Maximum likelihood detection is infeasible in uplink multiuser massive multiple-input and multiple-output (m-MIMO) systems due to the large dimension of the MIMO systems. Accordingly, suboptimal or near-optimal alternatives like linear minimum mean square error (LMMSE) detector and Zero Forcing (ZF) are used. However, the LMMSE and the ZF detectors need matrix inversion, which is computationally costly. We propose two detection schemes for massive MIMO, which compute an approximate inverse based on the Cayley-Hamilton theorem, and have quadratic complexity in the number of users. Simulation results exhibit the similarity of the BER performance of the proposed schemes to that of the ideal ZF or LMMSE.

中文翻译：

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大规模 MIMO 系统的低复杂度信号检测

由于 MIMO 系统的大维度，最大似然检测在上行链路多用户大规模多输入多输出 (m-MIMO) 系统中是不可行的。因此，使用了次优或接近最优的替代方案，例如线性最小均方误差 (LMMSE) 检测器和迫零 (ZF)。然而，LMMSE 和 ZF 检测器需要矩阵求逆，这在计算上是昂贵的。我们提出了两种大规模 MIMO 检测方案，它们基于 Cayley-Hamilton 定理计算近似逆，并且在用户数量上具有二次复杂度。仿真结果表明，所提出方案的误码率性能与理想的 ZF 或 LMMSE 的误码率性能相似。