We consider large MIMO systems where only a few transmit antennas are active in every timeslot resulting in a sparse transmit vector. A typical scenario is a wireless sensor network where nodes transmit just sporadically to a single aggregation node. Having just a few receive antennas leads to spatial subsampling or compressive sampling. Based on the sparsity of the transmit signal, reconstruction algorithms known from compressed sensing can be applied. They can even exploit the discrete nature of transmit symbols used in digital communications. In this paper, the Bayesian approximate message passing algorithm is considered. We expand the algorithm by an individual prior component for each element, e.g. information from a decoder, and survey its influence on the recovery performance.
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