In this article, we propose a physical layer security (PLS) technique, namely security-aware spatial modulation (SA-SM), in a multiple-input multiple-output-based heterogeneous network, wherein both optical wireless communications and radio-frequency (RF) technologies coexist. In SA-SM, the time-domain signal is altered prior to transmission using a key at the physical layer for combating eavesdropping. Unlike conventional PLS techniques, SA-SM does not rely on channel characteristics for securing the information, as its perception is self-imposed, which allows its adoption in radio-optical networks. Additionally, a novel periodical key selection algorithm is proposed. Instead of having multiple keys stored in the nodes, by using off-the-shelf and low-complexity machine learning (ML) methods, including a support vector machine, logistic regression and a single-layer neural network, SA-SM nodes can estimate the used key. Results show that a positive secrecy capacity can be achieved for both the RF and optical links by using 1000 different keys, with a minimal signal-to-noise ratio penalty of less than 5 dB for the legitimate user using SA-SM versus conventional transmission at a bit-error-rate of 10⁻⁴. The analysis also includes computational time and classification accuracy evaluation of the various proposed ML techniques using different hardware architectures.

在本文中，我们提出了一种基于多输入多输出的异构网络中的物理层安全（PLS）技术，即安全感知空间调制（SA-SM），其中光无线通信和射频（ RF）技术并存。在SA-SM中，在传输之前使用物理层上的密钥来更改时域信号，以防止窃听。与传统的PLS技术不同，由于SA-SM的感知是自我强加的，因此它不依赖于信道特性来保护信息，这使其可以在无线电网络中采用。另外，提出了一种新颖的周期性密钥选择算法。通过使用现成的，低复杂度的机器学习（ML）方法（包括支持向量机）来代替在节点中存储多个密钥，通过逻辑回归和单层神经网络，SA-SM节点可以估计使用的密钥。结果表明，通过使用1000个不同的密钥，可以为RF和光链路实现正的保密容量，对于使用SA-SM的合法用户，与传统的传输方式相比，使用SA-SM的最小信噪比损失小于5 dB。误码率为10⁻⁴。分析还包括使用不同的硬件体系结构对各种提议的ML技术的计算时间和分类准确性评估。