Low-complexity soft-output detection for massive MIMO using SCBiCG and Lanczos methods
Low-complexity soft-output detection for massive MIMO using SCBiCG and Lanczos methods
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DOI：
10.1109/cc.2015.7386166
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发表时间：
2015-12
影响因子：
4.1
通讯作者：
Xiao Chiyang;Su Xin;Zeng Jie;Rong Li-ping;X. Xibin;Wang Jing
中科院分区：
文献类型：
--
作者：
Xiao Chiyang;Su Xin;Zeng Jie;Rong Li-ping;X. Xibin;Wang Jing
大规模MIMO是一项很有前途的技术，可以提高5G的频谱效率、小区覆盖和系统容量。然而，这些好处是以计算复杂性为代价的，特别是在基站有数百个天线的系统中。本文旨在利用对称复双共轭梯度（SCBiCG）和Lanczos方法解决上行海量MIMO系统中的最小均方误差（MMSE）检测问题。这两种方法都避免了MMSE所需的大规模矩阵反演，从而将计算复杂度相对于用户设备的数量降低了一个数量级。为了使所提出的方法能够用于软输出检测，我们还推导了对数似然比（llr）的近似计算方案，从而进一步降低了复杂性。我们将所提出的方法与现有的精确和近似检测方法进行了比较。仿真结果表明，该方法能够以较低的计算复杂度获得接近最优的MMSE检测性能。
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.