The Internet of Things (IoT) employing symbiotic radio (SR) technology encounters challenges such as low throughput and susceptibility to double fading. To address these challenges, this paper integrates non-orthogonal multiple access (NOMA) with simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) technology in an SR system, introducing a novel transmission model termed STAR-RIS-assisted NOMA-SR with hybrid active-passive communication. The proposed model operates in three phases. In the first two phases, when the primary system’s licensed spectrum is occupied, the backscatter devices (BDs) utilize backscatter communication (BC) to establish a symbiotic relationship with the primary system. Specifically, in Phase 1, STAR-RIS enhances the energy harvesting (EH) of BDs via the reflection mode, while in Phase 2, it aids both the primary and secondary systems via the transmission mode. In Phase 3, when the licensed spectrum is idle, STAR-RIS facilitates the active communication (AC) of BDs via the transmission mode. To maximize the total throughput of BDs while guaranteeing the primary system’s target throughput, we formulate a non-convex optimization problem and develop a block coordinate descent (BCD)-based resource allocation scheme. The problem is decomposed into subproblems and solved using successive convex approximation (SCA), variable substitution, and semi-definite relaxation (SDR) to jointly optimize transmission time, beamforming, STAR-RIS reflection and transmission coefficients, as well as BDs’ power allocation and reflection coefficients. Numerical results show that the proposed scheme enhances the total throughput of BDs by 14.36%, 43.43%, 67.78%, and 439.69% compared to four baseline schemes.

In this paper, the equivalence between the rate maximization and weighted minimum mean square error (WMMSE) minimization problems is utilized to design the hybrid beamforming scheme for millimeter wave relay systems. A deep unfolding method is proposed based on the structure of the WMMSE iterative algorithm, and the back propagation algorithm is updated based on the generalized chain rule of complex matrix gradients. Simulation results show that the proposed method has the satisfactory generalization ability, and prevails existing methods both in the spectral efficiency and running time.

Otoacoustic emission (OAE) biometrics are inherently robust to replay and falsification attacks. The widely studied transient-evoked OAE (TEOAE) is non-stationary and offers biometric value only in normal-hearing individuals since it is more susceptible to hearing loss. To address these issues, this paper presents a novel yet promising OAE biometric modality-stimulus-frequency OAE (SFOAE). Unlike TEOAE, SFOAE is a highly stationary signal whose fine structures are idiosyncratic to an individual, and relatively stable over time, making it easier to be a biometric without additional complex feature extraction. Moreover, SFOAE is even present in ears with 50 dB HL hearing loss, applicable to hearing-impaired users. In this paper, SFOAE spectra in response to three stimulus levels are fused in the feature level to consolidate different information, followed by a linear discriminant analysis or a multi-kernel convolutional neural network to further reduce the intra-subject variability and increase the inter-subject variability. Tested on a large cohort of subjects containing varying levels of deafness, the SFOAE-based biometric system yields an equal error rate of 0.541% and 1.364% in closed-set and open-set verification scenarios, respectively. In an identification mode, 99.43% and 97.37% accuracies are attained for closed-set and open-set protocols, respectively. In particular, we observe perfect performance in a population restricted to normal hearing in closed-set scenarios. The reason why the system performs well has been examined based on several comparative tests. Although there are implementation issues to be resolved before SFOAE can be applied in the field of biometric, this paper preliminarily demonstrates the basis and excellent potential of SFOAE as a biometric.

It remains a tough challenge to tackle sound event localization and detection (SELD) problem, especially when sound scene complexity increases and overlapping acoustic sources appear. To improve the SELD performance, we propose an ensemble system, which consists of a ResNet and Conformer backbone network (SELD-RCnet) and its two variants, SED-RCnet and SSL-RCnet. For SELD-RCnet and SSL-RCnet, we use short time Fourier transform (STFT) magnitude spectrogram, phase spectrogram, and active acoustic intensity vectors (IVs) as input features. For SSL-RCnet, an innovative predictive target is also developed and the performance is thus improved. For SED-RCnet, we use Log-Mel spectrogram as input features. To overcome the lack of training data, we adopt two novel approaches to first order Ambisonic (FOA) format dataset augmentation, namely audio channel swapping (ACS) and time-frequency masking (TFM). Finally, in the L3DAS22 Challenge, our submitted system achieves significant improvements over the baseline and ranks the second place for the Task2. Therefore, according to the competition rules, we submit this work to describe our system in details.

In this paper, we utilize the full-duplex mode to further improve the rate of millimeter wave communications. Correlated channel estimation errors are considered to develop a robust hybrid beamforming scheme. A zero-space projection based method is proposed to compress the self-interference. Then, analog parts of the transceiver are designed to maximize the gains of RF-to-RF effective channels. Finally, digital parts of the transceiver are solved iteratively by utilizing the equivalence between the maximization of mutual information and the minimization of weighted minimum mean squared error. The simulation results show that the proposed scheme prevails other existing designs.

A novel time-frequency masking approach for circular microphone array speech enhancement in the presence of competing interference and background noise is proposed in this paper. Multichannel speech enhancement systems can often be constructed by a concatenation of a beamformer and a single-channel postfilter, which rely on accurate estimation of steering vector and the residual interference plus noise power spectrum density (PSD), respectively. However, the performance of existing multiple microphone speech enhancement algorithm will degrade in the presence of competing interference. The proposed phase-based time-frequency masking approach can improve the estimation of the steering vector and residual interference plus noise PSD in the presence of competing interference and background noise. The experimental analysis verifies the advantages achieved by the proposed method, in comparison with the state-of-the-art multiple microphone speech enhancement methods.

The speech enhancement problem is studied using an improved robust generalized sidelobe canceler (GSC) beamforming algorithm based on microphone array, in the cases of speaker noise and the music interferences. The conventional GSC algorithm based on variable step size and a priori signal-to-noise ratio (SNR) algorithm is not robust under the nonstationary noise because the solution of the signal-to-noise ratio (SNR) is not given. To enhance the robustness, in this paper, a improved GSC algorithm is developed, where adaptive filter coefficients are updated based on signal output power ratio (SPR). The numerical studies including speaker noise and music noise demonstrate that the improved algorithm outperforms the traditional GSC and the GSC based on variable step size technique.

In this paper, robust transceiver designs are proposed for millimeter wave multiple-input multiple-output cooperative systems with the amplify-and-forward cooperative strategy. Hybrid structures are adopted to jointly design the processors at the source, the relay, and the destination, respectively. Contrariwise to most existing works that develop codebook-based solutions with perfect channel state information (CSI) assumptions, the proposed designs present codebook- free solutions based on the so-called Alternating Direction Method of Multipliers approach and imperfect CSI with Gaussian-distributed errors. Numerical results show that the proposed designs provide substantial improved spectral efficiencies compared with the existing designs.

The problem of blind image recovery using multiple blurry images of the same scene is addressed in this paper. To perform blind deconvolution, which is also called blind image recovery, the blur kernel and image are represented by groups of sparse domains to exploit the local and nonlocal information such that a novel joint deblurring approach is conceived. In the proposed approach, the group sparse regularization on both the blur kernel and image is provided, where the sparse solution is promoted by -norm. In addition, the reweighted data fidelity is developed to further improve the recovery performance, where the weight is determined by the estimation error. Moreover, to reduce the undesirable noise effects in group sparse representation, distance measures are studied in the block matching process to find similar patches. In such a joint deblurring approach, a more sophisticated two-step interactive process is needed in which each step is solved by means of the well-known split Bregman iteration algorithm, which is generally used to efficiently solve the proposed joint deblurring problem. Finally, numerical studies, including synthetic and real images, demonstrate that the performance of this joint estimation algorithm is superior to the previous state-of-the-art algorithms in terms of both objective and subjective evaluation standards. The recovery results of real captured images using unmanned aerial vehicles are also provided to further validate the effectiveness of the proposed method.

This work addresses image recovery problem in the presence of salt-and-pepper noise and image blur. The salt-and-pepper noise reviewed as the impulsive noise, in this paper, is modeled as a sparse signal because of its impulsiveness. To accurately reconstruct the clean image and the blur kernel, the framelet domains are exploited to sparsely represent the image and the blur kernel. From the reformulations conducted, a joint estimation is devised to simultaneously perform the image recovery, the salt-and-pepper noise suppression and the blur kernel estimation under a optimization framework. To solve the optimization problem, an efficient solver based on accelerated proximal gradient (APG) is developed to obtain the joint estimation solution. Numerical studies demonstrate the superior performance of the joint estimation algorithm compared with the state-of-the-art approaches in terms of both objective and subjective evaluation standards.

The problem of synthetic aperture radar image recovery in the presence of wideband interference (WBI) is investigated. Delayed versions of a transmitted signal are utilized to construct a dictionary in which a signal of interest (SOI) has a sparse representation. In this letter, WBI is sparsely represented by the time-frequency domain. By utilizing the transform domains, a joint estimation approach is devised to simultaneously perform WBI suppression and SOI recovery within an optimization framework. Based on the separability property in the optimization, an alternating direction method of multipliers-based approach is developed to efficiently obtain a solution. Finally, simulation results are presented to demonstrate the superior performance of the joint estimation algorithm.

This letter addresses the problem of synthetic aperture radar (SAR) image recovery in the presence of radio frequency interference (RFI), which degrades SAR image quality if it is not effectively suppressed. In this letter, the RFI is modeled as the superposition of multiple complex sinusoids such that the RFI suppression problem is transformed to a frequency estimation problem. To accurately estimate the amplitudes of the sinusoids and their corresponding frequencies in the case of a low number of range samples, the frequency sparsity in the frequency domain is successfully exploited. From the estimated amplitudes and frequencies, the RFI can be reconstructed and then used for suppression. To recover the signal of interest (SOI) and by utilizing the estimated RFI, a joint estimation is derived to simultaneously perform the RFI suppression and the SOI recovery. This joint approach can effectively suppress the RFI even if it overlaps with the SOI in both the time and frequency domains. The common threshold decision approach is not required for our joint estimation to reduce the RFI. Simulation results and real-world experiments are presented to demonstrate the superior performance of the joint estimation algorithm.

With an appropriate geometric configuration, a helicopter-borne rotating synthetic aperture radar (ROSAR) can break through the limitations of conventional strip-map monostatic SAR on forward-looking imaging. Owing to such a capability, ROSAR has extensive potential applications, such as self-navigation and self-landing. Moreover, it has many advantages if combined with frequency-modulated continuous wave (FMCW) technology. In this letter, a novel geometric platform configuration and an imaging algorithm for helicopter-borne FMCW-ROSAR are proposed. First, by adopting the higher order approximation of slant range model to improve the azimuth resolution for FMCW-ROSAR, the precise 2-D spectrum of the echo signal is derived based on series reversion. Moreover, at the same time, the Doppler offset caused by the continuous motion of the antenna is analyzed and compensated as well. Then, according to the analysis on the range-dependent velocity variation caused by ROSAR geometric configuration, an efficient inverse chirp-Z transform is utilized to remove the variant range cell migration, and a well-focused SAR image can thus be obtained. Finally, the experimental results with simulated data demonstrate the effectiveness of the proposed algorithm.