Joint Transmit Beamforming Design for Secure Communication and Radar Coexistence Systems

In this paper, we propose a dynamic spectrum allocation approach for the coexistence of a radar system with a communications system whose operating frequency ranges overlap. We develop a combined mutual information criterion for the joint waveform and power spectrum design to optimize the performance of the radar and communications systems. The performance is evaluated in terms of the minimum separation distance between the coexisting systems under the constraint that a predefined operational SINR must be achieved for both systems. Significant performance gains close to 3.5 dB for the communication system and 1 dB for the radar system are observed in terms of the minimum separation distance compared to the scenario when the waveform with equal power allocation across the frequency bandwidth is used.

Coexisting radar and communication (CRC) systems on the same spectrum band have been recently recognized as a promising approach to solve the spectrum scarcity problem. Additionally, with the new requirements of achieving higher energy efficiency and restoring the consumed power, an initiative has been exploited to embed both information and energy within the same signal. Motivated by that, this paper presents a unified model to implement the concept of simultaneous wireless information and power transfer (SWIPT) in the coexistence environment between radar and communication systems. Specifically, we propose a novel energy harvesting based CRC model (CRCEH), in which the signals transmitted from both the communication and radar transmitters are precoded towards their conventional targets, communication receiver and radar targets respectively, as well as to an energy harvesting receiver. This system is capable of solving the spectrum scarcity problem as well as supporting low power devices with their energy requirements from the energy harvested at the energy harvesting receiver. The design problem is formulated as maximizing the radar detection probability subject to communication capacity and energy harvesting constraints. However, directly processing the data from the radar receiver requires high computational complexity and hardware costs. In order to circumvent this issue, this paper also proposes to deploy lens antenna array (LNA) to CRCEH system to sharply direct the signals to the energy harvesting receiver together with their respective destinations. Since LNA is able to focus the signal power on a subset of antennas, we may only deal with the data from the selective antenna subset associated with the maximal power. Numerical results show that the proposed method is able to achieve improved performance in terms of radar detection under communication capacity constraints compared to the conventional approaches besides the great benefit of harvesting the energy to feed some limited energy devices.

This letter investigates the spectrum sharing between a multiple-input single-output (MISO) secure communication system and a multiple-input multiple-output (MIMO) radar system in the presence of one suspicious eavesdropper. We harness the detrimental radar interference, which traditionally impairs communication systems, as a strategic mechanism to jam the eavesdropper, thereby substantially enhancing the performance of secure communication. In particular, by considering the imperfect channel state information (CSI) for the user and eavesdropper, we present a robust joint design for the radar waveform and communication beamforming vector at the two systems, with the objective of maximizing the worst-case secrecy rate at the user, while ensuring the detection performance of the radar system. To tackle this challenging problem, we propose a two-layer robust cooperative algorithm based on the Charnes-Cooper transformation and the Cauchy-Schwarz inequality. Simulation results reveal the trade-off between secrecy rate and detection probability and highlight the pivotal role of such robust designs in facilitating secure coexistence systems.

What is new in a radar system, what advancement is going on, and what may be the road map for future radar system development, this is the question for which authors tried to find out the solution throughout this review. Radar transformation is described in two parts. Like every communication system, analog radar baseband is transformed into digital baseband which is adding the advantage of digital signal processing (DSP) of the transmitted and received signal which helps to improve target parameter characterization. Software-defined radio (SDR) is one of the powerful tools which are heavily used to develop digital radar baseband in recent days. In the second part, single antenna-based old radar system is transformed into multi-antenna-based modern radar which helping to improve signal-to-noise ratio (SNR) in the radar receiver. This front end antenna part is still more or less analog, but several multi-antennas techniques are adding different advantages to the total system. A good amount of signal reception using multi-input multi-output (MIMO) system is helping in target characterization in the digital baseband section in which advance signal processing is working. These days few other multi-antenna techniques like Array antenna, Phased MIMO antenna are becoming popular for improving SNR of the system. In this article all multi-antenna techniques are reviewed to find out the best one in favor of Radar.

Spectrum sharing between radar and communication systems has attracted substantial recent research attention. In order to avoid the mutual interference, source separation of the aliasing signal is a promising solution for joint communication and radar (JCR) sensing. However, most of the existing source separation methods are very sensitive to wide-band signals with rapidly varying instantaneous amplitude (IA) and not applicable to scenarios with complex overlapping subsignals. To overcome the above limitations, a data-driven sparsity-based source separation method is proposed. Without depending on signal parameters, a sparse signal observation model of the received signal is established. Then, source separation is transformed into estimation of the IA and instantaneous frequency (IF), and an iterative method is developed. Moreover, the Cramér-Rao lower bound (CRLB) of the source separation error (SSE) variance is derived based on the CRLB of IA and IF. Simulation results demonstrate that our proposed method is superior to the benchmark schemes and is robust to noise and initial IF errors.

Joint radar and communication (JRC) technology is gradually becoming an essential approach to alleviating spectral congestion. Radar and communications systems were designed with common spectral and hardware resources to reduce size, improve performance, reduce cost, and decongest the spectrum. Various approaches have been proposed to achieve the coexistence of radar and communication systems. This paper mainly focuses on the research directions of radar communication coexistence (RCC) and dual-function radar communication systems (DFRC) in JRC technology. We summarize and analyze the existing research problems in the JRC era. According to the characteristics and advantages of JRC technology, we highlight several potentials in military and commercial applications.

The integrated radar and communication system has become a research hotspot in recent years due to its advantages of high utilization of hardware equipment and spectrum. For the application requirement of the integrated radar and communication system, a power allocation algorithm based on spectrum sharing is proposed. Firstly, we establish a signal model of integrated radar and communication system. Then, the evaluation index of radar performance based on mutual information between the random target impulse response and the received radar signal is constructed, and an optimization model with the goal of maximizing mutual information and channel capacity is constructed. Finally, combined with the KKT condition derivation, it utilizes the block coordinate descent method to find optimal solution iteratively. Simulation results show that under the given constraints, the power allocation algorithm proposed in this paper can effectively improve system performance.

This paper investigates the problem of low probability of intercept-based optimal power allocation (LPI-OPA) for an integrated multistatic radar and communication system, which consists of multiple transmitters operating at different frequencies, a radar receiver, and a communication receiver (CR). The integrated multistatic radar and communication system is capable of fulfilling the requirements of both radar and communication subsystems. The key tenet of the integrated system is to minimize the total power consumption by optimizing the transmit power allocation at each transmitter for radar waveforms and information signals, which is constrained by a predetermined target detection performance for the RR and a desired information rate for the CR. Since the analytical closed-form expression of the probability of detection is not tractable, its upper bound is derived. We analytically show that the resulting optimization problem can be reformulated as two subproblems, which can be solved by an efficient solution procedure based on the approach of linear programming and the Karush–Kuhn–Tuckers optimality conditions. Simulation results are provided to show that the LPI performance of the integrated multistatic radar and communication system can significantly be enhanced by employing our proposed LPI-OPA scheme.

This paper focuses on the problem of reconfigurable intelligent surface (RIS) aided co-design for multiple-input-multiple-output (MIMO) radar and MIMO communication systems with shared spectrum resources. The purpose is to maximize the radar output signal-to-interference-plus-noise-ratio (SINR) while ensuring the communication functionalities, where there is no line-of-sight (NLOS) propagation between the radar and the target. To achieve the goal, we develop a cyclic framework based on semi-definite programming (SDP), semi-definite relaxation (SDR), and alternating direction method of multiplier (ADMM) to jointly optimize the radar transmit waveforms, the receive filters, the communication codebook, and the RIS coefficients. Numerical experiments are conducted to evaluate the effectiveness of the proposed algorithm.

This paper studies the problem of Stackelberg game based distributed power allocation for spectral coexisting multistatic radar and communication systems. The strategy aims to minimize the radiated power of each radar by optimizing transmit power allocation for a desired signal-to-interference-plus-noise ratio (SINR) meanwhile the communication base station (CBS) is protected from the interference of radar transmissions. We formulate this distributed power allocation process as a Stackelberg game, where the CBS is a leader and the radars are the followers. The Nash equilibrium (NE) for the formulated game is derived. Then, the existence of the NE and uniqueness of the solution are analytically proved. Moreover, an iterative algorithm is developed to solve the resulting problem. Finally, numerical results verify that the proposed scheme is effective on power allocation and CBS protection with reduced signaling overhead.

Joint radar communication systems have been recently proposed for radar and communication sharing spectrum and hardware, to reduce the resource cost of platforms. In this paper, we consider the waveform design problem for a joint multiuser MIMO communication and MIMO radar system. We propose an optimization problem for waveform design, in which the signal-to-interference-plus-noise ratio (SINR) at users and the transmit beam pattern of radar are jointly optimized. To guarantee the efficiency of power amplifiers, the transmit waveform is constrained to be constant modulus. To deal with the non-convex constant modulus constraint, we propose an alternating descend method to solve the problem. The simulation results indicate that under high transmit signal-to-noise ratio (SNR), the communication quality can be guaranteed, along with a reasonable performance loss of radar. Moreover, the performance gap between the constant modulus constraint and the more relaxed per-antenna power constraint, is not significant.

Co-existence of radar and communication systems based on cooperative spectrum sharing is of significant interest in recent years. This letter proposes two joint power allocation schemes for radar and communication spectrum sharing, where both systems use the same bandwidth with cross interference between them. The proposed joint design maximizes a performance metric of one system while meeting a service constraint requirement for the other, i.e., throughput for the communication system or signal-to-interference-plus-noise ratio (SINR) for the radar. For both designs, we show that either the radar or the communication system needs to transmit at peak power to achieve the optimum solution, depending on the relation among the peak power constraint, channel conditions, and service constraints.

In this paper, we investigate the problem of robust beamforming for multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system, when there exist radar look direction mismatch and communication channel state information (CSI) error. To address these system imperfections, a region-of-interest (ROI) rather than a specific angle is considered for radar sensing, and the communication CSI is assumed to be available with a bounded unknown error. With these considerations, we design a robust dual-function beamformer through optimizing the radar sensing performance in terms of the energy radiated towards the ROI, while guaranteeing the communication quality-of-service (QoS) measured by the per-user signal-to-interference-plus-noise ratio (SINR) as required. For this robust design, a max-min optimization problem is formulated and a semidefinite programming (SDP) reformulation based on the S-procedure is presented. The popular semidefinite relaxation (SDR) technique is employed to solve the resulting non-convex problem, and the tightness of SDR is discussed. Further, we propose to incorporate an extra constraint of main beam control into the problem to achieve a flat magnitude response of the beampattern over the ROI. In addition, it is shown that the proposed approach is straightforwardly applicable to the scenario of multiple targets. Numerical examples are carried out to show the tightness, and demonstrate the effectiveness and robustness of the proposed MIMO-DFRC beamforming approach.

Antenna arrays with space-time adaptive processing (STAP) are commonly utilized to allow the reception of signals in harsh interference environments. The primary applications of STAP have been radar and communications systems, where the STAP weights are optimized for output signal-to-interference-plus-noise ratio (SINR); however, there is increasing demand for STAP in time-of-arrival (TOA) estimation applications, such as global navigation satellite system (GNSS) receivers. It is understood that TOA estimation performance is primarily dependent on receiver postcorrelation carrier-to-noise ratio (C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ). In this study, the distinction between SINR and C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is established, and a performance analysis of the common STAP algorithms is performed in the presence of interference. It is demonstrated that output SINR is an inadequate indication of GNSS receiver C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> performance. As a result, caution should be used when measuring the relative performance of different STAP algorithms in GNSS applications. Consequently, the present work develops a novel STAP algorithm which maximizes C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . Additionally, the performance of common STAP algorithms is analyzed in the context of this performance bound, and it is demonstrated that, in many cases, their performance is near optimal.

Electromagnetic spectrum congestion is a serious problem. The majority of the desirable spectrum lie below 6 GHz, and is consumed by communication and RADAR systems. DARPA's Shared Spectrum Access for Radar and Communications (SSPARC) program seeks to improve efficient use of spectrum by allowing RADAR and communication systems to share spectrum in the 3.5 GHz S-band. In this paper, we present some results on the MAC layer performance of the IEEE 802.11 protocol in the presence of RADAR interference. We propose a simple model for the carrier sense multiple access mechanism and use it to gain insight into some important performance metrics such as MAC layer throughput, and packet service time. Finally, we provide some numerical examples to demonstrate the usefulness of the results in designing spectrum sharing strategies in real systems.