看到不可观察的：无线通信网络的信道学习
Seeing the Unobservable: Channel Learning for Wireless Communication Networks
摘要：
无线通信网络严重依赖于信道状态信息（CSI）来为信号处理和网络操作做出明智的决策。然而，传统的CSI捕获方法面临着许多困难：导频辅助的信道训练消耗了大量的信道资源，减少了节能的机会;而位置辅助的信道估计存在位置信息不准确和不充分的问题。在本文中，我们提出了一种新的信道学习框架，它可以解决这些困难，从可观察的CSI推断不可观察的。我们从理论上制定了这个框架，并说明了一个特殊的情况下，可以保证不可观测CSI的可学习性。然后描述信道学习的可能应用，包括多层网络中的小区选择、用于设备到设备（D2 D）通信的设备发现、以及用于负载均衡的端到端用户关联。我们还提出了一个基于神经网络的算法在多层网络中的细胞选择问题。该算法的性能进行评估，使用基于几何的随机信道模型（GSCM）。在具有5个小小区的设置中，平均小区选择准确度为73% -与需要真实位置信息的位置辅助算法相比仅损失3.9%。
英文摘要：
Wireless communication networks rely heavily on channel state information (CSI) to make informed decision for signal processing and network operations. However, the traditional CSI acquisition methods is facing many difficulties: pilot-aided channel training consumes a great deal of channel resources and reduces the opportunities for energy saving, while location-aided channel estimation suffers from inaccurate and insufficient location information. In this paper, we propose a novel channel learning framework, which can tackle these difficulties by inferring unobservable CSI from the observable one. We formulate this framework theoretically and illustrate a special case in which the learnability of the unobservable CSI can be guaranteed. Possible applications of channel learning are then described, including cell selection in multi-tier networks, device discovery for device-to-device (D2D) communications, as well as end-to-end user association for load balancing. We also propose a neuron-network-based algorithm for the cell selection problem in multi-tier networks. The performance of this algorithm is evaluated using geometry-based stochastic channel model (GSCM). In settings with 5 small cells, the average cell-selection accuracy is 73% - only a 3.9% loss compared with a location-aided algorithm which requires genuine location information.
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