Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation
Alva Kosasih, Xinwei Qu, Wibowo Hardjawana, Chentao Yue, Branka Vucetic
Abstract
The orthogonal time frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications. The existing low complexity OTFS detectors’ performance is suboptimal in rich scattering environments where there are a large number of moving reflectors that reflect the transmitted signal towards the receiver. In this letter, we propose an OTFS detector, referred to as the BPICNet OTFS detector that integrates NN, Bayesian inference, and parallel interference cancellation concepts. Simulation results show that the proposed detector outperforms the state-of-the-art.
Topics & Concepts
DetectorComputer scienceInterference (communication)Modulation (music)Frequency modulationSIGNAL (programming language)Bayesian probabilityElectronic engineeringSingle antenna interference cancellationAlgorithmArtificial intelligenceTelecommunicationsChannel (broadcasting)Radio frequencyPhysicsEngineeringAcousticsProgramming languagePAPR reduction in OFDMOptical Wireless Communication TechnologiesRadar Systems and Signal Processing