An efficient conjugate residual detector for massive MIMO systems

Abstract

In nowadays wireless communication systems, massive multiple-input multiple-output (MIMO) technique brings better energy efficiency and coverage but higher computational complexity than small-scale MIMO. For linear detection such as minimum mean square error (MMSE), prohibitive complexity lies in solving large-scale linear equations. For a better tradeoff between BER performance and computational complexity, iterative linear methods like conjugate gradient (CG) have been applied for massive MIMO detection. By leaving out a matrix-vector product of CG, conjugate residual (CR) further achieves lower computational complexity with similar BER performance compared to CG. Since the BER performance can be improved by utilizing pre-condition with incomplete Cholesky (IC) factorization, pre-conditioned conjugate residual (PCR) is proposed. Simulation results indicate that PCR method achieves better performance than both CR and CG methods. It has 1 dB performance improvement than CG at BER = 5 χ Analysis shows that CR achieves 20% computational complexity reduction compared with CG when antenna configuration is 128 χ 60. With the same configuration, PCR reduces complexity by 66% while achieves similar BER performance compared with the detector with Cholesky decomposition. Finally, the corresponding VLSI architecture is proposed in detail.