CMDNet：学习离散变量的概率松弛，用于低复杂度的软检测
CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity
摘要：
随着机器学习（ML），特别是深度神经网络（DNN）在2010年代在许多研究领域的巨大成功，提出了几种基于ML的方法用于大型逆线性问题的检测，例如，大规模多输入多输出系统背后的主要动机是，最大后验概率（MAP）检测的复杂度随着系统维度呈指数增长。而不是使用DNN，本质上是一个黑盒子，我们采取了一种略有不同的方法，并引入了离散变量的概率连续松弛来进行MAP检测。使密切的近似和不断优化，我们推导出一个迭代检测算法：混凝土MAP检测（CMD）。此外，通过深入展开到CMDNet的想法来扩展CMD，我们允许对不同工作点的少量参数进行（在线）优化，同时限制复杂性。与最近基于DNN的方法相比，我们基于信息论选择CMDNet的优化标准和输出，从而能够学习单个最佳检测器的近似概率。这对于当今通信系统中的软解码是至关重要的。MIMO系统中的数值模拟结果显示，CMDNet具有良好的精度复杂性权衡相比，最先进的。值得注意的是，我们证明CMDNet的软输出是可靠的解码器。
英文摘要：
Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems. The main motivation behind is that the complexity of Maximum A-Posteriori (MAP) detection grows exponentially with system dimensions. Instead of using DNNs, essentially being a black-box, we take a slightly different approach and introduce a probabilistic Continuous relaxation of disCrete variables to MAP detection. Enabling close approximation and continuous optimization, we derive an iterative detection algorithm: Concrete MAP Detection (CMD). Furthermore, extending CMD by the idea of deep unfolding into CMDNet, we allow for (online) optimization of a small number of parameters to different working points while limiting complexity. In contrast to recent DNN-based approaches, we select the optimization criterion and output of CMDNet based on information theory and are thus able to learn approximate probabilities of the individual optimal detector. This is crucial for soft decoding in today's communication systems. Numerical simulation results in MIMO systems reveal CMDNet to feature a promising accuracy complexity trade-off compared to State of the Art. Notably, we demonstrate CMDNet's soft outputs to be reliable for decoders.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
