A geometry-based channel model including the arbitrary trajectory of unmanned aerial vehicle (UAV), the power radiation patterns of intelligent reflecting surface (IRS) reflecting elements and transmit antenna elements, mutual coupling among IRS row and column reflecting elements, and IRS coverage area is proposed in this paper. In the line-of-sight (LoS) dominated communication scenarios, only part of the signal power can be reflected by IRS, and the performance gain of the UAV-enabled systems can be enhanced by using the reflected signal power. The mutual coupling among IRS reflecting elements, which limits the performance gain of IRS-assisted systems, is also investigated in the proposed model. Due to the limitation of coverage area of IRS, the IRS is not always effective in the process of UAV movement, and the activated conditions of IRS are also derived. In order to fully consider the influence of UAV's trajectory, we investigate the time-varying parameters including azimuth angles, elevation angles, and suboptimal reflection phases of IRS when it is activated. In the proposed model, we also investigate the influence of the mutual coupling among IRS reflecting elements on spatial-temporal correlation and ergodic channel capacity. Numerical results reveal that the mutual coupling of IRS reflecting elements reduces the performance gain of IRS-assisted systems. Also, it can be observed that IRS coverage area affects the performance gain of IRS-assisted systems, and when both UAV and receiver (Rx) are in IRS coverage area, the performance gain can be enhanced.

The echoes of the radar systems can provide useful information about the target, including range, velocity, shape, and angular direction. Extensive studies have utilized the information of the target to improve system performance, whereas the problem of a favorable closed-form asymptotic approximation of the target’s range information (RI) is seldom investigated. In this paper, we address the problem of obtaining a closed-form asymptotic approximation of the target’s RI in all SNR regions for radar detection systems. The RI is formulated as the mutual information (MI) between the random range and the received signals with complex additive white Gaussian noise (CAWGN). The basis of our scheme is to employ the a posteriori probability density function (PDF) of the range to extract RI from the echoes. We show the a posteriori PDF is a function of the autocorrelation function (ACF) of signal and the cross-correlation function (CCF) of signal-noise. By dividing the integration interval of the a posteriori PDF into the signal-noise interval and noise interval, a closed-form asymptotic approximation of RI is derived based on the probability of range distinguishability and the normalized a posteriori entropies of low and high signal-to-noise ratio (SNR). The results also reveal an interesting relationship between the RI and the uncertainty of the target. As special cases, the closed-form approximations of RI in low and high SNR are obtained. Moreover, for the problem of slightly looser in medium SNR approximation, a better approximation is derived based on a linear model approach. Numerical results are presented to validate the proposed theory and verify the effectiveness of the proposed approximation.

In this study, a maximum likelihood distance estimation algorithm is proposed for a multi‐carrier radar system to estimate the time delay of non‐integer sampling intervals. Particularly, the equivalent relationship between time domain delay and frequency domain phase shift and the relationship between target delay and channel impulse response is used. The idea of maximum likelihood estimation is to find the estimation value that maximises the likelihood function. Therefore, the core idea of this algorithm is the peak search. First, channel estimation is performed on the sampled signal in frequency domain to obtain the channel impulse response. Second, phase compensation and peak search is done to estimate the target delay. Finally, for multiple estimation results, the authors statistic the probability density distribution of the target. In addition, they deduce the Cramer–Rao bound (CRB) of the variance of time delay estimation. The measurement accuracy of the algorithm that they proposed approaches the CRB in the high signal‐to‐noise regime.

Orthogonal time–frequency space (OTFS) modulation has emerged as a promising technology to alleviate the effects of the Doppler shifts in high-mobility environments. As a prerequisite to demodulation and signal processing, automatic modulation classification (AMC) is essential for OTFS systems. However, a very limited number of works have focused on this issue. In this paper, we propose a novel AMC approach for OTFS systems. We build a dual-stream convolutional neural network (CNN) model to simultaneously capture multi-domain signal features, which substantially enhances recognition accuracy. Moreover, we propose a differentiated embedded pilot structure that incorporates information about distinct modulation schemes to further improve the separability of modulation types. The results of the extensive experiments carried out show that the proposed approach can achieve high classification accuracy even under low signal-to-noise ratio (SNR) conditions and outperform the state-of-the-art baselines.

Sensing mutual information (SMI) is widely adopted as a performance metric for integrated sensing and communication (ISAC) to enhance both sensing and communication capabilities. However, conventional approaches derive SMI from amplitude and phase, whereas an explicit evaluation of range mutual information (RMI) remains absent. In this paper, we investigate a novel closed-form approximation of RMI for ISAC. We first derive an explicit expression for the posterior probability density function (PDF) of the target range, which is formulated as a function of the signal’s autocorrelation and cross-correlation. Furthermore, we show that under high signal-to-noise ratio (SNR), the estimated range PDF approximates a Gaussian distribution in the sensing-unconstrained scenario and a truncated Gaussian distribution in the sensing-constrained scenario. Finally, we derive closed-form approximations of the RMI in both scenarios under high SNR. In the sensing-unconstrained scenario, the RMI is proportional to the delay interval, root-mean-square bandwidth, and SNR. In the constrained scenario, we obtain a closed-form RMI approximation by introducing an entropy correction term that quantifies the impact of boundary constraints. Additionally, we employ a maximum likelihood estimation (MLE) method to assess range estimation performance. Simulation results validate the accuracy of the theoretical results and the effectiveness of the proposed approximations.