Performance Analysis of Convex Data Detection in MIMO
Performance Analysis of Convex Data Detection in MIMO
复制标题
MIMO中凸数据检测的性能分析
DOI：
10.1109/icassp.2019.8683890
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
B. Hassibi
中科院分区：
文献类型：
--
作者：
Ehsan Abbasi;Fariborz Salehi;B. Hassibi
研究了一种凸数据检测方法在大型多输入多输出（MIMO）系统中的性能。我们的目标是恢复一个n维复信号，其条目是从一个任意的星座$\mathcal{D} \subset \mathbb{C}$，使用m噪声线性测量。由于最大似然（ML）估计涉及在离散集${\mathcal{D}^n}$上最小化损失函数，因此对于大n，它在计算上变得难以处理。一种方法是放松到一个$\mathcal{D}$凸集，并利用凸规划来精确地解决问题，然后将答案映射到集合$\mathcal{D}$中的最近点。我们假设他的身份。复高斯信道矩阵，并推导出了在m，n → ∞极限下所提出的凸方法的误符号概率表达式。以前的工作只能这样做的真实的价值星座，如BPSK和PAM。本文的主要贡献是将结果推广到复值星座。特别是，我们使用我们的主要定理来计算PSK和QAM星座的复杂算法的性能。此外，我们引入了一个封闭形式的公式，在高信噪比制度的符号错误概率，并确定一致的信号恢复所需的测量的最小数量m。
We study the performance of a convex data detection method in large multiple-input multiple-output (MIMO) systems. The goal is to recover an n-dimensional complex signal whose entries are from an arbitrary constellation $\mathcal{D} \subset \mathbb{C}$, using m noisy linear measurements. Since the Maximum Likelihood (ML) estimation involves minimizing a loss function over the discrete set ${\mathcal{D}^n}$, it becomes computationally intractable for large n. One approach is to relax to a $\mathcal{D}$ convex set and to utilize convex programing to solve the problem precise and then to map the answer to the closest point in the set $\mathcal{D}$. We assume an i.i.d. complex Gaussian channel matrix and derive expressions for the symbol error probability of the proposed convex method in the limit of m, n → ∞. Prior work was only able to do so for real valued constellations such as BPSK and PAM. The main contribution of this paper is to extend the results to complex valued constellations. In particular, we use our main theorem to calculate the performance of the complex algorithm for PSK and QAM constellations. In addition, we introduce a closed-form formula for the symbol error probability in the high-SNR regime and determine the minimum number of measurements m required for consistent signal recovery.