基于Barzilai-Borwein迭代的低复杂度大规模MIMO信号检测算法

在大规模多输入多输出系统中,最小均方误差(minimum mean square error, MMSE)算法能达到接近最优的线性信号检测性能,但是MMSE算法需要复杂的矩阵求逆运算,这限制了该算法的应用。为了降低运算复杂度,改进MMSE算法,利用Barzilai Borwein(BB)迭代算法来避免矩阵求逆运算,提出了结构简单的BB迭代信号检测算法,且基于信道硬化特性进一步优化了迭代初始解以加快算法的收敛速度。理论和仿真结果表明,所提出的BB迭代算法的性能优于最近提出的Neumann级数展开算法,而其复杂度相比截短阶数i=3的Neumann级数展开算法减少了一个数量级;且该算法收敛速度较快,在给定初始值的条件下,通过简单的几次迭代,能够快速接近MMSE算法的检测性能。

Barzilai-Borwein based signal detection algorithm for massive MIMO

For massive multiple input multiple output (MIMO) system, the minimum mean square error (MMSE) linear detection algorithm can achieve near optimal performance. However, the MMSE linear detection involves complicated matrix inverse, which limits the application of the MMSE detection algorithm. To reduce the operation complication, a modified MMSE algorithm is proposed which uses a structure simple Barzilai Borwein (BB) iterative algorithm to avoid the matrix inverse. The BB iterative technology is introduced in massive MIMO signal detection, and the initial iteration value is also optimized based on the characteristic of channel hardening to further quicken the convergence of the iterative process. The theoretical and simulated results show that the proposed BB iterative detection algorithm performs better than the recently proposed Neumann series approximation algorithm, while the computational complexity is reduced by about one order compared to the Neumann series approximation algorithm whose truncated order i equals 3. Moreover, the convergence rate of the proposed algorithm is fast. Under a given initial value, it can achieve performance very close to the MMSE algorithm with just several iterations.