Directional Modulation: A Secure Solution to 5G and Beyond Mobile Networks

Directional modulation (DM), as an efficient secure transmission way, offers security through its directive property and is suitable for line-of-propagation (LoP) channels such as millimeter wave (mmWave) massive multiple-input multipleoutput (MIMO), satellite communication, unmanned aerial vehicle (UAV), and smart transportation. If the direction angle of the desired received is known, the desired channel gain vector is obtainable. Thus, in advance, the DM transmitter knows the values of directional angles of desired user and eavesdropper, or their direction of arrival (DOAs) because the beamforming vector of confidential messages and artificial noise (AN) projection matrix is mainly determined by directional angles of desired user and eavesdropper. For a DM transceiver, working as a receiver, the first step is to measure the DOAs of desired user and eavesdropper. Then, in the second step, using the measured DOAs, the beamforming vector of confidential messages and AN projection matrix is designed. In this paper, we describe the DOA measurement methods, power allocation, and beamforming in DM networks. A machine learning-based DOA measurement method is proposed to make a substantial SR performance gain compared to single-snapshot measurement without machine learning for a given null-space projection beamforming scheme. However, for a conventional DM network, there still exists a serious secure issue: the eavesdropper moves inside the main beam of the desired user and may intercept the confidential messages intended to the desired users because the beamforming vector of confidential messages and AN projection matrix are only angle-dependence. To address this problem, we present a new concept of secure and precise transmission, where the transmit waveform has two-dimensional even three-dimensional dependence by using DM, random frequency selection, and phase alignment at DM transmitter. I. DIRECTIONAL MODULATION: CONCEPT AND SYSTEM Due to the broadcast nature of wireless transmission, confidential information is easily intercepted by malicious users, which will expose wireless networks to serious secure risk. Therefore, secure transmission, storage, processing, and protection of confidential information become extremely important research topics in wireless networks in recent years. Additionally, there is an increasing demand to design secure transmission methods to protect the legitimate users from being overheard. Recently, as millimeter wave (mmWave) This work was supported in part by the National Natural Science Foundation of China (Nos. 61771244, 61501238, 61702258, 61472190, and 61271230), in part by the Open Research Fund of National Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation (No. 201500013), in part by the Jiangsu Provincial Science Foundation under Project BK20150786, in part by the Specially Appointed Professor Program in Jiangsu Province, 2015, in part by the Fundamental Research Funds for the Central Universities under Grant 30916011205, and in part by the open research fund of National Mobile Communications Research Laboratory, Southeast University, China (Nos. 2017D04 and 2013D02). massive multiple-input multiple-output (MIMO), unmanned aerial vehicle (UAV), and internet of things (IoT) emerge, how to securely transmit confidential messages in line-ofpropagation (LoP) channel is becoming a hot research area. Directional modulation (DM), as an inherent secure method in LoP channel, attract more and more research activities from both academia and industry communities. Fig. 1. Schematic diagram of directional modulation network. In Fig. 1, a concept diagram is presented to show the basic idea of DM. In this figure, there are one DM transmitter with multiple transmit antennas, two eavesdroppers, and one desired user. The DM transmitter sends its confidential messages to the desired user such that the two eavesdroppers cannot intercept the confidential messages. By exploiting the directive property generated by antenna-array-based beamforming with the help of artificial noise (AN) projection, the DM transmitter transmits the confidential message to the desired direction and interferes with the eavesdroppers by AN projection. Due to the joint operation of AN projection and beamforming, the confidential messages can be securely and successfully transmitted to the desired user, and AN severely corrupts the eavesdroppers. We propose a smart DM transceiver shown in Fig. 2. Here, one of machine learning methods, Bayesian