Linear and Deep Neural Network-Based Receivers for Massive MIMO Systems With One-Bit ADCs
Linear and Deep Neural Network-Based Receivers for Massive MIMO Systems With One-Bit ADCs
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DOI：
10.1109/twc.2021.3082844
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发表时间：
2020-08
影响因子：
10.4
通讯作者：
Ly V. Nguyen;A. L. Swindlehurst;D. Nguyen
中科院分区：
文献类型：
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作者：
Ly V. Nguyen;A. L. Swindlehurst;D. Nguyen
使用一位模数转换器（ADC）是降低大规模多输入多输出（MIMO）系统中的成本和功耗的实用解决方案。然而，1位ADC引起的失真使得数据检测任务更具挑战性。在本文中，我们提出了一个两阶段的检测方法的大规模MIMO系统与一位ADC。在第一阶段中，我们提出了几个线性接收器的基础上的Bussgang分解，表现出显着的性能增益比传统的线性接收器。接下来，我们重新制定的最大似然（ML）检测问题，以解决其非鲁棒性。基于重新定义的ML检测问题，我们提出了一种模型驱动的基于深度神经网络的检测器，即OBMNet，其性能与现有的基于支持向量机的接收器相当，尽管计算复杂度要低得多。然后，提出了一种最近邻搜索方法的第二阶段，以改善第一阶段的解决方案。与通常在大的候选集上执行搜索的现有搜索方法不同，所提出的搜索方法生成有限数量的最可能的候选，从而限制了搜索复杂度。数值结果证实了所提出的两阶段检测方法的低复杂度，效率和鲁棒性。
The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.