# Toward Millimeter Wave Joint Radar-Communications: A Signal Processing Perspective

## TL;DR

This paper explores the signal processing challenges and innovations in millimeter-wave joint radar-communications systems, emphasizing waveform design and the potential of advanced techniques like MIMO, cognition, and machine learning.

## Contribution

It provides a comprehensive overview of signal processing techniques for mmWave JRC systems, highlighting novel waveform design and the integration of recent advances such as MIMO, cognition, and machine learning.

## Key findings

- Identifies key challenges in joint waveform design for mmWave JRC.
- Discusses the potential of MIMO, cognition, and machine learning to enhance system performance.
- Highlights opportunities for resource reduction and dynamic allocation in mmWave JRC systems.

## Abstract

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.