  As a green and secure wireless transmission method, secure spatial modulation
(SM) is becoming a hot research area. Its basic idea is to exploit both the
index of activated transmit antenna and amplitude phase modulation signal to
carry messages, improve security, and save energy. In this paper, we review its
crucial challenges: transmit antenna selection (TAS), artificial noise (AN)
projection, power allocation (PA) and joint detection at the desired receiver.
As the size of signal constellation tends to medium-scale or large-scale, the
complexity of traditional maximum likelihood detector becomes prohibitive. To
reduce this complexity, a low-complexity maximum likelihood (ML) detector is
proposed. To further enhance the secrecy rate (SR) performance, a
deep-neural-network (DNN) PA strategy is proposed. Simulation results show that
the proposed low-complexity ML detector, with a lower-complexity, has the same
bit error rate performance as the joint ML method while the proposed DNN method
strikes a good balance between complexity and SR performance.
