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Energy-efficient cooperative cognitive relaying schemes for cognitive radio networks

机译：认知无线电网络的节能合作认知中继方案

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摘要

We investigate a cognitive radio network in which a primary user (PU) may cooperate with a cognitive radio user (i.e., a secondary user (SU)) for transmissions of its data packets. The PU is assumed to be a buffered node operating in a time-slotted fashion where the time is partitioned into equal-length slots. We develop two schemes which involve cooperation between primary and secondary users. To satisfy certain quality of service (QoS) requirements, users share time slot duration and channel frequency bandwidth. Moreover, the SU may leverage the primary feedback message to further increase both its data rate and satisfy the PU QoS requirements. The proposed cooperative schemes are designed such that the SU data rate is maximized under the constraint that the PU average queueing delay is maintained less than the average queueing delay in case of non-cooperative PU. In addition, the proposed schemes guarantee the stability of the PU queue and maintain the average energy emitted by the SU below a certain value. The proposed schemes also provide more robust and potentially continuous service for SUs compared to the conventional practice in cognitive networks where SUs transmit in the spectrum holes and silence sessions of the PUs. We include primary source burstiness, sensing errors, and feedback decoding errors to the analysis of our proposed cooperative schemes. The optimization problems are solved offline and require a simple 2-dimensional grid-based search over the optimization variables. Numerical results show the beneficial gains of the cooperative schemes in terms of SU data rate and PU throughput, average PU queueing delay, and average PU energy savings.
机译：我们研究了认知无线电网络，其中主要用户（PU）可以与认知无线电用户（即次要用户（SU））合作以传输其数据包。假定PU是以时隙方式操作的缓冲节点，其中时间被划分为等长时隙。我们开发了两个计划，涉及主要和次要用户之间的合作。为了满足某些服务质量（QoS）要求，用户共享时隙持续时间和信道频率带宽。此外，SU可以利用主要反馈消息来进一步增加其数据速率并满足PU QoS要求。设计所提出的协作方案，使得在保持PU平均排队延迟小于非协作PU情况下的平均排队延迟的约束下，SU数据速率最大。另外，所提出的方案保证了PU队列的稳定性，并将SU发出的平均能量保持在一定值以下。相比于认知网络中的常规实践，所提出的方案还为SU提供了更健壮和潜在的连续服务，在认知网络中，SU在PU的频谱孔和静默会话中进行传输。我们将主要源突发性，感测错误和反馈解码错误包括在内，以分析我们提出的合作方案。优化问题可以离线解决，并且需要对优化变量进行简单的基于二维网格的搜索。数值结果表明，在SU数据速率和PU吞吐量，平均PU排队延迟和平均PU能量节省方面，这些合作方案均有益。

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