Waveform Design and Processing for Joint Wireless Communications and Sensing

Since the advent of radar/sensing systems, they have always had fixed frequencies for operation. Due to the exponential growth of communications systems, the need for dedicated spectrum for them also increased, causing spectrum scarcity for both communications and sensing. It was obvious that some form of flexible spectrum sharing was necessary between these two functionalities. Soon enough, this led the researchers to focus on joint communications and sensing (JCAS) systems that share spectral resources efficiently. The hardware convergence due to the similar functioning of the two systems complemented the frequency convergence of JCAS systems. In fact, JCAS is one of the prominent requirements in future sixth-generation (6G) communications systems.

This thesis focuses on integrating the sensing functionality on top of wireless mobile communications systems, such as in fifth-generation (5G). To facilitate effective JCAS, the thesis provides signal processing techniques for designing waveforms that optimally share the spectral resources, for single-input single-output (SISO) as well as multiple-input multiple-output (MIMO) systems. In addition, novel radar processing techniques are investigated for MIMO systems to better detect the targets in the environment.

The standard waveform in 5G, that is, orthogonal frequency-division multiplexing (OFDM), is also considered for joint waveform design. In such a communications system, the resources are usually not fully utilized and there exist unused subcarriers within the OFDM waveform. These subcarriers are filled with optimized samples to minimize the lower bounds of delay and velocity estimates’ error variances of sensing, for SISO JCAS systems. The simulations with standard-compliant 5G waveforms illustrate the improvements possible in sensing, while also helping to maximize the efficiency in the transmit power amplification process, along the same optimization scheme. The simulation results are complemented through practical radio-frequency measurements of an outdoor environment depicting the significant gains that can be obtained in the range–angle map of sensing, due to the waveform optimization.

For MIMO JCAS systems, apart from conventional communications streams, separate transmit (TX) streams are used to improve sensing performance through two separate schemes. One scheme involves optimizing the sensing streams to minimize the lower bounds of delay and angle estimates’ error variances of sensing. Simulation results indicate that the errors of sensing can be minimized while striking a good balance with the communications capacity. The other scheme depicts that the target detection can be enhanced using sensing streams on top of a communications stream. Specifically, the number of false targets detected can be significantly reduced in comparison to single-stream communication.

The antenna arrays in MIMO communications systems nowadays are a combination of analog and digital architectures, i.e., hybrid, instead of consisting of a fullydigital architecture, for reduced costs and power consumption. Radar processing in such a hybrid architecture with multiple TX streams is not straightforward in comparison to the conventional fully-digital MIMO radar. Hence, this thesis also provides novel radar processing techniques to obtain the range–angle and range–velocity maps of the sensed environment. The simulation results illustrate that the targets can be reliably detected through the proposed MIMO processing, while also providing super-resolution in the angular domain.