Wireless spectrum is a limited resource, and the rapid increase in demand for wireless communication-based services is increasing day by day. Hence, maintaining a good quality of service, high data rate, and reliability is the need of the day. Thus, we need to apportion the available spectrum in an efficient manner. Dual-Function Radar and Communication (DFRC) is an emerging field and bears vital importance for both civil and military applications for the last few years. Since hybridization of wireless communication and radar designs provoke diverse challenges, e.g., interference mitigation, secure mobile communication, improved bit error rate (BER), and data rate enhancement without compromising the radar performance, this paper reviews the state-of-the-art developments in the spectrum shared between mobile communication and radars in terms of coexistence, collaboration, cognition, and cooperation. Compared to the existing surveys, we explore an open research issue on radar and mobile communication operating with mutual benefits based on collaboration in terms of spectrum sharing. Additionally, this paper provides important perspectives for future research of DFRC technology.

In this paper, we develop a novel approach for a joint platform of radar and communication systems. Previous approaches for dual-function radar-communication systems focused only on the simultaneous transmission of radar and communications in radar active mode. To increase the throughput of communications without affecting the radar operation, we enable communication during the whole pulse repetition interval by using two generalized sidelobe cancellers. We propose two different operational modes of the dual-function radar-communications system. In active mode, the radar function is achieved through the mainlobe, and communications are achieved through the sidelobes. In active mode, only one generalized sidelobe canceller is functional. In rest mode, both the generalized sidelobe cancellers are functional. The first generalized sidelobe canceller has the mainlobe and sidelobe levels as in active mode, and the second generalized sidelobe canceller has the same mainlobe level but double the power in the sidelobes. The output is the difference of the two, in which the mainlobe is cancelled, while the sidelobes have the same power, as in the case of active mode. The effectiveness of the proposed scheme is investigated in terms of the bit error rate. Moreover, the proposed system allows communication during the whole pulse repetition interval, thus enhancing the throughput tremendously.

The design of the modern computing paradigm of heuristics is an innovative development for parameter estimation of direction of arrival (DOA) using sparse antenna arrays. In this study, the optimization strength of the flower pollination algorithm (FPA) is exploited for the DOA estimation in a low signal to noise ratio (SNR) regime by applying coprime sensor arrays (CSA). The enhanced degree of freedom (DOF) is achieved with FPA by investigating the global minima of highly nonlinear cost function with multiple local minimas. The sparse structure of CSA demonstrates that the DOF up to $O\left(MN\right)$ is achieved by employing $M+N$ CSA elements, where M and N are the numbers of antenna elements used to construct the CSA. Performance analysis is conducted for estimation accuracy, robustness against noise, robustness against snapshots, frequency distribution of root mean square error (RMSE), variability analysis of RMSE, cumulative distribution function (CDF) of RMSE over Monte Carlo runs and the comparative studies of particle swarm optimization (PSO). These reveal the worth of the proposed methodology for estimating DOA.

For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).