With the increasing use of cognitive radio technology in vehicular communications, vehicles will be enabled with cognitive radio in the future. Cognitive radio assisted vehicular networks make cognitive radio enabled vehicles utilize licensed spectrum on highways opportunistically. This work tackles cognitive radio assisted vehicular networks including M primary users (transmitter), M primary receivers, a secondary user (transmitter) with K channels and K secondary receivers. A channel is assigned to each primary user for data transmission whenever they need to use. The secondary user has data backlog. It knows neither the states (good or bad) of M channels, or statistics of channel evolution processes. It selects K of M channels to send data at each time slot. If it senses that a selected channel is in good state, it uses that channel for data packet transmission in same time slot. If not, it chooses another channel at the next time slot. We tackle this problem under average reward criteria for finite and infinite time horizon. This work presents Uniforming Random Ordered Policy (UROP) and shows UROP achieves near-optimal throughput under block fading assumption for generic channel evolution process. Numerical results show that our policy achieves near-optimal throughput (more than 80% of optimal throughput for scarce number of available channels) under average reward criteria whereas MP may achieve less than even 25% of optimal throughput for scarce number of available channels.

随着认知无线电技术在车辆通信中的使用越来越多，未来车辆将启用认知无线电。认知无线电辅助车辆网络使支持认知无线电的车辆机会性地利用高速公路上的许可频谱。这项工作处理认知无线电辅助车载网络，包括M个主用户（发射器）、M个主接收器、一个具有K个通道和K个辅助接收器的次要用户（发射器）。为每个主要用户分配一个信道，以便在他们需要使用时进行数据传输。次要用户有数据积压。它不知道M的状态（好或坏）渠道，或渠道演变过程的统计。它在每个时隙选择M个通道中的K个发送数据。如果它检测到一个选定的通道处于良好状态，它会使用该通道在同一时隙中传输数据包。如果不是，它在下一个时隙选择另一个频道。我们在有限和无限时间范围的平均奖励标准下解决这个问题。这项工作提出了统一随机有序策略 (UROP)并显示 UROP 在通用信道演进过程的块衰落假设下实现了近乎最佳的吞吐量。数值结果表明，我们的策略在平均奖励标准下实现了接近最优的吞吐量（超过 80% 的可用信道数量稀少的最优吞吐量），而 MP 可能实现低于可用信道数量稀少的最优吞吐量的 25%。