ADMM-Based Decoder for Binary Linear Codes Aided by Deep Learning

Discovering Binary Codes for Documents by Learning Deep Generative Models

We describe a deep generative model in which the lowest layer represents the word-count vector of a document and the top layer represents a learned binary code for that document. The top two layers of the generative model form an undirected associative memory and the remaining layers form a belief net with directed, top-down connections. We present efficient learning and inference procedures fo...

A Decision-Aided Parallel SC-List Decoder for Polar Codes

—In this paper, we propose a decision-aided scheme for parallel SC-List decoding of polar codes. At the parallel SC-List decoder, each survival path is extended based on multiple information bits, therefore the number of split paths becomes very large and the sorting to find the top L paths becomes very complex. We propose a decision-aided scheme to reduce the number of split paths and thus red...

A Low-Cost Decoder for Arbitrary Binary Variable-Length Codes

Encoders and decoders for variable-length codes such as Huffman Codes can be costly to implement. This paper describes low-cost encoder and decoder for binary variable-length codes that is simple to implement when decoding speed is not an issue.

New Binary Linear Codes

Binary Linear Block Codes