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Infrastructure based spectrum sensing scheme in VANET using reinforcement learning

机译：使用强化学习的VANET中基于基础设施的频谱感知方案

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Spectrum sensing is one of the fundamental functionality performed by a cognitive radio to identify vacant radio spectrum for dynamic spectrum access (DSA). However, there are many challenges still existing before the benefits of DSA can be realized. The challenges include multipath fading, shadowing and hidden primary user (PU) problem. The challenges are more severe in vehicular communication due to unique characteristics such as dynamic topology caused by vehicle mobility. Furthermore, spectrum sensing is dependent on the activities of the PU traffic pattern which are not known in advance. In a typical cognitive radio network, the PU plays a passive role. Therefore, a sensing technique should account for traffic pattern of the PU autonomously. However, most of the proposed spectrum sensing schemes in vehicular communication assumes a static ON/OFF PU model which does not realistically model the PU traffic pattern. In this paper, we propose reinforcement learning (RL) to model the traffic pattern of the PU and use the model to predict channels likely to be free in future. The RL is implemented on road side unit (RSU) which send predicted vacant PU channels to vehicles on the road. Before the channels can be used, vehicles perform spectrum sensing. To account for multipath fading and shadowing, adaptive spectrum sensing is proposed. The results from spectrum sensing, sensing time and PU channel capacity are calculated into a scalar value and used as reward for RL at RSU. The RSU continuously update the reward for channels of interest using sensing history from passing vehicles as reward. Compared to history based schemes from literature, the RL technique proposed in this paper performs better. (C) 2019 Elsevier Inc. All rights reserved.
机译：频谱感测是认知无线电执行的基本功能之一，用于识别空频谱以用于动态频谱访问（DSA）。但是，在实现DSA的好处之前，仍然存在许多挑战。挑战包括多径衰落，阴影和隐藏的主要用户（PU）问题。由于诸如车辆移动性引起的动态拓扑之类的独特特性，在车辆通信中的挑战更加严峻。此外，频谱感测取决于PU业务模式的活动，该活动事先未知。在典型的认知无线电网络中，PU扮演被动角色。因此，感测技术应该自主地考虑PU的业务模式。然而，大多数在车辆通信中提出的频谱感测方案假定静态的ON / OFF PU模型，而该模型不能现实地对PU业务模式进行建模。在本文中，我们提出了强化学习（RL）来对PU的流量模式进行建模，并使用该模型来预测将来可能免费的频道。 RL在路侧单元（RSU）上实施，该单元将预测的空闲PU通道发送到道路上的车辆。在使用通道之前，车辆必须执行频谱感测。为了解决多径衰落和阴影，提出了自适应频谱感测。频谱感测，感测时间和PU信道容量的结果被计算为标量值，并用作RSU的RL奖励。 RSU使用来自过往车辆的感测历史作为奖励，不断更新感兴趣频道的奖励。与文献中基于历史的方案相比，本文提出的RL技术表现更好。 （C）2019 Elsevier Inc.保留所有权利。

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