  Traditional physical layer secure beamforming is achieved via precoding
before signal transmission using channel state information (CSI). However,
imperfect CSI will compromise the performance with imperfect beamforming and
potential information leakage. In addition, multiple RF chains and antennas are
needed to support the narrow beam generation, which complicates hardware
implementation and is not suitable for resource-constrained Internet-of-Things
(IoT) devices. Moreover, with the advancement of hardware and artificial
intelligence (AI), low-cost and intelligent eavesdropping to wireless
communications is becoming increasingly detrimental. In this paper, we propose
a multi-carrier based multi-band waveform-defined security (WDS) framework,
independent from CSI and RF chains, to defend against AI eavesdropping.
Ideally, the continuous variations of sub-band structures lead to an infinite
number of spectral features, which can potentially prevent brute-force
eavesdropping. Sub-band spectral pattern information is efficiently constructed
at legitimate users via a proposed chaotic sequence generator. A novel security
metric, termed signal classification accuracy (SCA), is used to evaluate the
security robustness under AI eavesdropping. Communication error probability and
complexity are also investigated to show the reliability and practical
capability of the proposed framework. Finally, compared to traditional secure
beamforming techniques, the proposed multi-band WDS framework reduces power
consumption by up to six times.
