Text readability assessment involves categorizing texts based on readers' comprehension levels. Hybrid automatic readability assessment (ARA) models, combining deep and linguistic features, have recently attracted rising attention due to their impressive performance. However, existing hybrid ARA models generally ignore the specific-intrinsic information of deep and linguistic representations, and cannot fully explore their common-intrinsic information. In this paper, we introduce a self-supervised collaborative information bottleneck (SCIB) module for ARA to address these issues. Specifically, we collaboratively consider both specific-intrinsic and common-intrinsic information of the linguistic representation and various levels of deep representations including the document-, sentence- and word-level deep representations, and yield their refined representations via a self-supervised information bottleneck scheme. Extensive experiments are conducted on four English and two Chinese corpora to demonstrate the effectiveness of the proposed model. Experimental results show that the proposed model outperforms state-of-the-art models in terms of four important evaluation metrics, and the suggested SCIB module can effectively capture the specific- and common-intrinsic information.

The hybrid automatic readability assessment (ARA) models that combine deep and linguistic features have recently received rising attention due to their impressive performance. However, the utilization of linguistic features is not fully realized, as ARA models frequently concentrate excessively on numerical values of these features, neglecting valuable structural information embedded within them. This leads to limited contribution of linguistic features in these hybrid ARA models, and in some cases, it may even result in counterproductive outcomes. In this paper, we propose a novel hybrid ARA model named InterpretARA through introducing a linguistic interpreter to better comprehend the structural information contained in linguistic features, and leveraging the contrastive learning that enables the model to understand relative difficulty relationships among texts and thus enhances deep representations. Both document-level and segment-level deep representations are extracted and used for the readability assessment. A series of experiments are conducted over four English corpora and one Chinese corpus to demonstrate the effectiveness of the proposed model. Experimental results show that InterpretARA outperforms state-of-the-art models in most corpora, and the introduced linguistic interpreter can provide more useful information than existing ways for ARA.

This article proposes a framework of cooperative multistation secure transmission in high-frequency skywave communications for wide-area Internet of Things applications by simultaneously exploiting the benefits of massive multiple-input multiple-output and coordinated multiple points communications. In this framework, the original message sent to the user is divided into several submessages by the core network, each of which will be transmitted to the user by the base stations (BSs). A joint optimization problem is established to maximize the user rate by optimally utilizing multiple BSs with multiple antennas for cooperative precoding. Semidefinite programming is introduced for the precoding vector design for the partial channel state information scenario. To solve the mixed-integer optimization problem of selecting BSs, a low complexity algorithm is proposed. Simulation results show that the performance of the proposed algorithm approaches that of exhaustive search, which demonstrates the effectiveness of the proposed algorithm.

In this paper, we investigate the robust precoding with imperfect channel state information (CSI) for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. Starting with a sparse beam based a posteriori channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust precoder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization (MM). Furthermore, we develop a low-complexity BDRP design with an ergodic sum-rate upper bound, simplifying the MM based design algorithm. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

In this paper, we investigate the robust precoding for high frequency skywave massive multiple-input multiple-output communications with imperfect channel state information (CSI). Starting with a sparse beam based a posteriori channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust pre-coder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. Considering the large antenna array aperture and increased signal bandwidth, the effect of the propagation delay across the large-scale antenna array cannot be ignored, and thus the steering vectors vary across different subcarriers. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error (MMSE) based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. In order to reduce the design complexities of the MMSE receiver and precoder, we derive a polynomial expansion based design using a deterministic equivalent. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. The steering vectors vary across different subcarriers due to the effect of the propagation delay across the largescale antenna array. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.