Joint Activity Detection and Data Decoding in Massive Random Access via a Turbo Receiver

Xinyu Bian, Yuyi Mao, Jun Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


In this paper, we propose a turbo receiver for joint activity detection and data decoding in grant-free massive random access, which iterates between a detector and a belief propagation (BP)-based channel decoder. Specifically, responsible for user activity detection, channel estimation, and soft data symbol detection, the detector is developed based on a bilinear inference problem that exploits the common sparsity pattern in the received pilot and data signals. The bilinear generalized approximate message passing (BiG-AMP) algorithm is adopted to solve the problem using probabilities of the transmitted data symbols estimated by the channel decoder as prior knowledge. In addition, extrinsic information is derived from the detector to improve the channel decoding accuracy of the decoder. Simulation results show significant improvements achieved by the proposed turbo receiver compared with conventional designs.
Original languageEnglish
Title of host publication2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
Publication statusPublished - Jul 2021


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