  Since the secrecy rate (SR) performance improvement obtained by secure
directional modulation (DM) network is limited, an active intelligent
reflective surface (IRS)-assisted DM network is considered to attain a high SR.
To address the SR maximization problem, a novel method based on Lagrangian dual
transform and closed-form fractional programming algorithm (LDT-CFFP) is
proposed, where the solutions to base station (BS) beamforming vectors and IRS
reflection coefficient matrix are achieved. However, the computational
complexity of LDT-CFFP method is high . To reduce its complexity, a blocked
IRS-assisted DM network is designed. To meet the requirements of the network
performance, a power allocation (PA) strategy is proposed and adopted in the
system. Specifically, the system power between BS and IRS, as well as the
transmission power for confidential messages (CM) and artificial noise (AN)
from the BS, are allocated separately. Then we put forward null-space
projection (NSP) method, maximum-ratio-reflecting (MRR) algorithm and PA
strategy (NSP-MRR-PA) to solve the SR maximization problem. The CF solutions to
BS beamforming vectors and IRS reflection coefficient matrix are respectively
attained via NSP and MRR algorithms. For the PA factors, we take advantage of
exhaustive search (ES) algorithm, particle swarm optimization (PSO) and
simulated annealing (SA) algorithm to search for the solutions. From simulation
results, it is verified that the LDT-CFFP method derives a higher SR gain over
NSP-MRR-PA method. For NSP-MRR-PA method, the number of IRS units in each block
possesses a significant SR performance. In addition, the application PA
strategies, namely ES, PSO, SA methods outperforms the other PA strategies with
fixed PA factors.
