol. 25, no. 15, pp. 17150–17166,

[65] S. Peng, H. Jiang, H. Wang, H. Alwageed, and Y.-D. Yao,
“Modulation classifcation using convolutional neural network
based deep learning model,” in Proceedings of the 26th Wireless
and Optical Communication Conference, WOCC 2017, pp. 1–5,
USA, April 2017.
[66] B. Tang, Y. Tu, Z. Zhang, and Y. Lin, “Digital signal modulation
classifcation with data augmentation using generative adversarial nets in cognitive radio networks,” IEEE Access, vol. 6, pp.
15713–15722, 2018.
[67] Y. Tu, Y. Lin, J. Wang, and J.-U. Kim, Semi-Supervised Learning
with Generative Adversarial Networks on Digital Signal Modulation Classifcation, 2018.
[68] S. Hsue and S. S. Soliman, “Automatic modulation classifcation
using zero crossing,” IEE Proceedings F - Radar and Signal
Processing, vol. 137, no. 6, pp. 459–464, 1990.
[69] N. E. West and T. O’Shea, “Deep architectures for modulation
recognition,” in Proceedings of the 2017 IEEE International
Symposium on Dynamic Spectrum Access Networks, DySPAN
2017, pp. 1–6, USA, March 2017.
[70] H. Wu, Q. Wang, L. Zhou et al., “VHF radio signal modulation
classifcation based on convolution neural networks,” Matec
Web of Conferences, vol. 246, Article ID 03032, 2018.
[71] D. Zhang, W. Ding, B. Zhang et al., “Automatic modulation
classifcation based on deep learning for unmanned aerial
vehicles,” Sensors, vol. 18, no. 3, p. 924, 2018.
[72] D. Zhang, W. Ding, B. Zhang et al., Heterogeneous Deep Model
Fusion for Automatic Modulation Classifcation, 2018.
[73] M. Kulin, T. Kazaz, I. Moerman, and E. De Poorter, “End-toEnd learning from spectrum data: a deep learning approach for
wireless signal identifcation in spectrum monitoring applications,” IEEE Access, vol. 6, pp. 18484–18501, 2018.
[74] A. Selim, F. Paisana, J. A. Arokkiam, Y. Zhang, L. Doyle, and L.
A. DaSilva, “Spectrum monitoring for radar bands using deep
convolutional neural networks,” in Proceedings of the 2017 IEEE
Global Communications Conference, GLOBECOM 2017, pp. 1–6,
Singapore, December 2017.

[75] T. J. O’Shea, T. Roy, and T. C. Clancy, “Over-the-air deep
learning based radio signal classifcation,” IEEE Journal of
Selected Topics in Signal Processing, vol. 12, no. 1, pp. 168–179,

[76] S. Riyaz, K. Sankhe, S. Ioannidis, and K. Chowdhury, “Deep
learning convolutional neural networks for radio identifcation,” IEEE Communications Magazine, vol. 56, no. 9, pp. 146–
152, 2018.
[77] K. Ahmad, U. Meier, and H. Kwasnicka, “Fuzzy logic based
signal classifcation with cognitive radios for standard wireless
technologies,” in Proceedings of the 2010 5th International
Conference on Cognitive Radio Oriented Wireless Networks and
Communications, CROWNCom 2010,pp. 1–5, France,June2010.
[78] K. Ahmad, G. Shresta, U. Meier, and H. Kwasnicka, “NeuroFuzzy Signal Classifer (NFSC) for standard wireless technologies,” in Proceedings of the 2010 7th International Symposium on
Wireless Communication Systems, ISWCS’10, pp. 616–620, UK,

[79] M. Schmidt, D. Block, and U. Meier, “Wireless interference
identifcation with convolutional neural networks,” in Proceedings of the 15th IEEE International Conference on Industrial
Informatics, INDIN 2017, pp. 180–185, Germany, July 2017.
[80] A. N. Mody, S. R. Blatt, D. G. Mills et al., “Recent advances in
cognitive communications,” IEEE Communications Magazine,
vol. 45, no. 10, pp. 54–61, 2007.
[81] N. Bitar, S. Muhammad, and H. H. Refai, “Wireless technology
identifcation using deep convolutional neural networks,” in
Proceedings of the 28th Annual IEEE International Symposium
on Personal, Indoor and Mobile Radio Communications, PIMRC
2017, pp. 1–6, Canada, October 2017.
[82] K. Longi, T. Pulkkinen, and A. Klami, “Semi-supervised convolutional neural networks for identifying wi-f interference
sources,” in Proceedings of the Asian Conference on Machine
Learning, pp. 391–406, 2017.
[83] G. Baldini, R. Giuliani, and G. Steri, “Physical layer authentication and identifcation of wireless devices using the synchrosqueezingtransform,”AppliedSciences,vol.8,no.11,p.2167,

[84] J. Yang, L. Yin, L. Sang, X. Zhang, S. You, and H. Liu, A Practical
Implementation of Td-Lte And Gsm Signals Identifcation via
Compressed Sensing, 2017.
[85] D. A. Guimaraes and C. H. Lim, “Sliding-window-based detection for spectrum sensing in radar bands,” IEEE Communications Letters, vol. 22, no. 7, pp. 1418–1421, 2018.
[86] P. C. Sofotasios, L. Mohjazi, S. Muhaidat, M. Al-Qutayri, and
G. K. Karagiannidis, “Energy detection of unknown signals
over cascaded fading channels,” IEEE Antennas and Wireless
Propagation Letters, vol. 15, pp. 135–138, 2016.
[87] J. E. Friedel, T. H. Holzer, and S. Sarkani, “Development,
optimization, and validation of unintended radiated emissions
processing system for threat identifcation,” IEEE Transactions
on Systems, Man, and Cybernetics: Systems, pp. 1–12, 2018.
[88] F. Ding, A. Song, D. Zhang, E. Tong, Z. Pan, and X. You,
“Interference-Aware wireless networks for home monitoring
andperformanceevaluation,”IEEETransactions on Automation
Science and Engineering, vol. 15, no. 3, pp. 1286–1297, 2018.
[89] L. Wei, E. De Poorter, J. Hoebeke et al., “Assessing the coexistence of heterogeneous wireless technologies with an SDRbased signal emulator:acasestudy of wi-f andbluetooth,”IEEE
Transactions on Wireless Communications, vol. 16, no. 3, pp.
1755–1766, 2017.
[90] S. Grimaldi, A. Mahmood, and M. Gidlund, “Real-Time interference identifcation via supervised learning: embedding
coexistence awareness in iot devices,” IEEE Access, vol. 7, pp.
835–850, 2019.
[91] M. W. Aslam, Z. Zhu, and A. K. Nandi, “Automatic modulation
classifcation using combination of genetic programming and
KNN,” IEEE Transactions on Wireless Communications, vol. 11,
no. 8, pp. 2742–2750, 2012.
[92] C. Park, J. Choi, S. Nah, W. Jang, and D. Y. Kim, “Automatic
modulation recognition of digital signals using wavelet features
and SVM,” in Proceedings of the 2008 10th International Conference on Advanced Communication Technology, vol. 1, pp. 387–
390, Gangwon-Do, South Korea, February 2008.
[93] J. Lopatka and P. Macrej, “Automatic modulation classifcation
using statistical moments and a fuzzy classifer,” in Proceedings
of the WCC 2000-ICSP 2000.20005th International Conference
on Signal Processing Proceedings. 16th World Computer Congress
2000, vol. 3, pp. 1500–1506, IEEE, Beijing, China, August 2000.
[94] A. Abdelmutalab, K. Assaleh, and M. El-Tarhuni, “Automatic
modulation classifcation using hierarchical polynomial classifer and stepwise regression,” in Proceedings of the 2016 IEEE
Wireless Communications and Networking Conference (WCNC),
pp. 1–5, Doha, Qatar, April 2016.
[95] Z. Liu, L. Li, H. Xu, and H. Li, “A method for recognition and
classifcation for hybrid signals based on deep convolutional
neural network,” in Proceedings of the 2018 International Conference on Electronics Technology (ICET), pp. 325–330, IEEE,
Chengdu, China, 2018.
[96] S. Grunau, D. Block, and U. Meier, “Multi-label wireless interference classifcation with convolutional neural networks,” in
Proceedings of the 2018 IEEE 16th International Conference on
Industrial Informatics (INDIN), pp. 187–192, 2018.

最后编辑于
©著作权归作者所有,转载或内容合作请联系作者
  • 序言:七十年代末,一起剥皮案震惊了整个滨河市,随后出现的几起案子,更是在滨河造成了极大的恐慌,老刑警刘岩,带你破解...
    沈念sama阅读 198,154评论 5 464
  • 序言:滨河连续发生了三起死亡事件,死亡现场离奇诡异,居然都是意外死亡,警方通过查阅死者的电脑和手机,发现死者居然都...
    沈念sama阅读 83,252评论 2 375
  • 文/潘晓璐 我一进店门,熙熙楼的掌柜王于贵愁眉苦脸地迎上来,“玉大人,你说我怎么就摊上这事。” “怎么了?”我有些...
    开封第一讲书人阅读 145,107评论 0 327
  • 文/不坏的土叔 我叫张陵,是天一观的道长。 经常有香客问我,道长,这世上最难降的妖魔是什么? 我笑而不...
    开封第一讲书人阅读 52,985评论 1 268
  • 正文 为了忘掉前任,我火速办了婚礼,结果婚礼上,老公的妹妹穿的比我还像新娘。我一直安慰自己,他们只是感情好,可当我...
    茶点故事阅读 61,905评论 5 359
  • 文/花漫 我一把揭开白布。 她就那样静静地躺着,像睡着了一般。 火红的嫁衣衬着肌肤如雪。 梳的纹丝不乱的头发上,一...
    开封第一讲书人阅读 47,256评论 1 275
  • 那天,我揣着相机与录音,去河边找鬼。 笑死,一个胖子当着我的面吹牛,可吹牛的内容都是我干的。 我是一名探鬼主播,决...
    沈念sama阅读 36,978评论 3 388
  • 文/苍兰香墨 我猛地睁开眼,长吁一口气:“原来是场噩梦啊……” “哼!你这毒妇竟也来了?” 一声冷哼从身侧响起,我...
    开封第一讲书人阅读 35,611评论 0 254
  • 序言:老挝万荣一对情侣失踪,失踪者是张志新(化名)和其女友刘颖,没想到半个月后,有当地人在树林里发现了一具尸体,经...
    沈念sama阅读 39,891评论 1 293
  • 正文 独居荒郊野岭守林人离奇死亡,尸身上长有42处带血的脓包…… 初始之章·张勋 以下内容为张勋视角 年9月15日...
    茶点故事阅读 34,910评论 2 314
  • 正文 我和宋清朗相恋三年,在试婚纱的时候发现自己被绿了。 大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
    茶点故事阅读 36,736评论 1 328
  • 序言:一个原本活蹦乱跳的男人离奇死亡,死状恐怖,灵堂内的尸体忽然破棺而出,到底是诈尸还是另有隐情,我是刑警宁泽,带...
    沈念sama阅读 32,516评论 3 316
  • 正文 年R本政府宣布,位于F岛的核电站,受9级特大地震影响,放射性物质发生泄漏。R本人自食恶果不足惜,却给世界环境...
    茶点故事阅读 37,995评论 3 301
  • 文/蒙蒙 一、第九天 我趴在偏房一处隐蔽的房顶上张望。 院中可真热闹,春花似锦、人声如沸。这庄子的主人今日做“春日...
    开封第一讲书人阅读 29,132评论 0 19
  • 文/苍兰香墨 我抬头看了看天上的太阳。三九已至,却和暖如春,着一层夹袄步出监牢的瞬间,已是汗流浃背。 一阵脚步声响...
    开封第一讲书人阅读 30,447评论 1 255
  • 我被黑心中介骗来泰国打工, 没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留,地道东北人。 一个月前我还...
    沈念sama阅读 42,034评论 2 343
  • 正文 我出身青楼,却偏偏与公主长得像,于是被迫代替她去往敌国和亲。 传闻我的和亲对象是个残疾皇子,可洞房花烛夜当晚...
    茶点故事阅读 41,242评论 2 339