Hyper-Graph Network Decoders for Algebraic Block Codes

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

• input an encoded message with noise to a neural-networks model comprising a variable layer of nodes and a check layer of nodes, wherein each node is associated with at least one weight and a hyper-network node;
• update the weights associated with the variable layer of nodes by processing the encoded message using the hyper-network nodes associated with the variable layer of nodes;
• generate a first set of outputs by processing the encoded message using the variable layer of nodes and their respective updated weights;
• update the weights associated with the check layer of nodes by processing the first set of outputs using the hyper-network nodes associated with the check layer of nodes;
• and
• generate a decoded message without noise using the neural-networks model, wherein the generation comprises using at least the first set of outputs and the check layer of nodes and their respective updated weights.

A system comprising:

• one or more processors;
• and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
• input an encoded message with noise to a neural-networks model comprising a variable layer of nodes and a check layer of nodes, wherein each node is associated with at least one weight and a hyper-network node;
• update the weights associated with the variable layer of nodes by processing the encoded message using the hyper-network nodes associated with the variable layer of nodes;
• generate a first set of outputs by processing the encoded message using the variable layer of nodes and their respective updated weights;
• update the weights associated with the check layer of nodes by processing the first set of outputs using the hyper-network nodes associated with the check layer of nodes;
• and
• generate a decoded message without noise using the neural-networks model, wherein the generation comprises using at least the first set of outputs and the check layer of nodes and their respective updated weights.