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A fundamental challenge of the large-scale Internet-of-Things lies in how tosupport massive machine-type communications (mMTC). This letter proposes amedia modulation based mMTC solution for increasing the throughput, where amassive multi-input multi-output based base station (BS) is used for enhancingthe detection performance. For such a mMTC scenario, the reliable active devicedetection and data decoding pose a serious challenge. By leveraging thesparsity of the uplink access signals of mMTC received at the BS, a compressivesensing based massive access solution is proposed for tackling this challenge.Specifically, we propose a block sparsity adaptive matching pursuit algorithmfor detecting the active devices, whereby the block-sparsity of the uplinkaccess signals exhibited across the successive time slots and the structuredsparsity of media modulated symbols are exploited for enhancing the detectionperformance. Moreover, a successive interference cancellation based structuredsubspace pursuit algorithm is conceived for data demodulation of the activedevices, whereby the structured sparsity of media modulation based symbolsfound in each time slot is exploited for improving the detection performance.Finally, our simulation results verify the superiority of the proposed schemeover state-of-the-art solutions.

Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications