This paper investigates a machine learning-based power allocation design for secure transmission in a cognitive radio (CR) network. In particular, a neural network (NN)-based approach is proposed to maximize the secrecy rate of the secondary receiver under the constraints of total transmit power of secondary transmitter, and the interference leakage to the primary receiver, within which three different regularization schemes are developed. The key advantage of the proposed algorithm over conventional approaches is the capability to solve the power allocation problem with both perfect and imperfect channel state information. In a conventional setting, two completely different optimization frameworks have to be designed, namely the robust and non-robust designs. Furthermore, conventional algorithms are often based on iterative techniques, and hence, they require a considerable number of iterations, rendering them less suitable in future wireless networks where there are very stringent delay constraints. To meet the unprecedented requirements of future ultra-reliable low-latency networks, we propose an NN-based approach that can determine the power allocation in a CR network with significantly reduced computational time and complexity. As this trained NN only requires a small number of linear operations to yield the required power allocations, the approach can also be extended to different delay sensitive applications and services in future wireless networks. When evaluate the proposed method versus conventional approaches, using a suitable test set, the proposed approach can achieve more than 94% of the secrecy rate performance with less than 1% computation time and more than 93% satisfaction of interference leakage constraints. These results are obtained with significant reduction in computational time, which we believe that it is suitable for future real-time wireless applications.

中文翻译：

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利用深度学习在底层认知无线电网络中实现安全传输


本文研究了一种基于机器学习的功率分配设计，用于认知无线电（CR）网络中的安全传输。特别地，提出了一种基于神经网络（NN）的方法，以在辅助发射机的总发射功率和对主接收机的干扰泄漏的约束下最大化辅助接收机的保密率，其中三种不同的正则化方案是发达。与传统方法相比，所提出的算法的主要优点是能够利用完美和不完美的信道状态信息来解决功率分配问题。在传统设置中，必须设计两种完全不同的优化框架，即鲁棒设计和非鲁棒设计。此外，传统算法通常基于迭代技术，因此需要大量迭代，使得它们不太适合延迟约束非常严格的未来无线网络。为了满足未来超可靠低延迟网络前所未有的要求，我们提出了一种基于神经网络的方法，可以确定 CR 网络中的功率分配，同时显着减少计算时间和复杂性。由于这种经过训练的神经网络只需要少量的线性运算即可产生所需的功率分配，因此该方法还可以扩展到未来无线网络中不同的延迟敏感应用和服务。当评估所提出的方法与传统方法相比时，使用合适的测试集，所提出的方法可以以少于 1% 的计算时间实现超过 94% 的保密率性能，并且满足超过 93% 的干扰泄漏约束。 这些结果是在计算时间显着减少的情况下获得的，我们相信它适合未来的实时无线应用。