论文标题
大型MU-MIMO MMWAVE系统的稀疏Beamspace均衡
Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems
论文作者
论文摘要
我们提出了基于均衡的数据检测算法，用于全数字毫米波（MMWAVE）大量多源多输入多输入（MU-MIMO）系统，以利用Beamspace域中的稀疏性来降低复杂性。我们提供了对用户数量，基础天线和通道稀疏性的条件，而频道的频率比传统的天线域处理可能不那么复杂。我们使用具有现实的MMWave通道模型的模拟来评估现有和新的Beamspace均衡算法的性能复杂性权衡。我们的结果表明，假设在频道相干间隔内有足够数量的传输，我们提出的一种Beamspace均衡算法在视线条件下的复杂性降低高达8倍。
We propose equalization-based data detection algorithms for all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-out (MU-MIMO) systems that exploit sparsity in the beamspace domain to reduce complexity. We provide a condition on the number of users, basestation antennas, and channel sparsity for which beamspace equalization can be less complex than conventional antenna-domain processing. We evaluate the performance-complexity trade-offs of existing and new beamspace equalization algorithms using simulations with realistic mmWave channel models. Our results reveal that one of our proposed beamspace equalization algorithms achieves up to 8x complexity reduction under line-of-sight conditions, assuming a sufficiently large number of transmissions within the channel coherence interval.
