Abstract
In massive multiple-input multiple-output (MIMO) systems, when the number of base station (BS) antennas is much higher than the number of users. Linear zero-forcing (ZF) precoder is able to achieve the near-optimal capacity performance, once the massive MIMO channel matrix presents the property of asymptotic orthogonality. However, ZF precoder involves large matrix inversion with high complexity, especially when the number of users increases. In this paper, in order to avoid the habitual matrix inversion, we propose a novel low-complexity near-optimal precoder based on the Gram-Schmidt conjugate direction (GSCD) iterative method, which reduces the complexity from O(K3) to O(K2), where K is the number of users. Besides, a simple approach to determine the convergence rate achieved by GSCD-based precoder is obtained by exploiting the massive MIMO channel property of asymptotic orthogonality, which reveals that the proposed precoder converges faster with the increasing number of BS antennas. Numerical results reveal that GSCD-based precoder achieves the near-optimal capacity of the ZF precoder with a reduced number of iterations.
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