In this paper, a wideband direction-of-arrival (DOA) estimation method for underdetermined scenarios is proposed, which effectively solves the basis mismatch problem. Based on the fractional Fourier transform (FRFT), the wideband received signal model with a coprime array is firstly derived by exploiting the aggregation characteristic of wideband linear frequency modulated (LFM) signals in the fractional Fourier (FRF) domain. Then, in order to increase the degree of freedom, an extended uniform linear array is built, and the covariance matrix of the signal is reconstructed by employing the penalized atomic norm minimization with the consecutive spatial dictionary. Meanwhile, without the knowledge of noise level, the noise variance is estimated from the noisy incomplete data, which is utilized to improve the covariance matrix reconstruction performance. Additionally, for the unconditional model, the Cramér-Rao bound for the wideband DOA estimation based on a coprime array is derived. Different from the existing methods, the proposed method not only can estimate more DOAs of wideband signals than the number of physical sensors in the presence of unknown amplitude distortion, but also can obtain more accurate DOA estimation performance without basis mismatch. The effectiveness of the proposed method is verified by our numerical results.

In this letter, a new method on the ${K}$ -factor estimation is proposed for wireless communications over Rician frequency-flat fading channels. Firstly, the mean and variance of the squared envelope of the received signal are respectively derived. Then, we employ them to further derive the closed-form expression for the ${K}$ -factor and finally present its estimation formula. Performance analyses, supported by Monte Carlo simulation results for a range of channel conditions, reveal that our proposed estimator outperforms prior techniques, and illustrate the approximate robustness of our suggested work to the signal-to-noise ratio, maximum Doppler shift, and angle of arrival without considering a priori knowledge.

Aspect-based sentiment analysis (ABSA) is a crucial fine-grained sentiment analysis task that aims to determine sentiment polarity in a specific aspect term. Recent research has advanced prediction accuracy by pre-training models on ABSA tasks. However, due to the lack of fine-grained data, those models cannot be trained effectively. In this paper, we propose the cross-domain generative data augmentation framework (CDGDA) that utilizes a generation model to produce in-domain, fine-grained sentences by learning from similar, coarse-grained datasets out-of-domain. To generate fine-grained sentences, we guide the generation model using two prompt methods: the aspect replacement and the aspect–sentiment pair replacement. We also refine the quality of generated sentences by an entropy minimization filter. Experimental results on three public datasets show that our framework outperforms most baseline methods and other data augmentation methods, thereby demonstrating its efficacy.

As a communication system for maritime monitoring and navigation, automatic identification system (AIS) has been employed for many years, but its service range is limited in 40 nautical miles. Fortunately, an updated service navigation system named as space-based AIS has been presented in recent years, which can provide global coverage service. However, it encounters frequency offset issue caused by the relative satellites/ships motion. To cope with it, in this paper, a novel Doppler shift estimation algorithm is proposed for space-based AIS signals over satellite-to-ship wireless fading channels and non-Gaussian noise. In the estimation stage, the signals including the Doppler shift information are firstly separated from the received ones, but they are also disturbed by the noise. Then, Doppler shift is extracted from the above detached signals via the higher moments which cancel some excrescent parameters by using several flexible algebra operations, and its estimated expression is presented. Finally, numerical simulations are carried out to verify the performance of the proposed method, and simulation results demonstrate the benefits of the suggested scheme.

In wireless communication systems, Rice factor ratio (RFR) defined as K/(1 + K) is a key parameter not only to evaluate the quality of communication channel since it can reveal the severity of the small-scale fading, but also to be employed as a priori information for estimation of other parameters such as frequency. Consequently, its estimation is important for a variety of wireless application scenarios. In this paper, we propose an estimation algorithm on the RFR for the received signals that are disturbed by the Rician doubly selective fading channels and additive noise. During the estimation periods, we initially utilize the known signals to multiply the received signals. Second-order and fourth-order statistics are then employed to further deal with the processed signals mentioned above, which disposes of influence of some unnecessary parameters, e.g., indistinguishable multipaths, maximum Doppler shift, Doppler shift, and noise variance. Finally, a useful expression on the RFR estimation is derived for the Rician frequency selective fast fading channels by flexibly mathematical calculation. Furthermore, the presented method only uses the maximum estimation values of the second-order and fourth-order statistics defined in this paper, which can reduce the computational complexity. Importantly, the investigated scheme is robust to the signal-to-noise ratio over 0 dB and frequency offset (maximum Doppler shift and Doppler shift), and shows a slight improvement on the estimation performance with an increase of the aided data length. The performance and benefits of the proposed approach are verified and evaluated through computer simulations.