Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NOMA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated under both the perfect and imperfect channel state information (CSI). An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively under the perfect CSI case. A safe approximation and the $ \mathcal {S}$ -procedure are used to address the non-convex infinite inequality constraints of the problem under the imperfect CSI case. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes. Moreover, it is shown that both SE and EE of the proposed algorithm under the imperfect CSI can be significantly improved by exploiting IRS.

中文翻译：

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使用 NOMA 的 IRS 辅助 CRN 中频谱和能效权衡的多目标优化

非正交多址接入 (NOMA) 因其高频谱效率 (SE)、能源效率 (EE) 和更好的连接性而成为第六代无线通信网络的有希望的候选者。它可以应用于认知无线电网络（CRN），以进一步提高 SE 和用户连接性。然而，频谱共享造成的干扰和非正交资源的利用会降低可实现的性能。为了解决这个问题，智能反射面（IRS）被用于具有 NOMA 的下行多输入单输出（MISO）CRN。为了实现 SE 和 EE 之间的理想权衡，在完美和不完美的信道状态信息 (CSI) 下制定了多目标优化 (MOO) 框架。在完美的 CSI 情况下，利用基于迭代块坐标下降 (BCD) 的算法来迭代优化波束成形设计和 IRS 反射系数。一个安全的近似值和 $ \数学{S}$-procedure 用于解决不完美 CSI 情况下问题的非凸无限不等式约束。仿真结果表明，与基线方案相比，所提出的方案可以在 SE 和 EE 之间取得更好的平衡。此外，结果表明，在不完美的 CSI 下，所提出算法的 SE 和 EE 都可以通过利用 IRS 得到显着改善。