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The global navigation satellite system (GNSS)-based passive radar shows potential for permanent maritime surveillance. In this article, the GNSS signals are exploited for vessel target imaging. From the obtained radar image, meaningful information about the vessel, such as its shape, position, length, and orientation can be extracted. In addition, the vessel is observed from different angles by spatially diverse GNSS satellites, and the multistatic geometry enables to enhance the imagery quality. The main drawback of GNSS-based passive radar stays in its limited power budget. And the inaccessible motion makes the noncooperative vessel smeared using conventional radar imaging methods. To address the problems, at first, each bistatic echo over a long observation time is integrated with the range and Doppler (RD) domain after removing the 2-D migrations. The signal-to-noise ratio (SNR) can be increased after the step. Then, with respect to a particular target velocity, the local Cartesian plane is constructed, and the multiple RD maps are projected and combined in the plane to obtain the multistatic image. In view of the inaccessibility of target kinematic parameters, such imaging processing is modeled as an optimization problem, where vessel’s velocity is set as a decision variable and the aim is to minimize the image entropy. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which a well-focused vessel image can be obtained. In May 2021, we have successfully carried out the world’s first BeiDou-based passive radar maritime experiment, and the effectiveness of the proposed method is verified against the experimental data. [ABSTRACT FROM AUTHOR]

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal FedAvg scheme with error-free model exchange and full device participation. [ABSTRACT FROM AUTHOR]

Massive machine-type communications (mMTC) for the Internet of Things (IoT) are expected to support a large number of devices/users for short packet transmissions with low complexity and low energy consumption. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the desired data for mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to eliminate the multi-user interference, and a denoiser is designed to minimize the mean-squared error (MSE) of the transmitted signals. Then, maximum ${a}$ posteriori (MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellation for each user is designed, and it is shown to be asymptotically optimal as the number of the receiver antennas goes to infinity. Finally, simulation results reveal that the proposed noncoherent scheme outperforms the coherent one in the low SNR regime and for short packet transmissions. [ABSTRACT FROM AUTHOR]

This paper studies the joint digital self-interference (SI) cancellation and data detection in an orthogonal-frequency-division-multiplexing (OFDM) full-duplex (FD) system, considering the effect of phase noise introduced by the oscillators at both the local transmitter and receiver. In particular, an universal iterative two-stage joint SI cancellation and data detection framework is considered and its performance bound independent of any specific estimation and detection methods is derived. First, the channel and phase noise estimation mean square error (MSE) lower bounds in each iteration are derived by analyzing the Fisher information of the received signal. Then, by substituting the derived MSE lower bound into the SINR expression, which is related to the channel and phase noise estimation MSE, the SINR upper bound in each iteration is computed. Finally, by exploiting the SINR upper bound and the transition information of the detection errors between two adjacent iterations, the universal bit error rate (BER) lower bound for data detection is derived. [ABSTRACT FROM AUTHOR]

Data-dependent superimposed training (DDST) scheme has shown the potential to achieve high bandwidth efficiency, while encounters symbol misidentification caused by hardware imperfection. To tackle these challenges, a joint model and data driven receiver scheme is proposed in this paper. Specifically, based on the conventional linear receiver model, the least squares (LS) estimation and zero forcing (ZF) equalization are first employed to extract the initial features for channel estimation and data detection. Then, shallow neural networks, named CE-Net and SD-Net, are developed to refine the channel estimation and data detection, where the imperfect hardware is modeled as a nonlinear function and data is utilized to train these neural networks to approximate it. Simulation results show that compared with the conventional minimum mean square error (MMSE) equalization scheme, the proposed one effectively suppresses the symbol misidentification and achieves similar or better bit error rate (BER) performance without the second-order statistics about the channel and noise. [ABSTRACT FROM AUTHOR]

Massive machine-type communications (mMTC) are expected to support a large amount of randomly deployed users for short package transmissions. Noncoherent random access provides an efficient and practical multi-access protocol for mMTC, and also poses new challenges for the receiver design. In this paper, we leverage two well-known methods, i.e., message passing and deep learning, to jointly detect the user activity and the desired data for the noncoherent mMTC. First, by exploiting the exact distribution information of the received signal, a generalized approximate message passing (GAMP)-based algorithm is proposed, which is shown to jointly detect the user activity and the desired data by two modules: inter-user interference elimination and data detection for each user. Inspired by the two-module GAMP-based algorithm, we then propose a model-driven deep learning method, which utilizes the deep neural networks (DNNs) to approximate both the two modules. The loss function for training the DNNs is derived by formulating the two-module detection as an unconstrained optimization problem. Simulation results reveal that the proposed GAMP-based algorithm outperforms the proposed deep learning method when the channel distribution is perfectly known, while it suffers from a significant performance degradation for the case with imperfect channel distribution information. [ABSTRACT FROM AUTHOR]

This article puts forward a BeiDou-based passive multistatic radar (PMR) maritime moving target (MMT) detection technique via space–time hybrid integration (STHI) processing. Compared with passive bistatic radar (PBR), the utilization of multiple satellites provides an improvement in MMT detection performance, together with the capabilities of localization and velocity estimation. However, the multiple satellite transmitters cause the differences principally in bistatic range and Doppler centroid (DC) of the MMT. To integrate the PMR echoes, the biggest challenge is the two differences that need to be handled. In the proposed technique, first, the centroid-compensated keystone transform (CCKT) is proposed and applied to each PBR echo. It not only corrects range cell migration (RCM) but also equalizes the DC to the same. Then, the long-time integration is performed on each PBR echo, after which it is integrated into the range-Doppler frequency rate (DFR) domain. Finally, in order to settle the difference in bistatic range, an MMT position and velocity domain (i.e., the $X$ – $Y$ – $V$ domain) is constituted. The obtained multiple range-DFR maps are projected to the $X$ – $Y$ – $V$ domain, and then, the effective integration of multistatic echoes can be implemented. The final STHI result allows detecting the MMT reliably. Meanwhile, according to the 3-D position where the MMT is located in the $X$ – $Y$ – $V$ domain, the MMT can be localized, and its velocity can be estimated simultaneously. In May 2021, we have successfully carried out the world’s first BeiDou-based PMR MMT detection experiment, and the experimental results are given to prove the effectiveness of this technique. [ABSTRACT FROM AUTHOR]