© The Institution of Engineering and Technology
In many wireless communication systems, data can be divided into different importance levels. For these systems, unequal error protection (UEP) techniques are used to ensure lower bit error rate for the more important classes. Moreover, if the precise characteristics of the channel are known, UEP can be used to correctly recover the more important classes even under severe receiving conditions. In this study, a UEP scheme based on compressed sensing via a linear program is proposed. Discrete wavelet transform (DWT) is chosen as the sparsifying basis, and then DWT-coded information is divided into two-layered coded streams, each of which is transmitted differentially by applying an unequal number of information bits in linear codes according to the time-varying characteristic of the corrupted channel. In this proposed transmission scheme, the more important information is to guarantee error-free transmission. At the decoder, one can simply reconstruct the signal via the l 1-minimisation algorithm. Simulation results show that the proposed scheme can achieve a higher peak signal-to-noise ratio (PSNR) and obviously improve the error resilience compared to the equal error protection scheme and other UEP methods. More importantly, with the increase of channel corrupted ratio, the drop rate of PSNR is much slower than other solutions. It indicates that the proposed method has better robustness for severe channel conditions.
References
-
-
1)
-
20. Wei, L.F.: ‘Coded modulation with unequal error protection’, IEEE Trans. Commun., 1993, 41, (10), pp. 1439–1449 (doi: 10.1109/26.237878).
-
2)
-
26. Cui, H., Luo, C., Wu, J., et al: ‘Compressive coded modulation for seamless rate adaptation’, IEEE Trans. Wirel. Commun., 2013, 12, (10), pp. 4892–4904 (doi: 10.1109/TWC.2013.090413.121308).
-
3)
-
2. Chang, Y.C., Lee, S.W., Komiya, R.: ‘A low complexity hierarchical QAM symbol bits allocation algorithm for unequal error protection of wireless video transmission’, IEEE Trans. Consum. Electron., 2009, 55, (3), pp. 1089–1097 (doi: 10.1109/TCE.2009.5277961).
-
4)
-
14. Chen, C.H., Chung, W.H.: ‘Dual diversity space–time coding for multimedia broadcast/multicast service in MIMO systems’, IEEE Trans. Commun.2012, 60, (11), pp. 3286–3297 (doi: 10.1109/TCOMM.2012.081412.110503).
-
5)
-
10. Fang, T., Chau, L.P.: ‘GOP-based channel rate allocation using genetic algorithm for scalable video streaming over error-prone networks’, IEEE Trans. Image Process., 2006, 15, (6), pp. 1323–1330 (doi: 10.1109/TIP.2005.864159).
-
6)
-
29. Candès, E., Wakin, M.B., Boyd, S.P.: ‘Enhancing sparsity by reweighted l1 minimization’, J. Fourier Anal. Appl., 2008, 14, (5), pp. 811–905.
-
7)
-
9. Xu, Q., Stankovic, V., Xiong, Z.: ‘Wyner-Ziv video compression and fountain codes for receiver-driven layered multicast’, IEEE Trans. Circuits Syst. Video Technol., 2007, 17, (7), pp. 901–906 (doi: 10.1109/TCSVT.2007.897464).
-
8)
-
D. Donoho
.
Compressed sensing.
IEEE Trans. Inf. Theory
,
2 ,
1289 -
1306
-
9)
-
6. Nosratinia, A., Lu, J., Aazhang, B.: ‘Source-channel rate allocation for progressive transmission of images’, IEEE Trans. Commun., 2003, 51, (2), pp. 186–196 (doi: 10.1109/TCOMM.2003.809256).
-
10)
-
4. Chang, S.-H., Rim, M., Cosman, P.C., Milstein, L.B.: ‘Optimized unequal error protection using multiplexed hierarchical modulation’, IEEE Trans. Inf. Theory, 2012, 58, (9), pp. 5816–5840 (doi: 10.1109/TIT.2011.2173613).
-
11)
-
18. Park, J., Lee, H., Lee, S.: ‘Optimal channel adaptation of scalable video over a multicarrier-based multicell environment’, IEEE Trans. Multimedia, 2009, 11, (6), pp. 1062–1071 (doi: 10.1109/TMM.2009.2026084).
-
12)
-
E. Candès ,
J. Romberg ,
T. Tao
.
Near-optimal signal recovery from random projections: universal encoding strategies?.
IEEE Trans. Inf. Theory
,
2 ,
489 -
509
-
13)
-
J.A. Tropp ,
A.C. Gilbert
.
Signal recovery from random measurements via orthogonal matching pursuit.
IEEE Trans. Inf. Theory
,
12 ,
4655 -
4666
-
14)
-
16. Conci, N., Scorza, G.B., Sacchi, C.: ‘A cross-layer approach for efficient MPEG-4 video streaming using multicarrier spread-spectrum transmission and unequal error protection’. Proc. Int. Conf. Image Processing, ICIP, Geneva, September 2005, pp. 11–14.
-
15)
-
30. Tsaig, Y., Donoho, D.L.: ‘Extensions of compressed sensing’, Signal Process., 2006, 86, (3), pp. 549–571 (doi: 10.1016/j.sigpro.2005.05.029).
-
16)
-
3. Sherwood, G., Zeger, K.: ‘Error protection for progressive image transmission over memoryless and fading channels’, IEEE Trans. Commun., 1998, 46, (12), pp. 1555–1559 (doi: 10.1109/26.737389).
-
17)
-
J. Haupt ,
R. Nowak
.
Signal reconstruction from noisy random projections.
IEEE Trans. Inf. Theory
,
9 ,
4036 -
4048
-
18)
-
7. Baruffa, G., Micanti, B., Frescura, F.: ‘Error protection and interleaving for wireless transmission of JPEG 2000 images and video’, IEEE Trans. Image Process., 2009, 18, (2), pp. 346–356 (doi: 10.1109/TIP.2008.2008421).
-
19)
-
17. van der Schaar, M., Turaga, D.S.: ‘Cross-layer packetization and retransmission strategies for delay-sensitive wireless multimedia transmission’, IEEE Trans. Multimedia, 2007, 9, (1), pp. 185–197 (doi: 10.1109/TMM.2006.886384).
-
20)
-
1. Shannon, C.E.: ‘A mathematical theory of communication’, Bell Syst. Techn. J., 1948, 27, pp. 379–423 (doi: 10.1002/j.1538-7305.1948.tb01338.x).
-
21)
-
5. Pan, X., Banihashemi, A.H., Cuhadar, A.: ‘Progressive transmission of images over fading channels using rate-compatible LDPC codes’, IEEE Trans. Image Process., 2005, 15, (12), pp. 3627–3635 (doi: 10.1109/TIP.2006.882030).
-
22)
-
12. Obando, M., Freitas, W.C., Cavalcanti, F.R.P.: ‘Switching between hybrid MIMO structures for video transmission based on distortion model’. Proc. Int. Conf. VTC – Fall, Ottawa, September 2010, pp. 1–5.
-
23)
-
15. Wu, D., Hou, Y.T., Zhang, Y.Q.: ‘Scalable video coding and transport over broad-band wireless networks’, Proc. IEEE, 2001, 89, (1), pp. 6–20 (doi: 10.1109/5.904503).
-
24)
-
13. Weng, J.-J., Wang, C.-H., Jeng, L.-D.: ‘Space–time coding with multilevel protection for multimedia transmission in MIMO systems’. Proc. Int. Conf. Personal Indoor and Mobile Radio Communication – PIMRC, Tokyo, September 2009, pp. 2045–2049.
-
25)
-
E. Candès
.
The restricted isometry property and its implications for compressed sensing.
Comptes rendus-Mathématique
,
589 -
592
-
26)
-
2. Hagenauer, J., Stockhammer, T.: ‘Channel coding and transmission aspects for wireless multimedia’, Proc. IEEE, 1999, 87, (10), pp. 1764–1777 (doi: 10.1109/5.790636).
-
27)
-
25. Sharma, S.K., Patwary, M., Abdel-Maguid, M.: ‘Spectral efficient compressive transmission framework for wireless communication systems’, IET Signal Process., 2013, 7, (7), pp. 558–564 (doi: 10.1049/iet-spr.2012.0075).
-
28)
-
11. Holliday, T., Goldsmith, A.J., Poor, H.V.: ‘Joint source and channel coding for MIMO systems: Is it better to be robust or quick?’, IEEE Trans. Inf. Theory, 2008, 54, (4), pp. 1393–1405 (doi: 10.1109/TIT.2008.917725).
-
29)
-
27. Candes, E., Rudelson, M., Tao, T., Vershynin, R.: ‘Error correction via linear programming’. Proc. Forty-sixth Annual Symp. Foundations of Computer Science, 2005, pp. 295–308.
-
30)
-
21. Pursley, M.B., Shea, J.M.: ‘Nonuniform phase-shift-key modulation for multimedia multicast transmission in mobile wireless networks’, IEEE J. Sel. Areas Commun., 1999, 17, (5), pp. 774–783 (doi: 10.1109/49.768194).
-
31)
-
4. Thomos, T., Boulgouris, N.V., Strintzis, M.G.: ‘Wireless image transmission using turbo codes and optimal unequal error protection’, IEEE Trans. Image Process., 2005, 14, (11), pp. 1890–1901 (doi: 10.1109/TIP.2005.854482).
-
32)
-
A. Mohr ,
E. Riskin ,
R. Ladner
.
Unequal loss protection: Graceful degradation of image quality over packet erasure channels through forward error corrections.
IEEE J. Sel. Areas Commun.
,
6 ,
819 -
828
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2013.0366
Related content
content/journals/10.1049/iet-spr.2013.0366
pub_keyword,iet_inspecKeyword,pub_concept
6
6