In this paper, we propose a waveform design based electronic counter-countermeasures (ECCM) scheme against interrupted sampling repeater jamming (ISRJ) for wideband multiple-input multiple-output (MIMO) radar. The ECCM scheme includes two steps: ISRJ parameters estimation and ISRJ signal suppression. The ISRJ signal interpretations based on time-frequency (TF) domain and Doppler domain are proposed respectively. Based on the Doppler interpretation, a waveform with thumbtack ambiguity function and a corresponding multiple Doppler channels processing structure are proposed to obtain ISRJ parameters. According to the TF interpretation and the known ISRJ parameters, a waveform with desired TF distribution and a corresponding frequency filter processing strategy are proposed to suppress ISRJ signal. The proposed ECCM scheme can be directly used in phased-array mode without waveform optimization. Considering the multiple targets scenario, the simultaneous multiple beams technology in MIMO radar is preferred. Therefore, a constant modulus waveform design method is proposed to match the desired transmit beampattern and TF distribution. Besides, in order to further suppress the sidelobe output of ISRJ signal, an extended version of the proposed ECCM scheme in the slow-time dimension is proposed. Finally, the numerical simulations are provided to demonstrate the effectiveness of the proposed ECCM scheme in multiple targets scenario.

In this paper, we propose a novel precoding algorithm based on an unsupervised learning graph attention network (ULGAT) to improve the performance of the sum rate in reconfigurable intelligent surface (RIS) assisted communication systems. Specifically, an unsupervised learning scheme is used to optimise the precoding of RIS-assisted multi-user downlink systems, which significantly reduces the difficulty of obtaining samples. Then, a graph attention network (GAT) based on the multi-head attention is employed to refine the performance of the precoding, where the underlying topology formed is fully utilized by the channel matrix and phase shift matrix. The proposed ULGAT algorithm can obtain better performance by exploiting the learning capability of the GAT and making full use of the network topology with unsupervised learning to produce datasets at a low cost. The results of the simulation demonstrate that the proposed ULGAT algorithm significantly outperforms the existing algorithms in terms of the sum rate and generalization capability.

In this paper, we consider the joint optimization of transmit waveform and receive filter in colocated multiple-input multiple-output (MIMO) radar to enhance target detection performance in the presence of signal-dependent interference. It is noticed that in the detection stage, the output energy is mainly concentrated in the mainlobe region. Therefore, we decompose the receive filter as the cascade of a receive beamformer and a temporal filter. Then, under the peak-to-average ratio (PAR) constraint, a problem is formulated to realize a trade-off between the signal-to-interference-plus-noise ratio (SINR) and the integrated sidelobe level (ISL) at the pulse compression output of mainlobe synthesized signal. A non-decreasing algorithm, which is the combination of sequential optimization algorithm and minorization-maximization (MM) method, is developed to solve this problem. Besides, in order to reduce computation burden, a special case is proposed, where we fix the temporal filter as the mainlobe synthesized signal. Then, another non-decreasing algorithm based on the MM method is proposed to solve the special case. Numerical experiments show that the proposed algorithms can obtain high output SINR and low output ISL efficiently.

An extremely large-scale intelligent reflecting surface (XL-IRS) aided communication system is studied. Although XL-IRS can effectively combat the double path-loss attenuation, the large aperture size introduces significant near-field effects. The complex near-field propagation and large number of XL-IRS elements can lead to optimality and complexity challenges in beam focusing design. Focusing on a spectral efficiency (SE) maximization problem, two unsupervised learning based algorithms are conceived for the joint optimization of base station and XL-IRS beam focusing, which operate without pre-training and exhibit strong robustness. Specifically, a dense-connected dilated autoencoder meta learning (DDAML) algorithm is proposed to achieve high SE by utilizing dense connections, while reasonably designing an autoencoder to reduce computational complexity. Furthermore, considering a need for low execution time in practical applications, a convolutional dilated autoencoder meta learning (CDAML) algorithm is also proposed to further reduce computational complexity. Simulation results show that the proposed DDAML algorithm achieves the highest SE, while the proposed CDAML algorithm significantly reduces computational complexity at the cost of limited SE loss. Moreover, the two proposed algorithms also demonstrate remarkable robustness in XL-IRS-aided near-field communications.

The power spectral density (PSD) shape of the transmit waveform plays an important role in some fields of radar, such as electronic counter-countermeasures (ECCM), target detection, and target classification. In addition, radar hardware generally requires the waveform to have constant modulus (CM) characteristics. Therefore, it is a significant problem to synthesize the discrete-time CM waveform from a given PSD. To address this problem, some algorithms have been proposed in the existing literature. In this paper, based on the majorization–minimization (MM) framework, a novel algorithm is proposed to solve this problem. The proposed algorithm can be proved to converge to the stationary point, and the error reduction property can be obtained without the unitary requirements on the discrete Fourier transform (DFT) matrix. To accelerate the convergence rate of the proposed algorithm, three acceleration schemes are developed for the proposed algorithm. Considering a specific algorithm stopping condition, one of the proposed acceleration schemes shows better computation efficiency than the existing algorithms and is more robust to the initial points. Besides, when the DFT matrix is not unitary, the numerical results show that the proposed acceleration scheme has better matching performance compared with the existing algorithms.