We propose a method that combines fixed point algorithms with a neural network to optimize jointly discrete and continuous variables in millimeter-wave communication systems, so that the users' rates are allocated fairly in a well-defined sense. In more detail, the discrete variables include user-access point assignments and the beam configurations, while the continuous variables refer to the power allocation. The beam configuration is predicted from user-related information using a neural network. Given the predicted beam configuration, a fixed point algorithm allocates power and assigns users to access points so that the users achieve the maximum fraction of their interference-free rates. The proposed method predicts the beam configuration in a "one-shot" manner, which significantly reduces the complexity of the beam search procedure. Moreover, even if the predicted beam configurations are not optimal, the fixed point algorithm still provides the optimal power allocation and user-access point assignments for the given beam configuration.

翻译：我们提出一种方法,将固定点算法与神经网络结合起来,优化毫米波通信系统中的离散和连续变量,使用户的费率得到明确界定的公平分配。更详细地说,离散变量包括用户接入点任务和波束配置,而连续变量则指功率分配。波束配置是使用神经网络从与用户有关的信息中预测的。根据预测的波束配置,固定点算法分配权力,并指派用户访问点,以便用户实现无干扰率的最大部分。拟议方法以“一发式”的方式预测波束配置,这大大降低了波束搜索程序的复杂性。此外,即使预测的波束配置不理想,定点算法仍然为给定的波纹配置提供最佳的功率分配和用户接入点分配。