TinyTurbo: Efficient Turbo Decoders on Edge
S. Ashwin Hebbar, Rajesh Mishra, Sravan Kumar Ankireddy, Ashok Vardhan Makkuva, Hyeji Kim, Pramod Viswanath
Abstract
In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP algorithm but has much better reliability than the max-log-MAP baseline and performs close to the MAP algorithm. We show that TINYTURBO exhibits strong robustness on a variety of practical channels of interest, such as EPA and EVA channels, which are included in the LTE standards. We also show that TINYTURBO strongly generalizes across different rate, blocklengths, and trellises. We verify the reliability and efficiency of TINYTURBO via over-the-air experiments.
Topics & Concepts
TurboRobustness (evolution)Turbo codeComputer scienceReliability (semiconductor)AlgorithmBaseline (sea)Turbo equalizerEnhanced Data Rates for GSM EvolutionDecoding methodsArtificial intelligenceConcatenated error correction codeEngineeringChemistryAutomotive engineeringBiochemistryQuantum mechanicsGenePower (physics)Block codeOceanographyPhysicsGeologyError Correcting Code TechniquesAdvanced Wireless Communication TechniquesWireless Signal Modulation Classification