Being an enabling technique of the next generation wireless, massive multiple-input multiple-output (MIMO) can greatly enhance the spectral capacity and support much higher data rates. However, with hundreds of antennas, optimal massive MIMO detection suffers from prohibitive complexity. Thus, descent search (DS) algorithms have been employed because of their low-computation load and hardware-friendly features. However, the performance is not always guaranteed with varying antenna configurations or correlations. In this paper, a linear inequality constraint quadratic programming (LICQP) model is proposed first. The resulting constrained DS (CDS) detector outperforms the DS one. Furthermore, a universal pre-conditioner is proposed to improve the detector's convergence in imperfect conditions. Theoretical and numerical results indicate the preconditioned detector's advantage over the state-of-the-art (SOA) with 60% complexity reduction. Hardware implementation on Xilinx Virtex-7 shows that, compared with the SOA, our design achieves 1.75× throughput (35 Mbps in 128 × 8 i.i.d. channel) with similar hardware. Design framework proposed in this paper can be generalized to any other similar detectors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.