论文标题
天鹅：基于群的低复杂性PAPR降低方案
SWAN: Swarm-Based Low-Complexity Scheme for PAPR Reduction
论文作者
论文摘要
循环转移的部分发射序列（CS-PTS）通常用于SISO系统中，以减少OFDM信号的PAPR。与其他技术相比，CS-PTS具有出色的性能。然而，由于详尽的搜索要求，它需要过度的计算复杂性。在本文中，我们适应了CS-PTS在MIMO框架中运行，其中采用了奇异值分解（SVD）预编码。我们还提出了天鹅，这是一种基于群体智能的新型优化方法，以规避详尽的搜索。天鹅不仅可以显着降低计算复杂性，而且还达到了最佳和复杂性之间的公平平衡。通过模拟，我们表明天鹅以比其他竞争方法低得多的复杂性能实现近乎最佳的性能。
Cyclically shifted partial transmit sequences (CS-PTS) has conventionally been used in SISO systems for PAPR reduction of OFDM signals. Compared to other techniques, CS-PTS attains superior performance. Nevertheless, due to the exhaustive search requirement, it demands excessive computational complexity. In this paper, we adapt CS-PTS to operate in a MIMO framework, where singular value decomposition (SVD) precoding is employed. We also propose SWAN, a novel optimization method based on swarm intelligence to circumvent the exhaustive search. SWAN not only provides a significant reduction in computational complexity, but it also attains a fair balance between optimality and complexity. Through simulations, we show that SWAN achieves near-optimal performance at a much lower complexity than other competing approaches.
