Sparse Activity Detection for Massive Connectivity - 2018


This Project considers the large connectivity application in that a giant number of devices communicate with a base-station (BS) during a sporadic fashion. Device activity detection and channel estimation are central problems in such a situation. Because of the large range of potential devices, the devices would like to be assigned non-orthogonal signature sequences. The main objective of this Project is to show that by using random signature sequences and by exploiting sparsity within the user activity pattern, the joint user detection and channel estimation drawback can be formulated as a compressed sensing single measurement vector (SMV) or multiple measurement vector (MMV) downside depending on whether the BS has a single antenna or multiple antennas and efficiently solved using an approximate message passing (AMP) algorithm. This Project proposes an AMP algorithm design that exploits the statistics of the wireless channel and provides an analytical characterization of the probabilities of false alarm and missed detection via state evolution. We have a tendency to think about 2 cases relying on whether or not the big-scale part of the channel fading is thought at the BS and style the minimum mean squared error denoiser for AMP in step with the channel statistics. Simulation results demonstrate the substantial advantage of exploiting the channel statistics in AMP style; however, knowing the large-scale fading part does not seem to supply tangible benefits. For the multiple-antenna case, we use two totally different AMP algorithms, namely the AMP with vector denoiser and the parallel AMP-MMV, and quantify the good thing about deploying multiple antennas.

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