基于反应式禁忌搜索的高维线性矢量信道近ML信号检测
Near-ML Signal Detection in Large-Dimension Linear Vector Channels Using Reactive Tabu Search
摘要：
低复杂度的近最佳信号检测在大规模通信系统中是一个挑战。在本文中，我们提出了一个反应禁忌搜索（RTS）算法，一个启发式的组合优化技术，实现低复杂度的近最大似然（ML）信号检测的线性向量信道与大尺寸。两个实际上重要的大尺寸线性矢量信道被认为是：i）多输入多输出（MIMO）信道与大量（数十）的发射和接收天线，和ii）严重延迟扩展MIMO符号间干扰（ISI）信道与大量（数十至数百）的多径分量。这些信道是令人感兴趣的，因为前者提供了增加的频谱效率（几十bps/Hz）的益处，而后者提供了高时间分集阶数的益处。我们的仿真结果表明，虽然包括球形解码变体的算法在大维度上不能很好地扩展，但所提出的RTS算法在大维度上可以很好地扩展信号检测，同时随着维度数量的增加，其性能越来越接近ML。
英文摘要：
Low-complexity near-optimal signal detection in large dimensional communication systems is a challenge. In this paper, we present a reactive tabu search (RTS) algorithm, a heuristic based combinatorial optimization technique, to achieve low-complexity near-maximum likelihood (ML) signal detection in linear vector channels with large dimensions. Two practically important large-dimension linear vector channels are considered: i) multiple-input multiple-output (MIMO) channels with large number (tens) of transmit and receive antennas, and ii) severely delay-spread MIMO inter-symbol interference (ISI) channels with large number (tens to hundreds) of multipath components. These channels are of interest because the former offers the benefit of increased spectral efficiency (several tens of bps/Hz) and the latter offers the benefit of high time-diversity orders. Our simulation results show that, while algorithms including variants of sphere decoding do not scale well for large dimensions, the proposed RTS algorithm scales well for signal detection in large dimensions while achieving increasingly closer to ML performance for increasing number of dimensions.
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