Low-Complexity Large-Scale MIMO Detector with 16-Ary QAM Superposition Constellations

Abstract

To achieve higher bandwidth efficiency in wireless communication systems where the available frequency band is strictly limited, high-order modulation schemes are widely chosen as a promising solution to meet the band-efficiency requirement. This paper proposes two low-complexity signal detection architectures using superposition mapping and decomposition techniques at the modulator. The first signal detection, a so-called one-step detector, enjoys the low complexity level of the Tanner-graph-based detection algorithm, which is in the second order of the product of the modulation order and the number of transmit antennas. To further reduce the complexity level for the detector by 50%, a two-step belief propagation detector is proposed where interference of the stronger bit index is partially canceled when recovering the weaker bit index. As a result, the novel proposed two-step detector performs nearly as well as the one-step detection architecture when a massive number of antennas are employed at the receiver side while offering complexity reduction. This attribute of the proposed two-step large-scale multiple-input multiple-output (LS-MIMO) detector is significantly critical for 5G and beyond, where a massive/large-scale number of antennas should be employed to meet the growing demand for high-speed wireless access.