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In this work, we utilize the framework of compressed sensing (CS) fordistributed device detection and resource allocation in large-scalemachine-to-machine (M2M) communication networks. The devices deployed in thenetwork are partitioned into clusters according to some pre-defined criteria.Moreover, the devices in each cluster are assigned a unique signature of aparticular design that can be used to indicate their active status to thenetwork. The proposed scheme in this work mainly consists of two essentialsteps: (i) The base station (BS) detects the active clusters and the number ofactive devices in each cluster using a novel block sketching algorithm, andthen assigns a certain amount of resources accordingly. (ii) Each active devicedetects its ranking among all the active devices in its cluster using anenhanced greedy algorithm and accesses the corresponding resource fortransmission based on the ranking. By exploiting the correlation in the devicebehaviors and the sparsity in the activation pattern of the M2M devices, thedevice detection problem is thus tackled as a CS support recovery procedure fora particular binary block-sparse signal $x\in\mathbb{B}^N$ -- with blocksparsity $K_B$ and in-block sparsity $K_I$ over block size $d$. Theoreticalanalysis shows that the activation pattern of the M2M devices can be reliablyreconstructed within an acquisition time of $\mathcal{O}(\max\{K_B\log N,K_BK_I\log d\})$, which achieves a better scaling and less computationalcomplexity of $\mathcal{O}(N(K_I^2+\log N))$ compared with standard CSalgorithms. Moreover, extensive simulations confirm the robustness of theproposed scheme in the detection process, especially in terms of higherdetection probability and reduced access delay when compared with conventionalschemes like LTE random access (RA) procedure and classic cluster-based accessapproaches.

Block Compressed Sensing Based Distributed Device Detection for M2M Communications