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.

中文翻译：

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具有能量收集的协作认知无线电网络的总吞吐​​量最大化

认知无线电和能量收集技术在传统无线网络的频谱重用和寿命延长方面提供了显着的好处。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)。我们介绍两种合作模式：能源合作模式和联合合作模式。在能量合作模式下，PU和SU之间只存在能量合作，即SU利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 使用从主信号中获取的能量来中继主数据包。在三种代表性场景（具有一对 PU 和一对 SU 的 CRN，具有两对 PU 和一对 SU 的 CRN，以及具有一对 PU 和两对 SU 的 CRN）和一般场景，我们利用 PU 和 SU 之间的最佳时间分配，并平衡能量收集之间的权衡和数据包传输以获得最大可实现的总吞吐量。具体来说，我们首先将吞吐量最大化问题表述为非线性优化问题，然后通过单调性分析证明它们是凸问题。此外，我们在能源合作模式下获得了封闭形式的最优解。我们证明了联合协作模式下最优解的存在性，得到了上下界，并对最优解进行了数值分析。最后，