Progressive Coding and Iterative Source-Channel Decoding in Wireless Data Gathering Networks

Wireless data gathering networks are often tasked to gather correlated data under severe energy constraints. The use of simple channel codes with source-channel decoding can potentially provide good performance with low energy consumption. Here we consider progressive coding in multi-hop networks, where an intermediate node decodes its received noisy codewords. The estimated information is concatenated with the node's own information word and encoded; the resulting progressively-encoded codeword is then transmitted to the next node. In non-progressive coding, the node simply forwards the received noisy codewords along with its own encoded data. Here we compare the performance of two codes with low decoding complexity, Repeat-Accumulate (RA) and Low-Density Parity-Check (LDPC) codes, in combination with two progressive coding schemes. Progressive channel coding uses only channel decoding at the intermediate node, while progressive source-channel coding uses source-channel decoding, exploiting the probabilistic dependency of the information words (caused by the correlation structure of the data) jointly with the deterministic dependency induced by channel coding. Two decoding schemes are considered at the data center: channel decoding only and iterative source-channel decoding. In simulation experiments, we consider a line network topology with systematic RA and LDPC coding. Results show that progressive coding performs better than non-progressive coding, and RA codes perform better with lower computational complexity than LDPC codes, both for channel-decoding-only and iterative source-channel decoding.