Bayesian QAM demodulation and activity detection for multiuser communication systems

We consider overloaded (non-orthogonal) multiple access multiuser wireless communication systems with many transmitting devices and one central aggregation node, a typical scenario in e.g. machine-to-machine communications. The task of the central node is to detect the set of active devices and to separate and detect their data streams, whose number at any time instance is small compared to the total number of devices in the system. The payload bits are mapped to a quadrature amplitude modulation (QAM) symbol alphabet, transmitted by the active devices and received synchronously at the central node. The data detection can be cast as a compressed sensing (CS) problem due to the sparsity granted by the sporadic transmission of the typically low-complexity nodes. Separation of the real and imaginary parts of the measurement matrix, the unknown QAM symbols, and the received signal yields a group-sparsity problem. We utilize an efficient iterative Bayesian CS recovery scheme which, instead of separately solving for the real and imaginary parts, uses the Turbo principle to exchange and update parameters between the two solvers and thus comes to consensus regarding the sparsity structure. By tailoring this algorithm to QAM detection, joint activity detection, demodulation and data detection with high reliability is possible, even for very large-scale systems.

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