CIF: Small: Learning To Correct Errors

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Error-correcting codes play an essential role in ensuring integrity of data in numerous applications, including wireless communications, computer networks and data storage. The beauty of error-correcting codes and elegance of their decoding algorithms reflect the rich structure of the underlying mathematical objects, but also human creativity and ingenuity. Motivated by recent successes of Deep Neural Networks (DNN) in various applications, this project is investigating if, how, and how efficiently this structure and elegance can be learned by a machine, and how the discovery of new and improved decoding algorithms can be automated. The project is devising parallelizable neural network based decoders with ultra-low latency and improved error performance. The resulting decoders are intended to find broad applicability in emerging wireless standards (5G and beyond) as well as new applications including decoding for quantum computers, machine-to-machine communications, long-haul optical communications and flash memories in data centers.This project approaches the error-correction problem as classification of the points in a high-dimensional signal space, and uses DNNs to learn complex codeword decision region boundaries to approach the maximum-likelihood decoding performance. The project addresses two key practical challenges: a) achieving extremely low misclassification rates (many orders of magnitude lower) compared to traditional machine learning applications, and b) maintaining low-complexity of decoding via sparse and quantized neural networks. The first thrust utilizes coding-theory domain knowledge, and harnesses the structural similarities between iterative decoders and neural networks for improved learning of decoders. This is achieved by using sampled error patterns from iterative decoding algorithms for stochastic optimization of neural-network decoders. The second thrust focuses on using a new supervised approach for learning decoders, in which existing high-complexity decoders are employed to synthesize a low-complexity neural-network-based decoder with comparable performance. The third thrust establishes theoretical results on the generalization capabilities of neural-network-based decoders (including convergence rates and error performance). This research is providing multi-faceted insight into modern error-correction algorithms and machine learning, and should lead to both theoretical and practical advances of importance to the communications and computer industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.