The interest on applications where machine learning algorithms and communications are combined has been on a rising in recent years. Machine learning and neural networks are being advocated as a way of improving the performance of several functions across all layers of future communication systems. Furthermore, in applications where complexity reduction is essential for the system feasibility at the cost of an affordable performance loss, more efficient systems might be achieved with the aid of machine learning algorithms. Signal detection for multiple-input multiple-output (MIMO) systems has become a hot topic in recent years given its prominent role in fourth and fifth generations of mobile networks. However, the computational complexity in MIMO systems can become prohibitive when the number of antennas increases. Therefore, by leveraging neural networks architectures we propose a deep unfolded detector, whereby the algorithm of the probability data association (PDA) detector is adapted and enhanced by means of neural network learning capabilities. We unveil that the proposed detector is orders-of-magnitude less complex than the PDA detector, yet presenting no severe penalties in performance in terms of bit error rate (BER).

近年来，人们对机器学习算法和通信相结合的应用的兴趣一直在上升。机器学习和神经网络被提倡作为一种提高未来通信系统所有层的若干功能性能的方法。此外，在以可承受的性能损失为代价降低复杂性对于系统可行性至关重要的应用中，可以借助机器学习算法来实现更高效的系统。鉴于其在第四代和第五代移动网络中的突出作用，多输入多输出 (MIMO) 系统的信号检测已成为近年来的热门话题。然而，当天线数量增加时，MIMO 系统的计算复杂度可能会变得过高。所以，通过利用神经网络架构，我们提出了一种深度展开检测器，其中概率数据关联 (PDA) 检测器的算法通过神经网络学习能力进行了调整和增强。我们揭示了所提出的检测器比 PDA 检测器复杂几个数量级，但在误码率 (BER) 方面没有表现出严重的性能损失。