Dual-functional Radar-communication Research Articles

Phase response constrained symbol‐level waveform design for dual‐functional radar‐communication systems

AbstractIn this letter, a novel algorithm is proposed to design symbol‐level waveform for dual‐functional radar‐communication (DFRC) systems with low range sidelobe. Different from current schemes design waveform at the block level, a phase response constraint is introduced at the symbol level to provide an additional degree of freedom to decrease the range sidelobe in the pulse compression procedure, which is highly desired in radar systems. In particular, the phase response at the target direction is constrained to be similar to the given reference phase, and the matching error between the designed beampattern and the desired one is minimized subject to the constant modulus constraint. Furthermore, to guarantee the quality of service for communication, constructive interference is exploited at the symbol level for each communication user. An alternating direction method of multipliers algorithm is also proposed to solve the resulting nonconvex optimization problem with tractable subproblems solved sufficiently by the manifold optimization and standard quadratic problem. Numerical simulation results demonstrate the performance of the proposed method.

Dual-functional radar-communication based on frequency modulated continuous wave exploiting constraint frequency hopping

This paper focuses on the design of a frequency modulated continuous wave (FMCW) dual-functional radar-communication (DFRC) system with low-loss radar detection performance, aiming at achieving a balance among communication rate, symbol error rate (SER), and out-of-band leakage. For what the communication functionality concerns, the constrained frequency hopping (C–FH) sequence mapping and up/down chirp are used to embed data, limiting the out-of-band leakage. We provide explicit rules for traversal-FH sequence to adjust the number of configurable frequency points within each chip, and limit the SER at the receiver. For the radar signal processing, after coherent processing and sampling, we proceed time-domain extension and range-bin phase compensation (TE-RBPC) according to the FH value, achieving the same range profile as an unmodulated (FMCW) without any influence from the FH sequences. The simulation results show that the proposed C–FH-chirp waveform reduces the out-of-band power leakage by 10 dB if compared with a phase shift keying scheme, and provides a 5 dB improvement in the signal noise ratio under the same SER if compared with an unconstrained FH-chirp scheme. Moreover, by comparing it with a scheme without compensation, the sidelobe amplitude of radar range profile in TE-RBPC scheme is reduced by about 39 dB.

A Dual Mode Multi-Beam and Integrated CPM-LFM Signal for Dual-Functional Radar Communication Systems

A Dual Mode Multi-Beam and Integrated CPM-LFM Signal for Dual-Functional Radar Communication Systems

Let It GUWO: Waveform Optimization for Angular Blanking and Robustification in a MIMO Dual-Functional Radar-Communication System

Let It GUWO: Waveform Optimization for Angular Blanking and Robustification in a MIMO Dual-Functional Radar-Communication System

Unimodular Waveform Design for the DFRC System with Constrained Communication QoS

In this study, we investigated two waveform design problems for a dual-functional radar communication (DFRC) system, taking into consideration different constrained communication quality-of-service (QoS) requirements. Our objective was to minimize the mean-square error (MSE) of radar beampattern matching as the cost function. To this end, the multi-user interference (MUI) energy constraint and constructive interference (CI) constraint were, respectively, formulated to ensure the communication QoS. It is important to note that we designed a strict per-user MUI energy constraint at each sampling moment to achieve more accurate control over communication performance. Additionally, we introduced a constant-modulus constraint to optimize the efficiency of the radio frequency (RF) amplifier. To tackle the nonconvex waveform design problems encountered, we employed the alternative direction methods of multipliers (ADMM) technique. This allowed us to decompose the original problem into two solvable subproblems, which were then solved using the majorization–minimization (MM) method and geometrical structure. Finally, we obtained extensive simulation results which demonstrate the effectiveness and superiority of the proposed algorithm.

Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks

With the ongoing changes in global climate, ocean data play a crucial role in understanding the complex variations in the earth system. These variations pose significant challenges to human efforts in addressing the changes. As a data hub for the satellite geodetic technique, unmanned aerial vehicles (UAVs) instill new vitality into ocean data collection due to their flexibility and mobility. At the same time, the dual-functional radar-communication (DFRC) system is considered a promising technology to empower ubiquitous communication and high-accuracy localization. In this paper, we explore a new fusion of UAV and DFRC to assist data acquisition in the ocean surveillance scenario. The floating buoys transmit uplink data transmission to the UAV with non-orthogonal multiple access (NOMA) and attempt to localize the target cooperatively. With the mobility of the UAV and power control at the buoys, the system throughput and the target localization performance can be improved simultaneously. To balance the communication and sensing performance, a two-objective optimization problem is formulated by jointly optimizing the UAV’s location and buoy’s transmit power to maximize the system throughput and minimize the attainable localization mean-square error. We propose a joint communication and radar-sensing many-objective optimization (CRMOP) algorithm to meliorate the communication and radar-sensing performance simultaneously. Simulation results demonstrate that compared with the baseline, the proposed algorithm achieves superior performance in balancing the system throughput and target localization.

On Outage-Based Beamforming Design for Dual-Functional Radar-Communication 6G Systems

This article studies and derives beamforming design in a dual-functional radar-communication (DFRC) multiple-input-multiple-output system. We focus on a scenario, where the DFRC base station communicates with downlink communication users, with imperfect channel state information knowledge, and performs target detection, all via the same transmit signal. Through careful relaxation procedures, we arrive at a suitable and novel optimization problem, which maximizes the radar output power in the Bartlett sense, under probabilistic outage signal-to-interference-and-noise ratio constraints. Theoretical analysis proves optimality of the solution given by the relaxed version of the problem, as well as closed-form solutions in certain scenarios. Finally, the achieved performances and trade-offs of the proposed beamforming design are demonstrated through numerical simulations.

Joint Design of Transmitting Waveform and Receiving Filter via Novel Riemannian Idea for DFRC System

Recently, the problem of target detection in noisy environments for the Dual-Functional Radar Communication (DFRC) integration system has been a hot topic. In this paper, to suppress the noise and further enhance the target detection performance, a novel manifold Riemannian Improved Armijo Search Conjugate Gradient algorithm (RIASCG) framework has been proposed which jointly optimizes the integrated transmitting waveform and receiving filter. Therein, the reference waveform is first designed to achieve excellent pattern matching of radar beamforming. Furthermore, to ensure the quality of system information transmission, the energy of multi-user interference (MUI) of communication signals is incorporated as the constraint. Additionally, the typical similarity constraint is introduced to ensure the transmitting waveform with a good ambiguity function. Finally, simulation results demonstrate that the designed waveform not only enhances the system’s target detection performance in noisy environments but also achieves a relatively good multi-user communication ability when compared with other prevalent waveforms.

Hybrid Beamforming in mmWave Massive MIMO for IoV With Dual-Functional Radar Communication

The dual-functional radar communication (DFRC) has been regarded as one of the most attractive solutions for facilitating vehicle applications in internet of vehicles (IoV) by utilizing millimeter Wave (mmWave) and massive multiple-in-multiple-out (MIMO) technologies. In this paper, we propose a hybrid beamforming (HBF) scheme with low power consumption and fully directional beamforming gains. The proposed HBF scheme integrates digital beamforming (DBF) with analog beamforming (ABF) to adopt a fully connected (F-C) structure to realize high spectral efficiency (SE) and accurate target sensing. Specifically, for HBF in a single-user MIMO (SU-MIMO) system, we design a fast Riemannian manifold optimization (FRMO) method by minimizing the weighted summation of the communication beamforming error and radar beamforming error. The proposed method can achieve optimal DBF for downlink communication and guarantee the desired beampattern for radar sensing. For HBF in a multiuser MIMO (MU-MIMO) system, we formulate the weighted optimization problem to mitigate multiuser interference (MUI). We design a low-complexity method, namely, the adaptive particle swarm optimization (APSO) algorithm, to obtain a near-optimal solution for HBF based on the criteria of minimizing MUI and the radar beamforming error. We conduct excessive simulations to evaluate the proposed algorithms for the HBF design in both the SU-MIMO system and MU-MIMO system. The numerical results demonstrate that the proposed algorithms achieve near-optimal performance compared to existing HFB methods with high computational efficiency in terms of beamforming gains and SE.

Robust Beamforming Design for RIS-Aided Integrated Sensing and Communication System

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

Waveform Design for MIMO Dual-Functional Radar-Communication System Using MUI Energy Minimization With PAPR and CRB Constraints

In this letter, we design a multiple-input multiple-output (MIMO) dual-functional radar-communication (DFRC) waveform which can simultaneously detect multiple MIMO radar targets and serve downlink users. A nonconvex optimization problem is established to minimize the downlink multi-user interference (MUI) energy under the constraints of total power, peak average power ratio (PAPR) and Cramér-Rao bound (CRB) of direction-of-arrival (DOA) estimation. We propose a multivariable-iteration-based alternating direction method of multipliers (MI-ADMM) algorithm to solve such a nonconvex problem by transforming it into several convex subproblems. Thus, we only need to solve each convex subproblem alternately during each iteration. Additionally, we analyze the complexity of the proposed algorithm. Numerical simulations show the impacts of PAPR and CRB constraints on the performance of the DFRC system. The results demonstrate that the DFRC waveform can achieve a great trade-off between the performance of MIMO radar and communication.

Multi-User and Multi-Target Dual-Function Radar-Communication Waveform Design: Multi-Fold Performance Tradeoffs

The dual-functional radar-communication (DFRC) has played an essential role in the effective utilization of both hardware and spectrum resources. It enables the integrated sensing and communication (ISAC) towards 5G-Advanced or 6G. As an effective way to realize DFRC, in this paper, we design and optimize the transceiver waveform for a multi-input-multi-output (MIMO) DFRC system, where the dual functional base station (BS) simultaneously detects the multiple targets while communicating with multiple communication users. To this end, we first characterize the multi-fold tradeoffs between communication and radar sensing by defining an achievable performance region of the proposed DFRC system. Then, we formulate the optimal DFRC waveform design as a fairness profile optimization (FPO) problem to achieve the Pareto boundary of performance region, which is solved by using successive convex approximation (SCA) method. Moreover, two low-complexity waveform designs based on semidefinite programming and matrix space projection methods are provided to satisfy the goal of realizing green DFRC network. Simulation results confirm the superiority of our proposed designs.

Joint Optimization of Radar and Communications Performance in 6G Cellular Systems

Dual functional radar communication (DFRC) is a promising approach that provides a viable solution for the problem of spectrum sharing between communication and radar applications. This paper studies a DFRC system with multiple communication users (CUs) and a radar target. The goal is to devise beamforming vectors at the DFRC transmitter in such a way that the radar received signal-to-clutter-plus-noise-ratio (SCNR) is maximized while satisfying the minimum data rate requirements of the individual CUs. With regard to clutter, we consider two scenarios based on the possibility of clutter removal. Even though the formulated optimization problems are non-convex, we present efficient algorithms to solve them using convex optimization techniques. Specifically, we use duality theory and Karush-Kuhn-Tucker conditions to show the underlying structure of optimal transmit precoders. In the proposed solution, it is observed that there is no need to transmit separate probing signal for the radar detection in both the considered scenarios. This results in reduction in the number of optimization variables in the problem. Moreover, we make use of the asymptotic equivalence between Toeplitz matrices and Circulant matrices to further reduce the complexity of the proposed algorithm. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithms.

Net-Zero Energy Dual-Functional Radar-Communication Systems

This paper proposes beamforming designs for net-zero energy multi-input multi-output (MIMO) dual-functional radar-communication (DFRC) systems that are powered through energy harvesting (EH) resources and aim to operate autonomously without access to the power grid. We propose a weighted optimization problem to jointly maximize the radar mutual information and minimum quality of service (QoS) requested by communication users subject to energy balancing constraints. The proposed problem is not convex, hence it is tough to solve. We exploit semidefinite relaxation (SDR) and first-order Taylor expansion techniques to relax its non-convexity issues. We then propose an iterative algorithm to obtain the beamforming matrices for the reference scenario when full channel state information (CSI) and energy arrival information (EAI) are available. For the single-target scenario, we show that the proposed optimization contains rank-one solutions. For the multiple targets scenario, by adding auxiliary optimization variables, we show that rank-one matrices can be achieved from the optimal solutions of the proposed optimization. We then propose a robust optimization for the case where only imperfect CSI and EAI are assumed to be known. Finally, numerical simulations show that the proposed DFRC designs are convergent and obtain a graceful trade-off between the radar and communication performances.

Joint Waveform and Clustering Design for Coordinated Multi-Point DFRC Systems

To improve both sensing and communication performances, this paper proposes a coordinated multi-point (CoMP) transmission design for a dual-functional radar-communication (DFRC) system. In the proposed CoMP-DFRC system, the central processor (CP) coordinates multiple base stations (BSs) to transmit both the communication signal and the dedicated probing signal. The communication performance and the sensing performance are both evaluated by the signal-to-interference-plus-noise ratio (SINR). Given the limited backhaul capacity, we study the waveform and clustering design from both the radar-centric perspective and the communication-centric perspective. Dinkelbach’s transform is adopted to handle the single-ratio fractional objective for the radar-centric problem. For the communication-centric problem, we adopt quadratic transform to convexitify the multi-ratio fractional objective. Then, the rank-one constraint of communication beamforming vector is relaxed by semidefinite relaxation (SDR), and the tightness of SDR is further proved to guarantee the optimal waveform design with fixed clustering. For dynamic clustering, equivalent continuous functions are used to represent the non-continuous clustering variables. Successive convex approximation (SCA) is further utilized to convexitify the equivalent functions. Simulation results are provided to verify the effectiveness of all proposed designs.

Full-Duplex SIC Design and Power Allocation for Dual-Functional Radar-Communication Systems

To meet the requirements for simultaneous sensing and communication, full-duplex operation is essential for dual-functional radar-communication (DFRC) systems. In this letter, both the full-duplex self-interference cancellation (SIC) and the corresponding power allocation problem is studied for a pulsed DFRC system. First, a full-duplex design is provided, and the radar signal to interference-plus-noise ratio (SINR) and the communication rate are derived considering imperfect full-duplex SIC. Then, a max-min-ratio problem is formulated to maximize the radar SINR under the constraint of the communication rate. For time-invariant channels, a closed-form power allocation solution is derived for both radar and communication pulses. For time-variant channels, we provide an iterative solution by adopting quadratic transform and successive convex approximation (SCA). Simulation results are further given to verify the effectiveness of the proposed designs.

QoS-Aware Precoder Optimization for Radar Sensing and Multiuser Communications Under Per-Antenna Power Constraints

In this work, we concentrate on designing the precoder for the multiple-input multiple-output (MIMO) dual functional radar-communication (DFRC) system, where the dual-functional waveform is designed for performing multiuser downlink transmission and radar sensing simultaneously. Specifically, considering the signal-independent interference and signal-dependent clutter, we investigate the optimization of transmit precoding for maximizing the sensing signal-to-interference-plus-noise ratio (SINR) at the radar receiver under the constraint of the minimum SINR received at multiple communication users and per-antenna power budget. The formulated problem is challenging to solve due to the nonconovex objective function and nonconvex per-antenna power constraint. In particular, for the signal-independent interference case, we propose a distance-majorization induced algorithm to approximate the nonconvex problem as a sequence of convex problems whose optima can be obtained in closed form. Subsequently, our complexity analysis shows that our proposed algorithm has a much lower computational complexity than the widely-adopted semidefinite relaxation (SDR)-based algorithm. For the signal-dependent clutter case, we employ the fractional programming to transform the nonconvex problem into a sequence of subproblems, and then we propose a distance-majorization based algorithm to obtain the solution of each subproblem in closed form. Finally, simulation results confirm the performance superiority of our proposed algorithms when compared with the SDR-based approach. In conclusion, the novelty of this work is to propose an efficient algorithm for handling the typical problem in designing the DFRC precoder, which achieves better performance with a much lower complexity than the state-of-the-art algorithm.

Robust and Secure Resource Allocation for ISAC Systems: A Novel Optimization Framework for Variable-Length Snapshots

In this paper, we investigate the robust resource allocation design for secure communication in an integrated sensing and communication (ISAC) system. A multi-antenna dual-functional radar-communication (DFRC) base station (BS) serves multiple single-antenna legitimate users and senses for targets simultaneously, where already identified targets are treated as potential single-antenna eavesdroppers. The DFRC BS scans a sector with a sequence of dedicated beams, and the ISAC system takes a snapshot of the environment during the transmission of each beam. Based on the sensing information, the DFRC BS can acquire the channel state information (CSI) of the potential eavesdroppers. Different from existing works that focused on the resource allocation design for a single snapshot, in this paper, we propose a novel optimization framework that jointly optimizes the communication and sensing resources over a sequence of snapshots with adjustable durations. Besides, artificial noise (AN) is exploited by the BS for joint sensing and physical layer security provisioning. To this end, we jointly optimize the duration of each snapshot, the beamforming vector, and the covariance matrix of the AN for maximization of the system sum secrecy rate over a sequence of snapshots while guaranteeing a minimum required average achievable rate and a maximum information leakage constraint for each legitimate user. The resource allocation algorithm design is formulated as a non-convex optimization problem, where we account for the imperfect CSI of both the legitimate users and the potential eavesdroppers. To make the problem tractable, we derive a bound for the uncertainty region of the potential eavesdroppers’ small-scale fading based on a safe approximation, which facilitates the development of a block coordinate descent-based iterative algorithm for obtaining an efficient suboptimal solution. Simulation results illustrate that the proposed scheme can significantly enhance the physical layer security of ISAC systems compared to three baseline schemes. Moreover, compared to the conventional multi-stage approach for ISAC system design, the proposed approach based on variable-length snapshots not only facilitates a highly-directional offline sensing beam design but also allows us to flexibly prioritize communication or sensing depending on the application scenario.

Interference optimized dual-functional radar-communication waveform design with low PAPR and range sidelobe

Dual-functional radar-communication system integrates the radar and communication functions into one system with a shared dual-functional waveform. In this paper, we focus on the problem of dual-functional waveform design under the peak-to-average power ratio (PAPR) constraint and low range sidelobe requirement. When the conventional schemes aim to minimize the multi-user interference (MUI) energy and maximize the similarity to the reference radar waveform, in our proposed method, a weighted summation objective function incorporated with the PAPR constraint and optimized MUI is constructed for the waveform design. Specifically, MUI is classified into destructive interference and constructive interference, while the latter is exploited as a free power for communication and is constrained to guarantee the communication quality-of-service (QoS). To tackle the resulting high order nonconvex problem, we develop an iteration algorithm based on the alternating direction method of multipliers (ADMM) procedure, which involves two quadratic programming subproblems in each iteration and is easy to solve with the current solver. Moreover, by taking the imperfect channel state information (CSI) into consideration, we further investigate the robust dual-functional waveform design problem with the bounded uncertainty of the CSI. Numerical simulations validate the effectiveness of the proposed method.

Joint Transmit Beamforming and Receive Filters Design for Coordinated Two-Cell Interfering Dual-Functional Radar-Communication Networks

Dual-functional radar-communication (DFRC) has been viewed as a promising component in future networks. The considered two-cell interfering DFRC network involves two multi-antenna base stations (BSs), and each BS serves multiple single-antenna users and receives echo signals to detect the target. An optimization framework for the joint transmit beamforming and receive filters design of a coordinated two-cell network is formulated. Specifically, we minimize the transmit power at two BSs subject to the signal-to-interference-and-noise ratio constraints. To solve the formulated non-convex optimization problem, an alternating optimization-based method is invoked. For the transmit beamforming design, a locally optimal successive inner convex approximation (SICA)-based method with fast convergence is firstly proposed. To further reduce the computational complexity, we also provide three zero-forcing (ZF)-based sub-optimal methods for practical consideration. For the receive filters design, a power iterations-based method is proposed. Simulation results validate the effectiveness of our proposed SICA-based locally optimal and ZF-based sub-optimal schemes.