Massive machine-type communication (MMTC) is the key technology to meet the flourishing Internet of things industry. One prominent characteristic of MMTC is massive connections which brings a critical challenge of multiple access interference (MAI) on channel estimation, user identification, and data detection. Currently, various techniques have been proposed to mitigate the MAI, but those MAI alleviations are limited. Therefore, this paper proposes a novel technique called tandem spreading network-coded division multiple access (TSNDMA) to effectively cope with the MAI problem. In this technique, first a novel channel-reciprocity-based precompensation scheme is proposed for channel estimation. Then, a tandem spreading mechanism and a physical-layer network coding-based redundancy design are introduced for user identification and data detection with corresponding algorithms at the receiver. Simulation results show that TSNDMA can effectively mitigate the MAI to achieve a favorable system performance.
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