论文标题
深层上下文强盗，以基于MMWave的用户以用户为中心的超密集网络快速初始访问
Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks
论文作者
论文摘要
建议基于多输入多输出（MIMO）具有用户为中心（UC）超密集（UD）网络，以促进未来网络的高吞吐量要求。由于MMWave的阻塞敏感性很高，因此连接可能经常下降。因此，初始访问（IA）中有效且快速的光束管理至关重要。当前的细胞系统使用基于光束扫描的IA机制。 UC UD概念需要其所有访问点（AP）才能执行IA。这导致正交无线电资源短缺。非正交资源分配会导致干扰，从而导致较高的误导概率。在本文中，我们提出了一种基于新型的深层背景强盗（DCB）方法，以在基于MMWave的UC UD网络中执行快速有效的IA。 DCB模型使用用户的一个参考信号来预测IA光束。参考信号的使用减少改善了光束发现延迟，并放松了无线电资源的要求。射线追踪和基于随机通道模型的模拟表明，建议的系统在基于MMWave的UC UD网络中的梁误差和梁发现延迟方面优于其光束扫描对应物。
Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high throughput requirements of future networks. Due to the high blockage susceptibility of mmWave, the connections may drop frequently. Hence efficient and fast beam management in initial access (IA) is essential. Current cellular systems use beam sweeping based IA mechanisms. UC UD concept requires all of its access points (APs) to perform IA. This leads to a shortage of orthogonal radio resources. Nonorthogonal resource allocation causes interference which leads to a higher misdetection probability. In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks. The DCB model uses one reference signal from the user to predict the IA beam. The reduced use of reference signals improves beam discovery delay and relaxes the requirement for radio resources. Ray-tracing and stochastic channel model-based simulations show that the suggested system outperforms its beam sweeping counterpart in terms of probability of beam misdetection and beam discovery delay in mmWave based UC UD networks.
