Decentralized heuristic access policy design for two cognitive secondary users under a primary Type-I HARQ process

Roghayeh Joda, M. Zorzi
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引用次数: 1

Abstract

In this paper, decentralized heuristic access policies are designed for two secondary users (SUs) in an underlay cognitive radio network in which the PU employs Type-I Hybrid ARQ. Exploiting the redundancy in PU retransmissions, each SU receiver applies interference cancellation (IC) to remove a successfully decoded PU message in the subsequent PU retransmissions. There is no central control unit and we consider two different scenarios. In the first scenario, each SU transmitter only knows the PU message knowledge state of its corresponding SU receiver. In the second scenario, the SU transmitters are unaware of any PU message knowledge states at the SU receivers. We design access policies in both offline and online modes, where in an online mode each SU can learn from its own local information. Using heuristic approaches for the considered scenarios, decentralized access policies are proposed to maximize the average sum throughput of SUs under a PU throughput degradation constraint. The results also show that heuristic access policies using the online mode have a performance gain close to that obtained using the offline mode, while providing increased robustness and flexibility.
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基于i型HARQ进程的两个认知辅助用户的分散启发式访问策略设计
本文针对底层认知无线网络中的两个辅助用户(su)设计了分散启发式访问策略,其中PU采用i型混合ARQ。利用PU重传中的冗余,每个SU接收器在随后的PU重传中应用干扰消除(IC)来删除成功解码的PU消息。没有中央控制单元,我们考虑两种不同的情况。在第一种场景中,每个SU发送方只知道其对应SU接收方的PU消息知识状态。在第二个场景中,SU发射器不知道SU接收器上的任何PU消息知识状态。我们设计了离线和在线模式下的访问策略,在在线模式下,每个SU可以从自己的本地信息中学习。针对所考虑的场景,采用启发式方法,提出了分散访问策略,以在PU吞吐量下降约束下最大化su的平均总吞吐量。结果还表明,使用在线模式的启发式访问策略的性能增益接近使用离线模式获得的性能增益,同时提供了更高的鲁棒性和灵活性。
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