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A key challenge of massive MTC (mMTC), is the joint detection of deviceactivity and decoding of data. The sparse characteristics of mMTC makescompressed sensing (CS) approaches a promising solution to the device detectionproblem. However, utilizing CS-based approaches for device detection along withchannel estimation, and using the acquired estimates for coherent datatransmission is suboptimal, especially when the goal is to convey only a fewbits of data.  First, we focus on the coherent transmission and demonstrate that it ispossible to obtain more accurate channel state information by combiningconventional estimators with CS-based techniques. Moreover, we illustrate thateven simple power control techniques can enhance the device detectionperformance in mMTC setups.  Second, we devise a new non-coherent transmission scheme for mMTC andspecifically for grant-free random access. We design an algorithm that jointlydetects device activity along with embedded information bits. The approachleverages elements from the approximate message passing (AMP) algorithm, andexploits the structured sparsity introduced by the non-coherent transmissionscheme. Our analysis reveals that the proposed approach has superiorperformance compared to application of the original AMP approach.

Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach