Massive MIMO is a promising technology to improve spectral efficiency, cell coverage, and system capacity for 5G. However, these benefits take place at great cost of computational complexity, especially in systems wit...Massive MIMO is a promising technology to improve spectral efficiency, cell coverage, and system capacity for 5G. However, these benefits take place at great cost of computational complexity, especially in systems with hundreds of antennas at the base station. This paper aims to address the minimum mean square error(MMSE) detection in uplink massive MIMO systems utilizing the symmetric complex bi-conjugate gradients(SCBiCG) and the Lanczos method. Both the proposed methods can avoid the large scale matrix inversion which is necessary for MMSE, thus, reducing the computational complexity by an order of magnitude with respect to the number of user equipment. To enable the proposed methods for soft-output detection, we also derive an approximating calculation scheme for the log-likelihood ratios(LLRs), which further reduces the complexity. We compare the proposed methods with existing exact and approximate detection methods. Simulation results demonstrate that the proposed methods can achieve near-optimal performance of MMSE detection with relatively low computational complexity.展开更多
基金supported by Chinas 863 Project NO.2015AA01A706the National S&T Major Project NO.2014ZX03001011+1 种基金the Science and Technology Program of Beijing NO.D151100000115003the Scientific and Technological Cooperation Projects NO.2015DFT10160B
文摘Massive MIMO is a promising technology to improve spectral efficiency, cell coverage, and system capacity for 5G. However, these benefits take place at great cost of computational complexity, especially in systems with hundreds of antennas at the base station. This paper aims to address the minimum mean square error(MMSE) detection in uplink massive MIMO systems utilizing the symmetric complex bi-conjugate gradients(SCBiCG) and the Lanczos method. Both the proposed methods can avoid the large scale matrix inversion which is necessary for MMSE, thus, reducing the computational complexity by an order of magnitude with respect to the number of user equipment. To enable the proposed methods for soft-output detection, we also derive an approximating calculation scheme for the log-likelihood ratios(LLRs), which further reduces the complexity. We compare the proposed methods with existing exact and approximate detection methods. Simulation results demonstrate that the proposed methods can achieve near-optimal performance of MMSE detection with relatively low computational complexity.
