Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free New mobile communication system design for Rayleigh environments based on compressed sensing-source coding

  • Author(s): Houria Haneche 1 ; Abdeldjalil Ouahabi 2, 3 ; Bachir Boudraa 1
    • View affiliations
  • Source: Volume 13, Issue 15, 17 September 2019, p. 2375 – 2385
    DOI:  10.1049/iet-com.2018.5348 , Print ISSN 1751-8628, Online ISSN 1751-8636
© The Institution of Engineering and Technology
Received 10/05/2018, Accepted 24/06/2019, Revised 11/06/2019, Published 26/06/2019

A new end-to-end communication system is proposed to increase transmission speed, robustness, and security in order to meet the requirements of mobile systems that know an exponentially increasing data amount over time. The design relies on the use of compressed sensing-source coding instead of the supported speech coding standards in actual mobile communication systems. The proposed compressed sensing-source coding method allows reducing the speech coding complexity by using simple quantisation and binary encoding, saving communication system resources, and encrypting communications without additional costs. The performance of the resulting communication system is evaluated for speech communication via 10 dB Rayleigh environment in terms of perceptual evaluation of speech quality (PESQ) scores and coherence speech intelligibility index (CSII) when convolutional coding, orthogonal frequency division multiplexing, and diversity schemes are used. Results report that for a bit rate of 12.8 kbit/s the proposed scheme achieves fair speech intelligibility justified by a CSII value of 0.5, and offers good output speech quality measure, providing a PESQ of 3.33 for the same bit rate.

Inspec keywords: speech enhancement; speech coding; Rayleigh channels; source coding; speech intelligibility; convolutional codes; OFDM modulation

Other keywords: end-to-end communication system; convolutional coding; resulting communication system; speech coding complexity; noise figure 10.0 dB; mobile systems; actual mobile communication systems; compressed sensing-source coding method; encrypting communications; communication system resources; 10 dB Rayleigh environment; fair speech intelligibility; supported speech; Rayleigh environments; bit rate 12.8 kbit/s; good output speech quality measure; speech intelligibility index; new mobile communication system design; speech communication; speech quality scores; exponentially increasing data amount

Subjects: Other topics in statistics; Modulation and coding methods; Speech processing techniques; Speech and audio coding; Codes; Speech and audio signal processing; Radio links and equipment; Mobile radio systems

References

    1. 1)
      • 21. Gao, Z., Dai, L., Han, S., et al: ‘Compressive sensing techniques for next-generation wireless communications’, IEEE Wirel. Commun., 2018, 25, (3), pp. 144–153.
    2. 2)
      • 2. Donoho, D.L.: ‘Compressed sensing’, IEEE Trans. Inf. Theory, 2006, 52, (4), pp. 1289–1306.
    3. 3)
      • 11. Cambareri, V., Mangia, M., Pareschi, F., et al: ‘Low-complexity multiclass encryption by compressed sensing’, IEEE Trans. Signal Process., 2015, 63, (9), pp. 2183–2195.
    4. 4)
      • 16. Shirazinia, A., Chatterjee, S., Skoglund, M.: ‘Joint source-channel vector quantization for compressed sensing’, IEEE Trans. Signal Process., 2014, 6, (14), pp. 3667–3681.
    5. 5)
      • 30. Yang, A.Y., Ganesh, A., Zhou, Z., et al: ‘Fast l1-minimization algorithms for robust face recognition’, IEEE Trans. Image Process., 2013, 22, (8), pp. 3234–3246.
    6. 6)
      • 25. Ruyet, D.L., Pischella, M.: ‘Digital communications 1: source and channel coding’ (ISTE-John Wiley & Sons, London/Hoboken, UK/USA, 2015, 2nd edn.).
    7. 7)
      • 1. Candes, E.J., Wakin, M.B.: ‘An introduction to compressive sampling’, IEEE Signal Process. Mag., 2008, 25, (2), pp. 21–30.
    8. 8)
      • 29. Zhang, Y., Zhang, L.Y., Zhou, J., et al: ‘A review of compressive sensing in information security field’, IEEE Access, 2016, 4, pp. 2507–2519.
    9. 9)
      • 20. Wunder, G., Boche, H., Strohmer, T., et al: ‘Sparse signal processing concepts for efficient 5 g system design’, IEEE Access, 2015, 3, pp. 195–208.
    10. 10)
      • 15. Wang, J., Lee, Y., Lin, C., et al: ‘Compressive sensing-based speech enhancement’, IEEE/ACM Trans. Audio Speech Lang. Process., 2016, 24, (11), pp. 2122–2131.
    11. 11)
      • 9. Giacobello, D., Christensen, M.G., Murthi, M.N., et al: ‘Sparse linear prediction and its applications to speech processing’, IEEE Trans. Audio Speech Lang. Process., 2012, 20, (5), pp. 1644–1657.
    12. 12)
      • 26. Garofolo, J.S., Lamel, L.F., Fisher, W.M.: , et al ‘DARPA TIMIT acoustic-phonetic continuous speech corpus’ (National Institute of Standards and Technology (NIST), Gaithersburg, 1993).
    13. 13)
      • 23. Ouahabi, A.: ‘Signal and image multiresolution analysis’ (Iste-John Wiley & Sons, London/Hoboken, UK/USA, 2012).
    14. 14)
      • 14. Low, S.Y., Pham, D.S., Venkatesh, S.: ‘Compressive speech enhancement’, Speech Commun., 2013, 55, (6), pp. 757–768.
    15. 15)
      • 17. Haneche, H., Boudraa, B., Ouahabi, A: ‘Compressed sensing investigation in an end-to-end rayleigh communication system: speech compression’. 2018 Int. Conf. on Smart Communications in Network Technologies (SaCoNeT), El Oued, Algeria, 2018, pp. 73–77.
    16. 16)
      • 6. Farrag, M.: ‘Secure differential compressive spectrum sensing with 1-bit quantisation’, IET Commun., 2019, 13, (6), pp. 637–641.
    17. 17)
      • 7. Xue, T., Dong, X., Shi, Y.: ‘Multiple access and data reconstruction in wireless sensor networks based on compressed sensing’, IEEE Trans. Wirel. Commun., 2013, 12, (7), pp. 3399–3411.
    18. 18)
      • 22. Dong, J., Wang, W., Dai, W., et al: ‘Analysis SimCO algorithms for sparse analysis model based dictionary learning’, IEEE Trans. Signal Process., 2016, 64, (2), pp. 417–431.
    19. 19)
      • 8. Nagahara, M., Quevedo, D.E., Matsuda, T., et al: ‘Compressive sampling for networked feedback control’. 2012 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 2012, pp. 2733–2736.
    20. 20)
      • 13. Bianchi, T., Bioglio, V., Magli, E.: ‘Analysis of one-time random projections for privacy preserving compressed sensing’, IEEE Trans. Inf. Forensics Sec., 2016, 11, (2), pp. 313–327.
    21. 21)
      • 18. Bessette, B., Salami, R., Lefebvre, R., et al: ‘The adaptive multirate wideband speech codec (AMR-WB)’, IEEE Trans. Speech Audio Process., 2002, 10, (8), pp. 620–636.
    22. 22)
      • 4. Zhang, Y., Venkatesan, R., Dobre, O.A., et al: ‘Novel compressed sensing-based channel estimation algorithm and near-optimal pilot placement scheme’, IEEE Trans. Wirel. Commun., 2016, 15, (4), pp. 2590–2603.
    23. 23)
      • 27. ITU-T P. 862. 2:, ‘Wideband extension to recommendation P. 862 for the assessment of wideband telephone networks and speech codecs’, 2007.
    24. 24)
      • 10. Orsdemir, A., Altun, H.O., Sharma, G., et al: ‘On the security and robustness of encryption via compressed sensing’. 2008 IEEE Military Communications Conf. (MILCOM 2008), San Diego, CA, USA, 2008, pp. 1–7.
    25. 25)
      • 12. Bianchi, T., Bioglio, V., Magli, E: ‘On the security of random linear measurements’. 2014 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, 2014, pp. 3992–3996.
    26. 26)
      • 28. Kates, J.M., Arehart, K.H.: ‘Coherence and the speech intelligibility index’, J. Acoust. Soc. Am., 2005, 117, (4), pp. 2224–2237.
    27. 27)
      • 19. Salah-Eddine, C., Merouane, B.: ‘Robust coding of wideband speech immittance spectral frequencies’, Speech Commun., 2014, 65, pp. 94–108.
    28. 28)
      • 3. Ferdian, R., Hou, Y., Okada, M.: ‘A low-complexity hardware implementation of compressed sensing-based channel estimation for ISDB-T system’, IEEE Trans. Broadcast., 2017, 63, (1), pp. 92–102.
    29. 29)
      • 5. Dikmese, S., Ilyas, Z., Sofotasios, P.C., et al: ‘Sparse frequency domain spectrum sensing and sharing based on cyclic prefix autocorrelation’, IEEE J. Sel. Areas Commun., 2017, 35, (1), pp. 159–172.
    30. 30)
      • 24. Stuber, G.L.: ‘Principles of mobile communication’ (Springer, New York, USA, 2011, 3rd edn.).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2018.5348
Loading

Related content

content/journals/10.1049/iet-com.2018.5348
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address