  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
multiple-output (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.
