2018/10/01 by Tao Jiang, Yuanming Shi, Jiang, Tao +5
Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Energy Harvesting in Wireless Networks #Distributed Sensor Networks and Detection Algorithms
paper · pdf · doi:10.48550/arxiv.1810.00720
Massive device connectivity is a crucial communication challenge for Internet\nof Things (IoT) networks, which consist of a large number of devices with\nsporadic traffic. In each coherence block, the serving base station needs to\nidentify the active devices and estimate their channel state information for\neffective communication. By exploiting the sparsity pattern of data\ntransmission, we develop a structured group sparsity estimation method to\nsimultaneously detect the active devices and estimate the corresponding\nchannels. This method significantly reduces the signature sequence length while\nsupporting massive IoT access. To determine the optimal signature sequence\nlength, we study \the phase transition behavior of the group sparsity\nestimation problem. Specifically, user activity can be successfully estimated\nwith a high probability when the signature sequence length exceeds a threshold;\notherwise, it fails with a high probability. The location and width of the\nphase transition region are characterized via the theory of conic integral\ngeometry. We further develop a smoothing method to solve the high-dimensional\nstructured estimation problem with a given limited time budget. This is\nachieved by sharply characterizing the convergence rate in terms of the\nsmoothing parameter, signature sequence length and estimation accuracy,\nyielding a trade-off between the estimation accuracy and computational cost.\nNumerical results are provided to illustrate the accuracy of our theoretical\nresults and the benefits of smoothing techniques.\n