Similar articles

Abstract: rateless code usually generates a potentially infinite number of coded packets at the encoder and collects enough packets at the decoder to ensure reliable recovery of multiple information packets. The conventional rateless decoder usually works in a parallel manner which needs to initiate a new belief propagation (BP) decoding procedure upon each newly received collection of coded packets, thereby resulting in prohibitive decoding complexity in practice. In this paper, we present a novel serial decoding algorithm, i.e., the serial storage belief propagation (SS BP) algorithm, for rateless codes over noisy channels. Specifically, upon receiving a new group of coded packets, the decoder initiates a new attempt to decode all the packets received so far, using the results of the previous attempt as initial input. Moreover, in each iteration of the new attempt, the decoder serially propagates the messages group by group from the most recent one to the earliest one. In this way, the newly updated messages can be propagated faster, expediting the recovery of information packets. In addition, the proposed serial decoding algorithm has significantly lower complexity than the existing parallel decoding algorithms. Simulation results validate its effectiveness in AWGN, Rayleigh, and Rician fading channels.

[1]3GPP TS26.346:2007. Technical Specification Group Services and System Aspects, Multimedia Broadcast/Multicast Services (MBMS) Protocols and Codes (Release 6).

[2]Castura, J., Mao, Y., 2006. Rateless coding over fading channels. IEEE Commun. Lett., 10(1):46-48.

[3]Castura, J., Mao, Y., 2007a. Rateless coding and relay networks. IEEE Signal Process. Mag., 24(5):27-35.

[4]Castura, J., Mao, Y., 2007b. Rateless coding for wireless relay channels. IEEE Trans. Wirel. Commun., 6(5):1638-1642.

[5]Cataldi, P., Gerla, M., Zampognaro, F., 2009. Rateless Codes for File Transfer over DVB-S. First Int. Conf. on SPACOMM, p.7-12.

[6]Chung, S.Y., Richardson, T.J., Urbanke, R.L., 2001. Analysis of sum-product decoding of low-density parity-check codes using a Gaussian approximation. IEEE Trans. Inf. Theory, 47(2):657-670.

[7]Etesami, O., Shokrollahi, A., 2006. Raptor codes on binary memoryless symmetric channels. IEEE Trans. Inf. Theory, 52(5):2033-2051.

[8]Gallager, R.G., 1963. Low-Density Parity-Check Codes. MIT Press, Cambridge, MA.

[9]Hu, K., Castura, J., Mao, Y., 2006. WLC44-3: Reduced-Complexity Decoding of Raptor Codes over Fading Channels. IEEE Global Telecommunications Conf., p.1-5.

[10]Hu, X., Eleftheriou, E., 2005. Regular and irregular progressive edge growth Tanner graphs. IEEE Trans. Inf. Theory, 51(1):386-398.

[11]Kushwaha, H., Xing, Y., Chandramouli, R., 2008. Reliable multimedia transmission over cognitive radio networks using fountain codes. Proc. IEEE, 96(1):155-165.

[12]Loeliger, H.A., 2004. An introduction to factor graphs. IEEE Signal Process. Mag., 21(1):28-41.

[13]Luby, M., 2004. LT Codes. 43rd Annual IEEE Symp. on Foundations of Computer Science, p.271-280.

[14]Ma, Y., Yuan, D., Zhang, H., 2006. Fountain Codes and Applications to Reliable Wireless Broadcast System. IEEE Information Theory Workshop, p.66-70.

[15]MacKay, D.J.C., 2005. Fountain codes. IEE Proc. Commun., 152(6):1062-1068.

[16]Palanki, R., Yedidia, J.S., 2004. Rateless Codes on Noisy Channels. IEEE Int. Symp. on Information Theory, p.37.

[17]Richardson, T.J., Urbanke, R.L., 2001. The capacity of low-density parity-check codes under message-passing decoding. IEEE Trans. Inf. Theory, 47(2):599-618.

[18]Richardson, T.J., Shokrollahi, M.A., Urbanke, R.L., 2001. Design of capacity-approaching irregular low-density parity-check codes. IEEE Trans. Inf. Theory, 47(2):619-637.

[19]Shokrollahi, A., 2006. Raptor codes. IEEE Trans. Inf. Theory, 52(6):2551-2567.