In this paper we consider transport coding in a network. The results obtained with this approach indicate, that coding in network helps not only to increase the reliability of the transmitted information, but also can be used to improve such important characteristic of a network, as the mean message delay. It is possible to use quite simple for implementation and having low complexity Tornado codes for transport coding.

We suggest some nontraditional approaches to coding in a network. We show that it is possible to apply coding not only to increase the reliability of the transmitted information, but also to improve important characteristics of a network, such as the mean message delay. Also, we may consider the encryption of messages just as a coding process at the presentation layer in the network. We can thus distinguish different coding processes at different network layers. In this case, we have to be concerned with the impact, on one layer, of coding at another layer. Thus, the problem of reconciliation of coding at neighbor network layers arises. We suggest some solutions to this problem.

In this paper, a coding scheme with M-ary Walsh-Hadamard (WH) orthogonal modulation is considered for multi-carrier (MC) transmission. A novel pilot-assisted channel estimation approach is proposed, that efficiently exploits the properties of WH codes. Firstly, simulation results on the single-user performance show the influence of the energy distribution between pilot and information symbols. Then, the impact of the channel estimation method is assessed for different system loads in a MC code division multiple access (MC-CDMA) uplink scenario.

We suggest a decoding algorithm of q-ary linear codes, which we call supercode decoding. It ensures the error probability that approaches the error probability of minimum-distance decoding as the length of the code grows. For n/spl rarr//spl infin/ the algorithm has the maximum-likelihood performance. The asymptotic complexity of supercode decoding is exponentially smaller than the complexity of all other methods known. The algorithm develops the ideas of covering-set decoding and split syndrome decoding.