This paper investigates a general integrated sensing and covert communication (ISCC) system, where a base station (BS) transmits signals to covert users (CUs) while simultaneously sensing a dynamic target that acts as a warden (WA), maliciously attempting to eavesdrop on the covert communication. An essential task in realizing ISCC is the beamforming design, which however, is complicated by the dynamic nature of both the WA and the CUs in practice, i.e., (i) the rapid movement of the WA and (ii) the time-varying number of CUs. To address these challenges, in this paper, we develop a versatile recurrent graph neural network (RGNN)-based beamforming design framework, where the penalty method is first employed to transform the constrained optimization problem into an unconstrained one, and then an RGNN is customized to effectively output the beamforming vectors. Through implicitly learning features from the historical warden detection channels and the instantaneous channel state information among CUs and BS, the proposed approach could predict the next-time slot beamforming matrix while accommodating a scalable number of CUs, thus eliminating repeated WA channel estimation and re-optimization when handling dynamic scenarios. Moreover, a convolutional long short-term memory (CLSTM)-augmented message passing GNN (CL-MPGNN) is developed to realize the RGNN framework. In particular, a CLSTM module is first adopted to exploit the spatial-temporal features from the input to facilitate an effective predictive beamforming. Then, a set of message-passing layers is employed to guarantee the scalability of the beamforming design. Simulations verify the effectiveness of the proposed algorithm in terms of the covert communication performance, the covert communication-sensing tradeoff, and the generalizability, respectively.

To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach. However, an efficient precoder design highly depends on the accurate instantaneous channel state information at the transmitter (ICSIT), which however, is not always available in practice. To overcome this problem, in this paper, we focus on the orthogonal time frequency space (OTFS)-based URLLC system and adopt a deep learning (DL) approach to directly predict the precoder for the next time frame to minimize the frame error rate (FER) via implicitly exploiting the features from estimated historical channels in the delay-Doppler domain. By doing this, we can guarantee the system reliability even without the knowledge of ICSIT. To this end, a general precoder design problem is formulated where a closed-form theoretical FER expression is specifically derived to characterize the system reliability. Then, a delay-Doppler domain channels-aware convolutional long short-term memory (CLSTM) network (DDCL-Net) is proposed for predictive precoder design. In particular, both the convolutional neural network and LSTM modules are adopted in the proposed neural network to exploit the spatial-temporal features of wireless channels for improving the learning performance. Finally, simulation results demonstrated that the FER performance of the proposed method approaches that of the perfect ICSI-aided scheme.

This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent reliability performance, pragmatic precoder design is an effective and indispensable solution. However, the design requires accurate instantaneous channel state information at the transmitter (ICSIT) which is not always available in practice. Motivated by this, we adopt a deep learning (DL) approach to exploit implicit features from estimated historical delay-Doppler domain channels (DDCs) to directly predict the precoder to be adopted in the next time frame for minimizing the frame error rate (FER), that can further improve the system reliability without the acquisition of ICSIT. To this end, we first establish a predictive transmission protocol and formulate a general problem for the precoder design where a closed-form theoretical FER expression is derived serving as the objective function to characterize the system reliability. Then, we propose a DL-based predictive precoder design framework which exploits an unsupervised learning mechanism to improve the practicability of the proposed scheme. As a realization of the proposed framework, we design a DDCs-aware convolutional long short-term memory (CLSTM) network for the precoder design, where both the convolutional neural network and LSTM modules are adopted to facilitate the spatial-temporal feature extraction from the estimated historical DDCs to further enhance the precoder performance. Simulation results demonstrate that the proposed scheme facilitates a flexible reliability-latency tradeoff and achieves an excellent FER performance that approaches the lower bound obtained by a genie-aided benchmark requiring perfect ICSI at both the transmitter and receiver.

The implementation of integrated sensing and communication (ISAC) highly depends on the effective beamforming design exploiting accurate instantaneous channel state information (ICSI). However, channel tracking in ISAC requires large amount of training overhead and prohibitively large computational complexity. To address this problem, in this paper, we focus on ISAC-assisted vehicular networks and exploit a deep learning approach to implicitly learn the features of historical channels and directly predict the beamforming matrix for the next time slot to maximize the average achievable sum-rate of system, thus bypassing the need of explicit channel tracking for reducing the system signaling overhead. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system. Then, a historical channels-based convolutional long short-term memory network is designed for predictive beamforming that can exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed method can satisfy the requirement of sensing performance, while its achievable sum-rate can approach the upper bound obtained by a genie-aided scheme with perfect ICSI available.

This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system taking into account the multiple access interference. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channels-based convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information available.

Channel estimation is one of the main tasks in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MUC) systems. However, different from traditional communication systems, an IRS-MUC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MUC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.

The realization of practical intelligent reflecting surface (IRS)-assisted multi-user communication (IRS-MUC) systems critically depends on the proper beamforming design exploiting accurate channel state information (CSI). However, channel estimation (CE) in IRS-MUC systems requires a significantly large training overhead due to the numerous reflection elements involved in IRS. In this paper, we adopt a deep learning approach to implicitly learn the historical channel features and directly predict the IRS phase shifts for the next time slot to maximize the average achievable sum-rate of an IRS-MUC system taking into account the user mobility. By doing this, only a low-dimension multiple-input single-output (MISO) CE is needed for transmit beamforming design, thus significantly reducing the CE overhead. To this end, a location-aware convolutional long short-term memory network (LA-CLNet) is first developed to facilitate predictive beamforming at IRS, where the convolutional and recurrent units are jointly adopted to exploit both the spatial and temporal features of channels simultaneously. Given the predictive IRS phase shift beamforming, an instantaneous CSI (ICSI)-aware fully-connected neural network (IA-FNN) is then proposed to optimize the transmit beamforming matrix at the access point. Simulation results demonstrate that the sum-rate performance achieved by the proposed method approaches that of the genie-aided scheme with the full perfect ICSI.

Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from traditional communication systems, an IRS-MC system generally involves a cascaded channel with a sophisticated statistical distribution, which hinders the implementations of the Bayesian estimators. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a data-driven approach to realize the channel estimation. Specifically, we propose a convolutional neural network (CNN)-based deep residual network (CDRN) to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. In the proposed CDRN, a CNN denoising block equipped with an element-wise subtraction structure is designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously, which further improves the estimation accuracy. Simulation results demonstrate that the proposed method can almost achieve the same estimation accuracy as that of the optimal minimum mean square error (MMSE) estimator requiring the knowledge of the channel distribution.

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of communication channel and directly recover tag symbols. Inspired by the powerful capability of convolutional neural networks (CNN) in exploring the features of data in a matrix form, we design a novel covariance matrix aware neural network (CMNet)-based detection scheme to facilitate DTL for tag signal detection, which consists of offline learning, transfer learning, and online detection. Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based on the minimum error probability (MEP) criterion. Taking advantage of the outstanding performance of DTL in transferring knowledge with only a few training data, the proposed scheme can adaptively fine-tune the detector for different channel environments to further improve the detection performance. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

The existing spectrum sensing methods mostly make decisions using model-driven test statistics, such as energy and eigenvalues. A weakness of these model-driven methods is the difficulty in accurately modeling for practical environment. In contrast to the model-driven approach, in this paper, we use a deep neural network to automatically learn features from data itself, and develop a data-driven detection approach. Inspired by the powerful capability of convolutional neural network (CNN) in extracting features of matrix-shaped data, we use the sample covariance matrix as the input of CNN, proposing a novel covariance matrix-aware CNN-based detection scheme, which consists of offline training and online detection. Different from the existing deep learning-based detection methods which replace the whole detection system by an end-to-end neural network, in this work, we use CNN for offline test statistic design and develop a practical threshold-based online detection mechanism. Specially, according to the maximum a posteriori probability (MAP) criterion, we derive the cost function for offline training in the spectrum sensing model, which guarantees the optimality of the designed test statistic. Simulation results have shown that whether the PU signals are independent or correlated, the detection performance of the proposed method is close to the optimal bound of estimator-correlator detector. Particularly, when the PU signals are correlated with a correlation coefficient 0.7, the probability of detection of the proposed method outperforms the conventional maximum eigenvalue detection method by nearly 7.5 times at SNR = -14dB.

One of the key problems in spectrum sensing is to design the test statistic. Existing methods generally exploit the model-based features as the test statistic, such as energies and eigenvalues. However, these features could not accurately characterize the real environment. Motivated by this, in this paper, we use a deep neural network (DNN) to intelligently explore the data-driven test statistic. Firstly, we introduce a DNN-based detection framework, where a DNN-based likelihood ratio test (DNN-LRT) is derived to guarantee the optimality of the designed test statistic. As a realization of the developed DNN-based framework, we use the sample covariance matrix as the input of a convolutional neural network (CNN), and propose a covariance matrix-aware CNN (CM-CNN)-based spectrum sensing algorithm, which further improves the performance. In addition, we also provide the theoretical analysis of the proposed method. To the best of our knowledge, it's the first time to analyze the theoretical performance of CNN-based methods. Finally, simulation results demonstrate that the performance of the proposed method is close to that of the optimal detector. Particularly, the proposed method could achieve a detection probability of 96.7% with a false alarm probability of 1.9% at SNR = -18dB, which significantly outperforms the conventional methods.