Channel selection is a challenging task in cognitive radio vehicular networks. Vehicles have to sense the channels periodically. Due to this, a lot of time is wasted which could have been utilised for transmission of data. Employing road side units (RSUs) in sensing can prove to be useful for this purpose. The RSUs may select the channel and allocate it to the vehicles on demand. However, this sensing should be proactive. RSUs should know in advance the channel to be allocated when requested. For this purpose, a deep reinforcement learning algorithm namely deep reinforcement learning based optimal channel selection is proposed in this study for training the network according to the previously sensed data. Proposed protocol is simulated and results are compared with the existing methods. The packet delivery ratio is increased by 2%, throughput is increased by 1.8%, average delay is decreased by 2% and primary user collision ratio is reduced by 3.2% when compared with similar recent work by varying number of vehicles. On the other hand, when compared with similar recent work by varying channel availability, the packet delivery ratio is increased by 4.5 %, throughput by 4.3%, average delay is decreased by 3% and PU collision ratio by 5.5%.

中文翻译：

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基于深度强化学习的认知无线电自组织网络的最佳信道选择

在认知无线电车辆网络中，频道选择是一项艰巨的任务。车辆必须定期感应通道。因此，浪费了本来可以用于数据传输的大量时间。在感测中使用路侧单元（RSU）可能会证明对此有用。RSU可以选择信道并将其分配给车辆。但是，这种检测应该是主动的。RSU应在请求时事先知道要分配的信道。为此，本研究提出了一种深度强化学习算法，即基于深度强化学习的最优信道选择，用于根据先前感测到的数据来训练网络。仿真所提出的协议，并将结果与​​现有方法进行比较。封包传送率提高了2％，与最近的类似工作相比，不同数量的车辆相比，吞吐量增加了1.8％，平均延迟减少了2％，主要用户碰撞率减少了3.2％。另一方面，与通过改变信道可用性进行的近期类似工作相比，数据包传输率提高了4.5％，吞吐量提高了4.3％，平均延迟降低了3％，PU冲突率降低了5.5％。