Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to mitigate the severe Doppler shift in low Earth orbit (LEO) satellite communications. However, the frequency-dependent Doppler shift induced by the high mobility of LEO satellites leads to the Doppler squint effect (DSE). This effect compromises the channel sparsity in the delay-Doppler (DD) domain, rendering existing channel estimation methods ineffective. To overcome this challenge, this paper proposes a DSE-resilient transmission scheme for cyclic prefix OTFS (CP-OTFS)-based LEO satellite systems. Specifically, we analyze the input-output relationship of the CPOTFS- based LEO satellite communication system and derive a DSE-aware representation of the satellite-terrestrial channel in the DD domain. To efficiently capture DSE-aware channel characteristics, we propose a novel OTFS frame structure that allows the energy distribution of the received signal to serve as prior information for channel estimation. Meanwhile, this frame structure strategically allocates pilot symbols to achieve uniform energy distribution and reduce the peak-to-average power ratio (PAPR), while imposing a time-domain waveform continuity constraint to suppress out-of-band emission (OOBE) caused by rectangular pulses. Based on the frame structure, we propose a prior-aided iterative channel reconstruction (PAICR) algorithm to mitigate the severe power leakage induced by DSE. The proposed algorithm iteratively extracts and removes dominant channel components using Doppler-domain received signal energy observations, with a convergence criterion ensuring reliable termination. Furthermore, a Cramer-Rao lower bound is derived to provide a theoretical benchmark for evaluating the algorithm's performance.

Numerous multicarrier modulation schemes have been proposed recently to enhance the performance in narrowband doubly dispersive channels for emerging high-mobility applications. However, the ultra-reliable modulation framework in wideband linear time-varying (LTV) channels remains an open problem, where the time dilations and contractions brought by the high mobility cannot be ignored for the baseband signal to obtain the constant Doppler shift across the whole transmission band. To solve this problem, we propose the hyperbolic frequency multicarrier (HFMC) waveform in this paper based on the inspiration from affine frequency division multiplexing (AFDM) modulation, where the delay and Doppler shift are absorbed into a 1D shift in the affine domain to provide a compact characterization of doubly dispersive discrete-time channels. By adopting the passband representation of wideband LTV channels and hyperbolic frequency modulated (HFM) signals, we reveal that the Doppler scaling factor brought by the relative mobility can be absorbed into an equivalent delay. The basic principle of HFMC modulation is established by investigating the approximate orthogonality among HFMC subcarriers, which are generated from a basic HFM signal by utilizing uniformly spaced equivalent delay. The spectrum of HFMC subcarriers is also analyzed to evaluate the system capacity, where the overlapping nature in the frequency domain can be observed. The input-output characterization in wideband LTV channels is then executed to confirm the 1D integration of time delay and Doppler scaling factor for each path, which demonstrates the ability to exploit potential multipath diversity. The parameter optimization based on the input-output relation and spectrum analysis is finally developed to balance the efficiency and reliability. Numerical results demonstrate the excellent bit error rate (BER) performance of the proposed HFMC waveform in wideband LTV channels.

The recently proposed orthogonal delay-Doppler division multiplexing (ODDM) modulation has been demonstrated to enjoy excellent reliability over doubly-dispersive channels. However, most of the prior analysis tends to ignore the interactive dispersion caused by the wideband property of ODDM signal, which possibly leads to performance degradation. To solve this problem, we investigate the input-output relation of ODDM systems considering the wideband effect, which is also known as the Doppler squint effect (DSE) in the literature. The extra delay-Doppler (DD) dispersion caused by the DSE is first explicitly explained by employing the time-variant frequency response of multipath channels. Its characterization is then derived for both reduced cyclic prefix (RCP) and zero padded (ZP)-based wideband ODDM systems, where the extra DD spread and more complicated power leakage outside the peak region are presented theoretically. Numerical results are finally provided to confirm the significance of DSE. The derivations in this paper are beneficial for developing accurate signal processing techniques in ODDM-based integrated sensing and communication systems.

In the double-sided near-field extremely large-scale multi-input multi-output (XL-MIMO) systems, due to the spherical-wavefront propagation, the line-of-sight (LoS) path exhibits multiple independent propagation components, leading to a channel rank greater than one. In contrast, the non-line-of-sight (NLoS) path is typically dominated by a single propagation component. Consequently, the unified modeling of mixed LoS and NLoS paths remains unresolved, particularly when with non-parallel and non-coplanar uniform linear arrays (ULAs) at the transceivers. Furthermore, there exist bottlenecks in the beamforming codeword design and low-overhead estimation in double-sided near-field communications. In this paper, we present a unified channel model to characterize both LoS and NLoS paths in extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Apart from the transmitter (Tx)-side and receiver (Rx)-side separate response vectors, an additional Tx/Rx-coupled term with a Vandermonde windowing pattern is studied for the XL-MIMO LoS path. Two codebook-based beamfocusing schemes are proposed, which are termed as the beamspace-projection and diagonal-decomposition schemes. The achievable spectral efficiency and average power are theoretically analyzed in closed form. Under this framework, we further propose a low-overhead unified LoS/NLoS orthogonal matching pursuit (UOMP) algorithm for XL-MIMO channel estimation, which is then extended via 3-stage multiple-measurement-vector (3S-MMV) for complexity reduction. Last, simulation results demonstrate the superiority of the proposed strategies in both beamforming codeword selection and sparse channel estimation.

The orthogonal delay-Doppler division multiplexing (ODDM) modulation has been widely acknowledged as a potential candidate for supporting ultra-reliable wireless communications under high-mobility scenarios. Nevertheless, the downlink transmission utilizing ODDM modulation remains an open problem due to the high complexity of prior channel estimation designs and complicated multi-user interference in the delay-Doppler (DD) domain. To address this issue, the channel estimation and data detection for downlink DD domain multiple access (DDMA) are investigated in this paper. The fast Fourier transform (FFT) interpolation is first presented for estimating each equivalent channel delay tap. To further promote the channel estimation accuracy, an orthogonal matching pursuit (OMP)-enabled scheme is also developed, where the normalized delay time and Doppler shift for each virtual path are determined separately to reduce the computational complexity. To ease the data detection at the user equipment (UE) side, we propose the orthogonal precoding for data symbols to provide the Gaussian distribution as the prior knowledge thanks to the central limit theorem, which can support the highly efficient interference cancellation without sharing the constellation information among UEs. Simulation results confirm the excellent performance of the proposed downlink DDMA schemes, where comparable reliability with optimal interference cancellation by sharing the knowledge of constellation among all UEs while the flexibility and efficiency can be guaranteed.

Though meaningful progress has been achieved for mobile wireless networks with the development of orthogonal frequency division multiplexing (OFDM) modulation, the reliability and transmission efficiency of underwater acoustic (UWA) communications are still limited, which cannot support the ever-increasing requirements of underwater applications. The major barrier lies in the wideband time-varying channels with extremely large time scales. Since the time dilation or contraction of baseband signals cannot be ignored in UWA communications, the orthogonality between subcarriers of OFDM is destroyed more significantly than in narrowband high-mobility channels, which leads to severe performance degradation. To solve this problem, a novel multicarrier modulation scheme referred to as the orthogonal hyperbolic frequency division multiplexing (OHFDM) modulation is proposed in this paper inspired by the scale-invariance of hyperbolic frequency signals, where a series of orthogonal narrowband hyperbolic frequency subcarriers (HFSs) is adopted to load data symbols. The input-output relation is then characterized by jointly processing the carrier and subcarrier signals, and selecting the appropriate sampling time of the output of matched filters at the receiver corresponding to the time scale of the wideband time-varying channel. The analysis reveals that the approximate orthogonality can be guaranteed, i.e., much smaller inter-carrier-interference (ICI) than OFDM systems, which enhances the system reliability and reduces the processing complexity at the receiver notably. The robustness of the proposed OHFDM modulation when path-specific scales are involved is also confirmed theoretically in this paper. Simulation results demonstrate that the proposed OHFDM modulation outperforms OFDM in terms of bit error rate (BER) under typical UWA channels with large time scales.

Beyond 5G mobile networks are required to support the ultra-reliable data transmission with massive connectivity under high-mobility scenarios, where the delay-Doppler domain multiple access (DDMA) has been regarded as one of the potential candidates to avoid the severe performance degradation brought by double-dispersive channels. However, existing DDMA transceiver designs heavily rely on the shared codebooks or large guard space among user equipments (UEs), which restricts the flexibility, complexity, and spectral efficiency significantly. In this paper, we first propose the Zadoff-Chu training sequences-based frame structure and an element-wise iterative successive interference cancellation (SIC)-maximal ratio combining (MRC) detector for single-user transmission, which serves as the basis of flexible resource allocation in the delay-Doppler (DD) domain. The discussion is then extended to the MU downlink scenario, where rate splitting is adopted to effectively balance the noise and interference at the UE side to improve the bit error rate performance. For MU uplink cases, a non-orthogonal pilots-based iterative SIC-least square channel estimator is developed to promote the spectral efficiency while the iterative SIC-MRC detector is also provided. Simulation results demonstrate the excellent performance of the proposed DDMA scheme under typical DDMA patterns without guard space between UEs.

Communication objectives with high mobility bring severe Doppler shifts, causing the inter-carrier interference of orthogonal frequency division multiplexing (OFDM) system, which raises the requirements of novel modem schemes. However, existing modem schemes for high-mobility communications face challenges in adapting to diverse channel environments, involving complex channel estimation and etc. Fortunately, the potential of deep learning (DL) has been exploited in various communication applications. In order to design the consistent and robust modem scheme for different channel environments, we propose the DL-based architecture termed the closer twins (CTs) model, which borrows the idea from the Siamese structure in contrastive learning. In specific, two identical network backbones like twins can simultaneously process different channel inputs and make outputs consistent. We design a convlutional neural network called modem network (ModNet) as the backbone for the design of consistent and robust modem scheme. Moreover, to make traditional channel estimation and interpolation methods applicable to the designed modem scheme, a training-aided strategy called random-pilot (R-P) is proposed. In R-P strategy, we simulate the process of conventional channel estimation to modify the objective function of the modem scheme design. Furthermore, the performance of traditional channel estimation can be further improved by DL-based methods. We utilize the CTs model and design the backbone called estimation matrix network (EMNet) to optimize a linear channel estimation method, who outperforms the traditional methods with a similar complexity. Simulation results demonstrate that the proposed modem scheme outperforms OFDM, especially with high Doppler spread. The channel estimation strategy, supported by the R-P strategy and EMNet, achieves lower normalized mean square error compared with traditional methods, contributing to more reliable transmission.

Extremely large-scale antenna array (ELAA) is promising as one of the key ingredients for the sixth generation (6G) of wireless communications. The electromagnetic propagation of spherical wavefronts introduces an additional distance-dependent dimension beyond conventional beamspace. In this paper, we first present one concise closed-form channel formulation for extremely large-scale multiple-input multiple-output (XL-MIMO). All line-of-sight (LoS) and non-line-of-sight (NLoS) paths, far-field and near-field scenarios, and XL-MIMO and XL-MISO channels are unified under the framework, where additional Vandermonde windowing matrix is exclusively considered for LoS path. Under this framework, we further propose one low-complexity unified LoS/NLoS orthogonal matching pursuit (XL-UOMP) algorithm for XL-MIMO channel estimation. The simulation results demonstrate the superiority of the proposed algorithm on both estimation accuracy and pilot consumption.

This article proposes semantic embedding for image transformers (SEiTs) to explore semantic features of facial morphology in the action unit (AU) detection task. The conventional approaches typically rely on external information (e.g., facial landmarks) to obtain the location of facial components, whereas the SEiT can learn morphological features intrinsically from the face image. The pre-training task, namely semantic masked facial image modeling (SMFIM), aims to actively obtain facial morphological information. The pixels of the input facial image are randomly erased with semantic masks (e.g., nose, eyes, eyebrows, mouth, and lip). The embedding model tries to predict the presence of facial components for the input image that can learn semantic representations of the face simultaneously. The learned semantic embeddings are fed to transformer blocks, which enable global interaction between semantic elements. The SEiT integrates facial morphological information and global interaction characters, appropriate for AU detection. The experiments are conducted on the Binghamton-Pittsburgh 4D (BP4D) dataset and Denver intensity of spontaneous facial action (DISFA) dataset, and the results demonstrate the effectiveness of the proposed SEiT.

Extensive work has demonstrated the excellent performance of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios. Time-variant wideband channel estimation serves as one of the key compositions of OTFS receivers since the data detection requires accurate channel state information (CSI). In practical wideband OTFS systems, the Doppler shift brought by the high mobility is frequency-dependent, which is referred to as the Doppler Squint Effect (DSE). Unfortunately, DSE was ignored in overall prior estimation schemes employed in OTFS systems, which leads to severe performance loss in channel estimation and the consequent data detection. In this paper, we investigate DSE of wideband time-variant channel in delay-Doppler domain and concentrate on the characterization of OTFS channel coefficients considering DSE. The formulation and evaluation of OTFS input-output relationship are provided for both ideal and rectangular waveforms considering DSE. The channel estimation is therefore formulated as a sparse signal recovery problem and an orthogonal matching pursuit (OMP)-based scheme is adopted to solve it. Simulation results confirm the significance of DSE and the performance superiority compared with traditional channel estimation approaches ignoring DSE.

Null directions are important parameters for differential beamformers, which play an important role on the beamforming performance. In this paper, we investigate the performance of differential beamformers as a function of the null directions. We first derive the directivity factor (DF) as an explicit function of null and show that the DF decreases to 0 if any null approaches to the desired look direction. We then validate the theoretical analysis through simulations using the beampattern, DF and signal-to-interference gain as the performance measures. The results show that: 1) the performance of a differential beamformer degrades significantly if there is any null close to the desired look direction; 2) with a fixed null direction, increasing the order of the differential beamformer can help improve performance.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with the hybrid precoder employed have been regarded as a reliable option to raise the capacity and coverage of the cellular network. Extensive studies have demonstrated that the capacity achieved by the hybrid precoder with fixed architectures cannot satisfy the requirement of the ever-increasing data traffic. However, the performance of the hybrid precoding can be further promoted by exploiting the flexibility of the precoding structure, where the adaptively-connected architecture can provide a critical enhancement. In this paper, we investigate the adaptively-connected structure and propose a near-optimal hybrid precoding scheme based on the Hungarian algorithm. Simulation results demonstrate the significant advantage of the proposed scheme over existing hybrid precoding algorithms.

In recent years, emotion recognition has become a research focus in the area of artificial intelligence. Due to its irregular structure, EEG data can be analyzed by applying graphical based algorithms or models much more efficiently. In this work, a Graph Convolutional Broad Network (GCB-net) was designed for exploring the deeper-level information of graph-structured data. It used the graph convolutional layer to extract features of graph-structured input and stacks multiple regular convolutional layers to extract relatively abstract features. The final concatenation utilized the broad concept, which preserves the outputs of all hierarchical layers, allowing the model to search features in broad spaces. To improve the performance of the proposed GCB-net, the broad learning system (BLS) was applied to enhance its features. For comparison, two individual experiments were conducted to examine the efficiency of the proposed GCB-net based on the SJTU emotion EEG dataset (SEED) and DREAMER dataset respectively. In SEED, compared with other state-of-art methods, the GCB-net could better promote the accuracy (reaching 94.24 percent) on the DE feature of the all-frequency band. In DREAMER dataset, GCB-net performed better than other models with the same setting. Furthermore, the GCB-net reached high accuracies of 86.99, 89.32 and 89.20 percent on dimensions of Valence, Arousal and Dominance respectively. The experimental results showed the robust classifying ability of the GCB-net and BLS in EEG emotion recognition.

Differential beamformers with concentric circular microphone arrays (CCMAs) are desirable for use in various applications since they can form frequency-invariant spatial responses, have better beam steering flexibility than linear arrays, and suffer less with beampattern irregularity and white noise amplification than circular microphone arrays (CMAs). The methods developed previously for differential beamforming with CCMAs are based on the series expansion. Such methods need to know the analytic form of the target beam-pattern, which may not be accessible in practice. Furthermore, expansion error may lead to erroneous solution, which can cause noise amplification instead of reduction. In this paper, we extend our recently developed beamforming method for CMAs to the design of differential beamformers with CCMAs, which takes advantage of the symmetric null constraints from the beampattern. Simulations are performed to justify the properties of the proposed approach.

Differential microphone arrays (DMAs) can achieve high directivity and frequency-invariant spatial response with small apertures; they also have a great potential to be used in a wide spectrum of applications for high-fidelity sound acquisition. Although many efforts have been made to address the design of linear DMAs (LDMAs), most developed methods so far only work for the situation where the source of interest is incident from the endfire direction. This paper studies the steering problem of differential beamformers with linear microphone arrays. We present new insights into beam steering of LDMAs and propose a series of steerable differential beamformers. The major contributions of this paper are as follows. 1) A series of ideal functions are defined to describe the ideal, target beampatterns of LDMAs. 2) We prove that first-order differential beamformers with linear microphone arrays are not steerable and their mainlobes can only be at the endfire directions. 3) We deduce the fundamental conditions for designing steerable differential beamformers with LDMAs. 4) We develop a method to design steerable beamformers with LDMAs using null constraints. Simulations and experiments validate the properties of the developed method.

Microphone arrays combined with beamforming have been widely used to solve many important acoustic problems in a wide range of applications. Much effort has been devoted in the literature to microphone array beamforming, among which the Kronecker product beamforming method developed recently has demonstrated some interesting properties. Generally, this method decomposes the global beamforming filter into a Kronecker product of a number of sub-beamforming filters, each of which corresponds to a virtual subarray and can be designed individually. This decomposition not only reduces significantly the number of beamforming coefficients, but also can be explored to improve the robustness and flexibility of beamforming. This paper extends Kronecker product beamforming from two-dimensional arrays into three-dimensional cube arrays. We consider two decompositions, i.e., fully and partially separable ones. The former decomposes the entire array into three linear subarrays while the latter decomposes the entire array into a linear subarray and a planar one. Then, for each case, we derive the Kronecker product maximum white noise gain beamformer, the Kronecker product approximate maximum directivity factor (DF) beamformer, the Kronecker product null-steering beamformer, and the Kronecker product iterative maximum DF beamformer. Simulation results demonstrate the properties and advantages of the proposed beamformers.

Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns. To this end, from the viewpoint of algorithm, we propose a local discriminant preservation projection (LDPP) model, an under-studied subspace learning algorithm, that concerns the local discrimination and neighborhood structure preservation. In contrast with other conventional subspace projection methods, LDPP has two merits. On one hand, with local discrimination it has a higher tolerance to abnormal data or outliers. On the other hand, it can project the data to a more separable space with local structure preservation. Further, support vector machine, extreme learning machine (ELM), and kernelized ELM (KELM) have been used as classifiers for taste recognition in E-Tongue. Experimental results demonstrate that the proposed E-Tongue is effective for multiple tastes recognition in both efficiency and effectiveness. Particularly, the proposed LDPP-based KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in http://www.leizhang.tk/ tempcode.html.

Multi-Installment Divisible-Load Scheduling model is a hot topic in the field of Big Data Processing in heterogeneous parallel and distributed systems. The effective division of data and the determination of scheduling strategy are the key and difficult problems. Minimizing the make-span of the entire divisible load is the primary objective of multi-installment scheduling in heterogeneous parallel and distributed systems. It has been demonstrated that the make-span is minimized when the processor sequence follows the order in which the link speed decrease in single-installment scheduling, however, the optimization of multi-installment divisible-load scheduling is a very hard problem. The descending order of link speeds is usually not an optimal order. To solve this problem, we propose a multi-installment scheduling model considering the processor order, and design an efficient global optimization genetic algorithm to solve the model. Experimental results show that the proposed algorithm has better performance than that of the compared multi-Installment methods.

In recent years, research on facial expression recognition (FER) has become an increasingly active research topic. Deep learning is a new area, which gives a new way to classify images of human faces into emotion categories. However, it faces many difficulties caused by poor robustness and real-time performance. This paper designs a new architecture network based on Broad Learning System (BLS) for facial expressions recognition. It is established as a flat network. The original inputs are transferred and placed as mapped features in feature nodes, while the structure is expanded in wide sense in the enhancement nodes. To evaluate our architecture we tested the proposed method with the Extended Cohn-Kanade Dataset (CK+). The experimental results show that the BLS approach is very effective in facial expression recognition to compare with convolutional neural networks.