Distributed Sparse Activity Detection in CellFree Massive MIMO Systems Distributed sparse activity detection in cellfree massive multipleinput multipleoutput (MIMO) systems is considered in this paper. At the beginning of each channel coherence interval, all the active users send pilots to the access points (APs). Then, each AP makes its own decision on the activity of all the users based on the approximate message passing (AMP) iterative procedure. Following this, the optimal fusion rule is used at the fusion center to make the final decisions on the activity of all the users based on the individual decisions and the corresponding reliability obtained at all the APs. The performance levels of this distributed sparse activity detection method are analyzed with Monte Carlo simulations.

Active Users Detection Based on Deterministic Sequences for Massive Connectivity To support massive Machine Type Communications (mMTC) applications, especially for overcoming the high overhead problem, compressive sensing algorithm for grantfree multiple access is advocated. For an mMTC system with a large number of users, in most existing research, each user is allocated a complex Gaussian random sequence, and approximated message passing (AMP) algorithm is normally employed at the receiver to detect the active users. In this paper, binary deterministic sequences are considered, resulting much simpler implementation. It is shown that, due to the smaller coherence value, significant performance gain can be obtained by employing the binary deterministic sequences into AMP process. In particular, binary quasiorthogonal sequences (QOS) exhibit excellent performance in the shortpacket transmission. Moreover, an improved AMP algorithm is presented by designing a new stopping criterion to reduce the number of iterations without loss of accuracy.