This paper focuses on waveform design for multi-input multi-output (MIMO) dual-function radar-communication (DFRC) systems, particularly tailored for environments with multiple single-antenna downlink user equipments (UEs). Our approach leverages orthogonal frequency division multiplexing (OFDM) technology to address the challenges of frequency-selective fading. To mitigate the peak-to-average power ratio (PAPR) issues inherent in OFDM signals, the desired low-PAPR property is also incorporated into the design of the waveforms. For enhanced radar sensing functionality, we introduce an advanced metric, the weighted peak or integrated sidelobe level (WPISL), meticulously crafted to measure and minimize low-range sidelobes. On the communication front, we integrate constructive interference (CI) techniques to significantly enhance quality of service (QoS) in data transmission. To address the intricate optimization challenges presented by our design objectives, we have developed an efficient algorithm anchored in the majorization-minimization (MM) framework. The numerical experiments demonstrate that this algorithm notably surpasses existing state-of-the-art benchmarks in reducing range sidelobe interference. Furthermore, our CI-based approach yields enhanced performance compared to traditional least squares (LS) methods, achieving lower symbol error rates (SER) and higher average achievable sum rates.

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.

In orthogonal frequency division multiplexing (OFDM)-based dual-function radar-communication (DFRC) systems, achieving low peak-to-average power ratio (PAPR) is essential for transmission efficiency and performance. Traditional PAPR constraints, however, fall short in managing the dynamic range of waveforms, thereby limiting power maximization. This study introduces a peak-to-valley power ratio (PVPR) constraint for multiple-input multiple-output (MIMO) DFRC systems to enhance hardware compatibility, waveform fidelity, and consistent transmission power. We develop low-PVPR OFDM waveforms tailored for MIMO-DFRC systems, improving both radar and communication functions. For radar applications, we optimize the weighted peak or integrated sidelobe level (WPISL) and employ phase-encoded waveforms to minimize sidelobes. For communication purposes, we implement a constructive interference (CI) design coupled with a waveform similarity error (WSE) constraint. Utilizing the majorization-minimization (MM) framework, we convert the non-convex problem into a series of convex ones. Numerical results demonstrate significant improvements in sidelobe reduction, symbol error rate (SER), and average achievable rate, thereby validating the effectiveness of our approach in enhancing DFRC systems.

Dual-functional radar–communication systems are extensively employed for the detection and control of unmanned aerial vehicle groups and play crucial roles in scenario monitoring. In this study, we address the downlink precoding problem in large-scale multi-user multiple-input multiple-output dual-function radar–communication systems equipped with low-resolution quantized digital-to-analog converters. To tackle this issue, we develop a weighted optimization framework that minimizes the mean squared error between the transmitted symbols and their estimates while satisfying specific radar performance requirements. Due to the complexity introduced by discrete constraints, we decompose the original problem into three sub-problems to reduce computational burden. Furthermore, we propose a dynamic projection refinement algorithm within the alternating direction method of multiplier framework to efficiently solve these sub-problems. Numerical experiments demonstrate that our proposed method outperforms existing state-of-the-art techniques, particularly in terms of bit error rate in low signal-to-noise ratio scenarios.

In this paper, we try to design the joint waveform and passive beamforming within the context of dual-function radar–communication (DFRC) systems. Focusing on the intricate trade-off between stringent radar beampattern constraints and their desired performance, we introduce a novel manifold idea based on the alternating direction method of multipliers (ADMM) framework. Specifically, our proposed method, named DFRC-MA, could address the challenge of constant modulus waveform design in a multiple-input–multiple-output (MIMO) DFRC system. Firstly, our methodology begins by formulating the reference waveform to achieve an optimal radar beamforming pattern. Subsequently, we define the DFRC optimization problem to mitigate the multi-user interference (MUI) under the constant modulus constraint. Through a series of simulations, we evaluate the efficacy of DFRC-MA, where the integrated waveform designed by DFRC-MA exhibits superior performance over some prevalent ones.

To enhance the coherent synthesis efficiency of distributed coherent aperture radar (DCAR) under low-power calibration conditions, an adaptive subarray position calibration method based on manifold distance estimation (MDE) and symplectic geometry mode decomposition (SGMD) is proposed. The method first employs manifold distance measures to approximate the local neighborhoods of low-power calibration signals, thereby preserving the intrinsic structural information of the underlying signal manifold and deriving a trajectory matrix suitable for low-power calibration scenarios. The signal trajectory matrix is then embedded into a symplectic phase space, where Hamiltonian eigenvalues are obtained via symplectic similarity transformations. This process enables the accurate extraction of subarray spatial information from highly noise-contaminated calibration signals, effectively mitigating noise-induced distortion and improving the accuracy of DCAR coherent parameter estimation. The simulation and experimental results under low-power conditions indicate that when the received calibration signal has a signal-to-noise ratio (SNR) of −12 dB, the proposed method can achieve at least 80% coherent synthesis efficiency in wide-area DCAR. For small-scale DCAR configurations, even with a reduced calibration signal SNR of −18 dB, the method still achieves an SNR gain close to the ideal performance level.