Spatial Modulation: an Attractive Secure Solution to Future Wireless Network

TL;DR

This paper reviews secure spatial modulation in wireless networks, addressing key challenges and proposing low-complexity detection and deep learning-based power allocation strategies to enhance security and efficiency.

Contribution

It introduces a low-complexity ML detector and a DNN-based power allocation method to improve security and reduce computational complexity in spatial modulation systems.

Findings

Low-complexity ML detector matches joint ML performance in bit error rate.

DNN-based power allocation balances complexity and secrecy rate.

Proposed methods enhance security and efficiency in spatial modulation.

Abstract

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.