A low-complexity detector for uplink massive MIMO systems based on Gaussian approximate belief propagation

In massive multiple-input multiple-output (MIMO) systems, the number of base station (BS) antennas could reach tens or even hundreds, therefore the hardware cost of adopting conventional signal detectors can be unaffordable due to their high complexity which increases quickly with the number of the transceiver antennas. To solve this problem, this paper proposes a low-complex iterative detector based on Gaussian approximate belief propagation (GABP). The proposed algorithm calculates the first and second order of statistical properties of the messages sent between the transceiver nodes, and substantially reduces the complexity with just matrix-vector multiplication. The complexity of the proposed scheme is proved to be one order of magnitude smaller than that of conventional minimum mean square error (MMSE). The rapid convergence property is evaluated by mean square error (MSE). Simulation results demonstrate that the proposed algorithm could achieve better bit error rate (BER) performance than MMSE with a small number of iterations.