Wireless communications have been at the center of the revolution in technology for the last few years. The 5G communication system is the pinnacle of these technologies; however 4G LTE, WiFi, and even satellite technologies are still employed worldwide. So, the aim of the next generation network is to take advantage of these technologies for the better of the end users. Our research analyzes this subject and reveals a new and intelligent method that allows users to select the suitable RAT at each time and, therefore, to switch to another RAT if necessary. The Deep Q Network DQN algorithm was utilized, which is a reinforcement learning algorithm that determines judgments based on antecedent actions (rewards and punishments). The approach exhibits a high accuracy, reaching 93 percent, especially after a given number of epochs (the exploration phase), compared to typical MADM methods where the accuracy does not exceed 75 percent

翻译：无线通信技术在过去几年一直处于技术革命的核心。5G通信系统是这些技术的顶峰；然而，4G LTE、WiFi甚至卫星技术仍在全球范围内广泛使用。因此，下一代网络的目标是利用这些技术以更好地服务于终端用户。本研究分析了这一主题，并提出了一种新颖的智能方法，使用户能够在每个时刻选择合适的无线接入技术（RAT），并在必要时切换到另一RAT。该方法采用了深度Q网络（DQN）算法，这是一种基于先前行动（奖励与惩罚）进行决策的强化学习算法。与典型的多属性决策方法（其准确率不超过75%）相比，该方法展现出高准确率，尤其在经过一定轮次的探索阶段后，准确率可达93%。