Method and device for decoding data

An electronic device for decoding data, the electronic device comprising:

• a memory;
• a processor;
• a turbo decoder comprising a plurality of Machine Learning (ML) models;
• and
• a sparsity controller, coupled to the memory and the processor, the sparsity controller being configured to:
• receive encoded data,
• determine a sparsity of the plurality of ML models of the turbo decoder for decoding the encoded data based on quality of service (QoS) parameters, and
• decode the encoded data using the turbo decoder based on the determined sparsity.

A central electronic device for increasing a sparsity of trained Machine Learning (ML) models of a plurality of turbo decoders, the electronic device comprising:

• a memory;
• a processor;
• a performance analyzer for receiving weights of each layer of the trained ML models of the plurality of turbo decoders;
• and
• a federated learning controller configured to:
• determine an average of the weights of each layer of the trained ML models of the plurality of turbo decoders, and
• update the weights of each layer of the trained ML models of the plurality of turbo decoders with the average of the weights for increasing the sparsity of the ML models of the plurality of turbo decoders.

A turbo decoder for decoding data, comprises:

• two interleavers (12, 13);
• first and second Machine Learning (ML) models with an adjustable sparsity for decoding encoded data;
• and
• a deinterleaver configured to perform a deinterleaving operation on decoded data received from the second ML model,
• wherein the first ML model receives encoded data, and
• wherein the second ML model receives decoded data through a first interleaver from the first ML model, and receives the encoded data through a second interleaver.