  Synergistic design of communications and radar systems with common spectral
and hardware resources is heralding a new era of efficiently utilizing a
limited radio-frequency spectrum. Such a joint radar-communications (JRC) model
has advantages of low-cost, compact size, less power consumption, spectrum
sharing, improved performance, and safety due to enhanced information sharing.
Today, millimeter-wave (mm-wave) communications have emerged as the preferred
technology for short distance wireless links because they provide transmission
bandwidth that is several gigahertz wide. This band is also promising for
short-range radar applications, which benefit from the high-range resolution
arising from large transmit signal bandwidths. Signal processing techniques are
critical in implementation of mmWave JRC systems. Major challenges are joint
waveform design and performance criteria that would optimally trade-off between
communications and radar functionalities. Novel multiple-input-multiple-output
(MIMO) signal processing techniques are required because mmWave JRC systems
employ large antenna arrays. There are opportunities to exploit recent advances
in cognition, compressed sensing, and machine learning to reduce required
resources and dynamically allocate them with low overheads. This article
provides a signal processing perspective of mmWave JRC systems with an emphasis
on waveform design.
