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Hybrid Scheduling in Heterogeneous Half- and Full-Duplex Wireless Networks

机译:异构半和全双工网络中的混合调度

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摘要

Full-duplex (FD) wireless is an attractive communication paradigm with high potential for improving network capacity and reducing delay in wireless networks. Despite significant progress on the physical layer development, the challenges associated with developing medium access control (MAC) protocols for heterogeneous networks composed of both legacy half-duplex (HD) and emerging FD devices have not been fully addressed. Therefore, we focus on the design and performance evaluation of scheduling algorithms for infrastructure-based heterogeneous HD-FD networks (composed of HD and FD users). We first show that centralized Greedy Maximal Scheduling (GMS) is throughput-optimal in heterogeneous HD-FD networks. We propose the Hybrid-GMS (H-GMS) algorithm, a distributed implementation of GMS that combines GMS and a queue-based random-access mechanism. We prove that H-GMS is throughput-optimal. Moreover, we analyze the delay performance of H-GMS by deriving lower bounds on the average queue length. We further demonstrate the benefits of upgrading HD nodes to FD nodes in terms of throughput gains for individual nodes and the whole network. Finally, we evaluate the performance of H-GMS and its variants in terms of throughput, delay, and fairness between FD and HD users via extensive simulations. We show that in heterogeneous HD-FD networks, H-GMS achieves 16- $30imes $ better delay performance and improves fairness between HD and FD users by up to 50% compared with the fully decentralized Q-CSMA algorithm.
机译:全双工(FD)无线是一个有吸引力的通信范例,具有提高网络容量和减少无线网络延迟的高潜力。尽管物理层开发的显着进展,但是与开发媒体访问控制(MAC)协议的挑战尚未完全解决由传统半双工(HD)和新兴FD设备组成的异构网络。因此,我们专注于基于基础设施的异构HD-FD网络调度算法的设计和性能评估(由HD和FD用户组成)。我们首先显示集中贪婪最大调度(GMS)是异构高压FD网络中的吞吐量最优。我们提出了混合-GMS(H-GMS)算法,该算法,包括基于GMS的GMS的分布式实现和基于队列的随机接入机制。我们证明H-GMS是吞吐量最佳。此外,我们通过在平均队列长度上导出下限来分析H-GM的延迟性能。我们进一步展示了在各个节点和整个网络的吞吐量增益方面将高清节点升级到FD节点的好处。最后,我们通过广泛的模拟评估了在FD和HD用户之间的吞吐量,延迟和公平方面的H-GMS及其变体的性能。我们认为,在异构的HD-FD网络中,与完全分散的Q-CSMA算法相比,H-GMS在H-GMS实现16-30倍以上,更好地提高了高达50%的高达50%的公平性。

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