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Total Throughput Maximization of Cooperative Cognitive Radio Networks With Energy Harvesting

机译:带能量收集的协作认知无线电网络的总吞吐​​量最大化

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Cognitive radio and energy harvesting techniques have provided significant benefits in terms of spectrum reuse and lifetime prolongation for conventional wireless networks. We are thus motivated to consider the energy harvesting cognitive radio networks (CRNs) consisting of multiple primary users (PUs) and secondary users (SUs). We introduce two cooperation modes: the energy cooperation mode and joint cooperation mode. In the energy cooperation mode, there only exists energy cooperation between PUs and SUs, i.e., the SU transmits its own packets by using the energy harvested from primary signals. In the joint cooperation mode, the SU relays primary packets by using the energy harvested from primary signals. In each cooperation mode of three representational scenarios (the CRN with one pair of PUs and one pair of SUs, the CRN with two pairs of PUs and one pair of SUs, and the CRN with one pair of PUs and two pairs of SUs) and the general scenario, we exploit the optimal time allocation between PUs and SUs, and balance the tradeoff between energy harvesting and packet transmission to obtain the maximum total achievable throughput. To be specific, we first formulate the throughput maximization problems as non-linear optimization problems, and then prove them as convex problems by monotonicity analysis. Moreover, we obtain the closed-form optimal solution in the energy cooperation mode. We prove the existence of the optimal solution in the joint cooperation mode, obtain the upper and lower bounds, and provide numerical analysis for the optimal solution. Finally, we highlight the benefits of information cooperation and the impact of multi-user gain on the maximum of the total achievable throughput.
机译:在常规无线网络的频谱重用和寿命延长方面,认知无线电和能量收集技术已提供了显着优势。因此,我们有动力考虑由多个主要用户(PU)和次要用户(SU)组成的能量收集认知无线电网络(CRN)。我们介绍了两种合作模式:能源合作模式和联合合作模式。在能量协作模式下,PU和SU之间仅存在能量协作,即SU通过使用从一次信号中收集的能量来传输自己的数据包。在联合协作模式下,SU通过使用从主要信号中收集的能量来中继主要数据包。在三种代表性场景的每种协作模式下(带有一对PU和一对SU的CRN,带有两对PU和一对SU的CRN和带有一对PU和两对SU的CRN)和在一般情况下,我们利用PU和SU之间的最佳时间分配,并平衡能量收集和数据包传输之间的权衡,以获得可实现的最大总吞吐量。具体而言,首先将吞吐量最大化问题公式化为非线性优化问题,然后通过单调性分析将其证明为凸问题。此外,我们在能量合作模式下获得了封闭形式的最优解。证明了联合合作模式下最优解的存在性,求出上下界,并为最优解提供了数值分析。最后,我们强调了信息合作的好处以及多用户收益对可实现的总吞吐量最大值的影响。

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