  Recently, a novel lookup table based decoding method for binary low-density
parity-check codes has attracted considerable attention. In this approach,
mutual-information maximizing lookup tables replace the conventional operations
of the variable nodes and the check nodes in message passing decoding.
Moreover, the exchanged messages are represented by integers with very small
bit width. A machine learning framework termed the information bottleneck
method is used to design the corresponding lookup tables. In this paper, we
extend this decoding principle from binary to non-binary codes. This is not a
straightforward extension, but requires a more sophisticated lookup table
design to cope with the arithmetic in higher order Galois fields. Provided bit
error rate simulations show that our proposed scheme outperforms the log-max
decoding algorithm and operates close to sum-product decoding.
