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Symbiotic radio (SR) has emerged as a spectrum and energy-efficient communication paradigm for future passive Internet-of-Things (IoT). In this paper, we consider a multiuser multi- backscatter-device (BD) SR communication system to enhance the spectrum efficiency, by sharing a common time-frequency resource block. Due to the presence of inter-user and inter-BD interference, multiuser and multi-BD detection in the receiver design become much more challenging. Concretely, the detection problem involves several key components: direct-link channel estimation, backscatter-link channel estimation, user signal decoding, and BD symbol detection. A conventional way is to realise these components in two separate phases, in which a channel estimation phase is followed by a data decoding phase. However, channel state information (CSI) acquisition is very difficult for the multiuser multi-BD SR communication, as compared to the case of orthogonal multiple access. In addition, the backscatter-link is relatively weak, which further increases the difficulty of CSI acquisition. To address these issues, we propose a novel receiver design to perform joint channel estimation, user data decoding, and BD symbol detection. Based on the factor graph representation of the joint estimation problem, we design a message-passing receiver for the multiuser multi-BD SR system to iteratively refine the estimation outputs. Extensive simulation results demonstrate the effectiveness of the proposed receiver design. [ABSTRACT FROM AUTHOR]

The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts. [ABSTRACT FROM AUTHOR]

Noise fitting plays a key role in underwater acoustic communications. Traditional approximate models can fit global heavy-tail distribution of the impulsive noise with fixed parameters. These models are unable to cover local distributions with arbitrary lengths. In this paper, we propose a generative-adversarial-network-based underwater noise simulator (GUNS), which constructs a deep-neural-network-based generator and a convolutional-neural-network-based discriminator are constructed to learn the heavy-tail distribution of the impulsive noise. Based on the noise collected in the Wuyuanwan Bay, Xiamen, probability distribution function of the underwater acoustic noise simulated by the proposed GUNS performs lower Kullback-Leibler divergence, Jensen-Shannon divergence and mean square error than that employed traditional approximate models. [ABSTRACT FROM AUTHOR]

Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%. [ABSTRACT FROM AUTHOR]

In this article, we study the receiver design for the uplink transmission of a human-type communications (HTC) and machine-type communications (MTC) (H&M) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO and signal sparsity of MTC packets. With the probability model, we propose to conduct joint device activity identification, channel estimation, and signal detection. We develop a message-passing-based statistical interference framework to systematically and efficiently solve the joint estimation problem for the H&M coexisted massive MIMO system. Specifically, we propose two receiver schemes based on time-slotted and non-time-slotted grant-free random access for massive machine-type device connectivity. We show that, by exploiting the channel and signal sparsity, our proposed message-passing-based algorithms significantly outperform the conventional training-based approaches in which the device activity state and the channel are estimated by sending pilots prior to data transmission, and are able to approach the genie bound with known signal support in the relatively high signal-to-noise (SNR) regime. Last but not least, we show that there exists a significant gain in terms of the number of admissible devices in the system by allowing H&M coexistence, as compared to orthogonal transmission approaches in which different time/frequency slots are assigned to HTC and MTC services. [ABSTRACT FROM AUTHOR]

This letter is concerned with the high peak-to-average power ratio (PAPR) problem in filtered orthogonal frequency-division multiplexing (f-OFDM) systems. Iterative clipping and filtering (ICF) is adopted as a simple and effective method to suppress PAPR but introduces signal distortion. At the receiver, we propose a sparsity-learning based iterative algorithm for distortion compensation by exploiting the sparsity of the distortion. Specifically, our iterative algorithm consists of a linear estimator, a soft demodulator, and a distortion estimator. The iteration between the three modules is based on turbo message passing. We show that our scheme outperforms the conventional iterative methods in both complexity and performance. Numerical results demonstrate that our algorithm achieves more effective PAPR suppression and better error rate performance. [ABSTRACT FROM AUTHOR]

This paper investigates the passive beamforming and information transfer (PBIT) technique for multiuser multiple-input multiple-output (Mu-MIMO) systems with the aid of reconfigurable intelligent surfaces (RISs), where the RISs enhance the primary communication via passive beamforming (P-BF) and at the same time deliver additional information by the on-off reflecting modulation (in which the RIS information is carried by the on/off state of each reflecting element). For the P-BF design, we propose to maximize the achievable user sum rate of the RIS-aided Mu-MIMO channel and formulate the problem as a two-step stochastic program. A sample average approximation (SAA) based iterative algorithm is developed for the efficient P-BF design of the considered scheme. To strike a balance between complexity and performance, we further propose a simplified P-BF algorithm by approximating the stochastic program as a deterministic alternating optimization problem. For the receiver design, the signal detection at the receiver is a bilinear estimation problem since the RIS information is multiplicatively modulated onto the reflected signals of the reflecting elements. To solve this bilinear estimation problem, we develop a turbo message passing (TMP) algorithm in which the factor graph associated with the problem is divided into two modules: one for the estimation of the user signals and the other for the estimation of the on-off state of each RIS element. The two modules are executed iteratively to yield a near-optimal low-complexity solution. Furthermore, we extend the design of the Mu-MIMO PBIT scheme from single-RIS to multi-RIS, by leveraging the similarity between the single-RIS and multi-RIS system models. Extensive simulation results are provided to demonstrate the advantages of our P-BF and receiver designs. [ABSTRACT FROM AUTHOR]

In this paper, we study joint antenna activity detection, channel estimation, and multiuser detection for massive multiple-input multiple-output (MIMO) systems with general spatial modulation (GSM). We first establish a double-sparsity massive MIMO model by considering the channel sparsity of the massive MIMO channel and the signal sparsity of GSM. Based on the double-sparsity model, we formulate a blind detection problem. To solve the blind detection problem, we develop message-passing based blind channel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basically follows the affine sparse matrix factorization technique, but with critical modifications to handle the double-sparsity property of the model. We show that the BCSE algorithm significantly outperforms the existing blind and training-based algorithms, and is able to closely approach the genie bounds (with either known channel or known signal). In the BCSE algorithm, short pilots are employed to remove the phase and permutation ambiguities after sparse matrix factorization. To utilize the short pilots more efficiently, we further develop the semi-blind channel-and-signal estimation (SBCSE) algorithm to incorporate the estimation of the phase and permutation ambiguities into the iterative message-passing process. We show that the SBCSE algorithm substantially outperforms the counterpart algorithms including the BCSE algorithm in the short-pilot regime. [ABSTRACT FROM AUTHOR]

In this paper, we study time-varying orthogonal frequency division multiplexing (OFDM) systems, and propose a joint channel-and-signal estimation receiver based on turbo message passing (TMP) to efficiently suppress inter-carrier interference (ICI). We establish a factor graph representation of the problem and divide the whole factor graph into two parts, one for channel estimation and the other for signal detection. For the first part, we use Gaussian message passing (GMP) for channel estimation; for the second part, a discrete state space (DSS) model is employed to describe the transition of signal states, and a forward-backward algorithm is adopted for message passing over the transition trellis in signal detection. The resulting algorithm is referred to as DSS-GMP. The complexity of DSS-GMP quickly becomes the bottleneck as the increase of the signal constellation size and the ICI width. To address this issue, we further develop a continuous-state-space (CSS) model based turbo message passing algorithm, where the messages of modulated signals are approximated as continuous Gaussian messages. Numerical results demonstrate that the TMP based scheme significantly outperforms the state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Compressed sensing has been employed to reduce the pilot overhead for channel estimation in wireless communication systems. Particularly, structured turbo compressed sensing (STCS) provides a generic framework for structured sparse signal recovery with reduced computational complexity and storage requirement. In this paper, we consider the problem of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation in a frequency division duplexing (FDD) downlink system. By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing-based channel estimators under the STCS framework. Several STCS-based algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting the structured sparsity. We show that, compared with other existing algorithms, the proposed algorithms have a much faster convergence speed and achieve competitive error performance under a wide range of simulation settings. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel receiver design for orthogonal frequency division multiplexing (OFDM) transmission over time-varying channels, where the receiver employs a turbo message passing (TMP) framework for a joint channel estimation and the data detection. We show that the proposed TMP-based receiver is able to make more efficient use of the prior information about the channel and the data for intercarrier interference cancellation than the existing iterative receivers. The proposed TMP algorithm can be further combined with a soft-input soft-output decoder for the joint channel estimation, data detection, and decoding. Numerical results are presented to demonstrate the performance superiority of our proposed TMP-based OFDM receiver. [ABSTRACT FROM AUTHOR]

Mitigation of impulsive noise has been extensively studied in wireline, wireless radio, and powerline communication systems. However, its study in underwater acoustic (UWA) systems is quite limited. This paper considers impulsive noise mitigation for underwater orthogonal frequency-division multiplexing (OFDM) systems, where the system performance is severely impacted by the channel Doppler effect. We propose a practical approach based on a least squares formulation: First, the positions of impulsive noise are determined in the time domain based on the signal amplitude, and second, impulsive noise samples are jointly estimated with the Doppler shift based on the measurements of the OFDM null subcarriers. Based on the available channel estimate and tentative data symbol decisions, an iterative receiver is further developed. Data sets have been acquired in a recent sea experiment near Kaohsiung city, Taiwan, in May 2013. Performance results based on extensive simulations and collected data sets demonstrate that the proposed receivers effectively mitigate impulsive noise for UWA OFDM systems. [ABSTRACT FROM PUBLISHER]